Publications

Manuscripts

Q. H. Ho, T. Becker, B. Kraske, Z. Laouar, M. Feather, F. Rossi, M. Lahijanian, and Z. N. Sunberg, “Recursively-Constrained Partially Observable Markov Decision Processes,” 2023. (Submitted)
[Abstract] [BibTeX]

Abstract: In many problems, it is desirable to optimize an objective function while imposing constraints on some other aspect of the problem. A Constrained Partially Observable Markov Decision Process (C-POMDP) allows modelling of such problems while subject to transition uncertainty and partial observability. Typically, the constraints in C-POMDPs enforce a threshold on expected cumulative costs starting from an initial state distribution. In this work, we first show that optimal C-POMDP policies may violate Bellman’s principle of optimality and thus may exhibit pathological behaviors, which can be undesirable for many applications. To address this drawback, we introduce a new formulation, the Recursively-Constrained POMDP (RC-POMDP), that imposes additional history dependent cost constraints on the C-POMDP. We show that, unlike C-POMDPs, RC-POMDPs always have deterministic optimal policies, and that optimal policies obey Bellman’s principle of optimality. We also present a point-based dynamic programming algorithm that synthesizes optimal policies for RC-POMDPs. In our evaluations, we show that policies for RC-POMDPs produce more desirable behavior than policies for C-POMDPs and demonstrate the efficacy of our algorithm across a set of benchmark problems.
BibTex:
```
@unpublished{HoBeckerEtAl2023,
  author = {Ho, Qi Heng and Becker, Tyler and Kraske, Ben and Laouar, Zakariya and Feather, Martin and Rossi, Federico and Lahijanian, Morteza and Sunberg, Zachary N.},
  title = {Recursively-Constrained Partially Observable Markov Decision Processes},
  year = {2023},
  keywords = {POMDPs, CU, JPL, sub},
  note = {Submitted},
  nourl = {https://arxiv.org/abs/TODO}
}
```
Keywords: POMDPs, CU, JPL, sub
D. Stanley, R. Woollands, A. Rahmani, F. Rossi, and C. Choi, “Enabling Space-Based Computed Cloud Tomography with a Mixed Integer Linear Programming Scheduler,” 2023. (Submitted)
[Abstract] [BibTeX]

Abstract: Cumuliform clouds in Earth’s atmosphere scatter outgoing longwave radiation which interacts with aerosols to generate new clouds. These new clouds scatter additional outgoing radiation creating a feedback loop. Uncertainties in the strength of this feedback is the primary source of uncertainty in transient and equilibrium climate model predictions. Conventional remote cloud observation methods are inadequate for inferring the internal structures of these clouds. Cloud tomography operates by imaging a single cloud target from multiple locations with a large angular range. Many low convective clouds only have a lifetime of 15-25 minutes necessitating autonomous scheduling of observation targets as they appear. On-board autonomous scheduling is formulated as a mixed integer optimization problem (MILP) with a finite time horizon and a reward scheme designed to maximize the angular range of the observations. A finite time horizon MILP scheduler is well suited to this mission because the short lifetime of low convective clouds creates a natural time horizon. This MILP can solve for an optimal observation schedule in a maximum time of 15 ms on a conventional desktop CPU. The MILP scheduler is able to observe 59.4% more targets than a conventional push broom camera configuration. This initial result is promising and demonstrates the need for continued research efforts in this area.
BibTex:
```
@unpublished{StanleyWoollandsEa2023,
  author = {Stanley, David and Woollands, Robyn and Rahmani, Amir and Rossi, Federico and Choi, Changrak},
  title = {Enabling Space-Based Computed Cloud Tomography with a Mixed Integer Linear Programming Scheduler},
  year = {2023},
  keywords = {spacecraft, MILP, UIUC, JPL, sub},
  note = {Submitted},
  nourl = {https://arxiv.org/abs/TODO}
}
```
Keywords: spacecraft, MILP, UIUC, JPL, sub
F. Rossi, S. Bandyopadhyay, M. T. Wolf, and M. Pavone, “Multi-Agent Algorithms for Collective Behavior - A structural and application-focused atlas,” 2021. (Submitted)
[Abstract] [BibTeX]

Abstract: The goal of this paper is to provide a survey and application-focused atlas of collective behavior coordination algorithms for multi-agent system, classified according to their underlying mathematical structure. We survey the general family of collective behavior algorithms for multi-agent systems and classify them according to their underlying mathematical structure. In doing so, we aim to capture fundamental mathematical properties of algorithms (e.g., scalability with respect to the number of agents and bandwidth use) and to show how the same algorithm or family of algorithms can be used for multiple tasks and applications. Collectively, this paper provides an application-focused atlas of algorithms for collective behavior algorithms, with three objectives: 1. to act as a tutorial guide to practitioners in the selection of coordination algorithms for a given application; 2. to highlight how mathematically similar algorithms can be used for a variety of tasks, ranging from low-level control to high-level coordination; 3. to explore the state-of-the-art in the field of control of multi-agent systems and identify areas for future research.
BibTex:
```
@unpublished{RossiBandyopadhyayEtAl2021,
  author = {Rossi, Federico and Bandyopadhyay, Saptarshi and Wolf, Michael T. and Pavone, Marco},
  title = {Multi-Agent Algorithms for Collective Behavior - A structural and application-focused atlas},
  year = {2021},
  keywords = {Distributed systems, Survey, Stanford, JPL, sub},
  note = {Submitted},
  url = {https://arxiv.org/abs/2103.11067}
}
```
Keywords: Distributed systems, Survey, Stanford, JPL, sub

Journal Articles

F. Rossi, M. Saboia, S. Krishnamoorthy, and J. Vander Hook, “Proximal Exploration of Venus Volcanism with Teams of Autonomous Buoyancy-Controlled Balloons,” Acta Astronautica, vol. 208, pp. 389–406, 2023.
[Abstract] [BibTeX] [DOI]

Abstract: Altitude-controlled balloons hold great promise for performing high-priority scientific investigations of Venus’s atmosphere and geological phenomena, including tectonic and volcanic activity, as demonstrated by a number of recent Earth-based experiments. In this paper, we explore a concept of operations where multiple autonomous, altitude-controlled balloons monitor explosive volcanic activity on Venus through infrasound microbarometers, and autonomously navigate the uncertain wind field to perform follow-on observations of detected events of interest. We propose a novel autonomous guidance technique for altitude-controlled balloons in Venus’s uncertain wind field, and show the approach can result in an increase of up to 63% in the number of close-up observations of volcanic events compared to passive drifters, and a 16% increase compared to ground-in-the-loop guidance. The results are robust to uncertainty in the wind field, and hold across large changes in the frequency of \revIexplosive volcanic events, sensitivity of the microbarometer detectors, and numbers of aerial platforms.
BibTex:
```
@article{RossiSaboiaEtAl2023,
  author = {Rossi, Federico and Saboia, Ma\'ira and Krishnamoorthy, Siddharth and Vander Hook, Joshua},
  title = {Proximal Exploration of Venus Volcanism with Teams of Autonomous Buoyancy-Controlled Balloons},
  year = {2023},
  keywords = {Distributed systems, Venus, aerobot, JPL},
  url = {https://arxiv.org/abs/2303.02104},
  journal = {{Acta Astronautica}},
  volume = {208},
  pages = {389-406},
  doi = {10.1016/j.actaastro.2023.03.003}
}
```
Keywords: Distributed systems, Venus, aerobot, JPL
J. Vander Hook, F. Rossi, T. Stegun Vaquero, M. Troesch, M. Sanchez-Net, J. Schoolcraft, J.-P. de la Croix, and S. Chien, “Multirobot Onsite Shared Analytics Information and Computing,” IEEE Transactions on Control of Network Systems, vol. 10, no. 1, pp. 169–181, Mar. 2022.
[Abstract] [BibTeX] [DOI] [Code ]

Abstract: Computation load-sharing across a network of heterogeneous robots is a promising approach to increase robots capabilities and efficiency as a team in extreme environments. However, in such environments, communication links may be intermittent and connections to the cloud or internet may be nonexistent. In this paper we introduce a communication-aware, data-driven computation task scheduling problem for multi-robot systems and propose an integer linear program (ILP) that optimizes the allocation of computational tasks across a network of heterogeneous robots, accounting for the networked robots’ computational capabilities and for available (and possibly time-varying) communication links. We consider scheduling of a set of inter-dependent required and optional (but rewarding) tasks modelled by a dependency graph. We present a consensus-backed scheduling architecture for shared-world, distributed systems. We validate the ILP formulation and the distributed implementation in different computation platforms and in simulated scenarios with a bias towards lunar or planetary exploration scenarios. Our results show that the proposed implementation can increase the amount of rewarding tasks (e.g., science measurements) performed threefold compared to an analogous system with no computational load-sharing.
BibTex:
```
@article{Hook2021,
  author = {Vander Hook, Joshua and Rossi, Federico and Stegun Vaquero, Tiago and Troesch, Martina and Sanchez-Net, Marc and Schoolcraft, Joshua and de la Croix, Jean-Pierre and Chien, Steve},
  title = {Multirobot Onsite Shared Analytics Information and Computing},
  code = {https://github.com/nasa/mosaic},
  year = {2022},
  keywords = {Distributed systems, JPL, MOSAIC},
  url = {https://arxiv.org/abs/2112.06879},
  journal = {{IEEE Transactions on Control of Network Systems}},
  volume = {10},
  number = {1},
  pages = {169--181},
  doi = {10.1109/TCNS.2022.3198789},
  month = mar
}
```
Keywords: Distributed systems, JPL, MOSAIC
F. Rossi, S. Bandyopadhyay, M. Mote, J.-P. de la Croix, and A. Rahmani, “Communication-Aware Orbit Design for Small Spacecraft Swarms around Small Bodies,” AIAA Journal of Guidance, Control, and Dynamics, vol. 45, no. 11, pp. 2046–2060, 2022.
[Abstract] [BibTeX] [DOI] [Code ]

Abstract: Exploration of small Solar System bodies has traditionally been performed by single monolithic spacecraft carrying a number of science instruments. However, science instruments typically cannot be operated simultaneously due to the instrument requirements including optimal viewing angle, surface illumination, altitude and ground resolution, power, and data constraints. This observation has motivated interest in multi-spacecraft architectures where a swarm of small spacecraft, each carrying a single science instrument, studies a small body after being deployed by a carrier spacecraft, which then collects data from the vehicles and relays it to Earth. Such architectures hold promise to yield significant improvements in mission efficiency, increases in data quality, and shorter mission duration. A key difficulty in the design of such missions is the selection of orbits for the small spacecraft, which must satisfy not only instrument requirements, but also strict inter-spacecraft communication and on-board storage constraints. To address this, in this paper, we present a novel computationally-efficient optimization algorithm for communication-aware design of the orbits of a small spacecraft swarm orbiting a small body. The proposed approach captures constraints including instrument requirements, inter-spacecraft communication bandwidths, and on-board memory usage, and it can accommodate highly irregular gravity field models and surface geometries. We propose an efficient algorithm for optimization of instrument observations and inter-spacecraft communications; we then leverage the differentiable nature of the proposed algorithm to accelerate a gradient-based global search algorithm. Numerical simulations of a six-spacecraft swarm studying 433 Eros show that the proposed approach successfully identifies high-quality orbits, and significantly outperform communication-agnostic optimization techniques, resulting in a 10% increase in scientific returns and a 30% increase in the quality of the collected data.
BibTex:
```
@article{RossiBandyopadhyayMoteDeLaCroixRahmani22,
  author = {Rossi, Federico and Bandyopadhyay, Saptarshi and Mote, Mark and de la Croix, Jean-Pierre and Rahmani, Amir},
  title = {Communication-Aware Orbit Design for Small Spacecraft Swarms around Small Bodies},
  url = {https://www.federico.io/pdf/Rossi.Bandyopadhyay.Mote.delaCroix.Rahmani.JGCD22.pdf},
  keywords = {ICC, orbit design, JPL},
  doi = {https://doi.org/10.2514/1.G006515},
  code = {https://github.com/nasa/icc},
  year = {2022},
  journal = {{AIAA Journal of Guidance, Control, and Dynamics}},
  volume = {45},
  number = {11},
  pages = {2046--2060}
}
```
Keywords: ICC, orbit design, JPL
R. Woollands, F. Rossi, T. Stegun Vaquero, M. Sanchez Net, S. Bae, V. Bickel, and J. Vander Hook, “Maximizing Dust Devil Follow-Up Observations on Mars Using CubeSats and On-Board Scheduling,” Journal of the Astronautical Sciences, vol. 69, pp. 918–940, Jun. 2022.
[Abstract] [BibTeX] [DOI]

Abstract: Several million dust devil events occur on Mars every day. These events last, on average, about 30 minutes and range in size from meters to hundreds of meters in diameter. Designing low-cost missions that will improve our knowledge of dust devil formation and evolution, and their connection to atmospheric dynamics and the dust cycle, is fundamental to informing future crewed Mars lander missions about surface conditions. In this paper we present a mission for a constellation of low orbiting Mars cubesats, each carrying imagers with agile pointing capabilities. The goal is to maximize the number of dust devil follow-up observations through real-time, on-board scheduling. We study scenarios where cubesats are equipped with a 2.5 degree boresight angle camera that accommodates five slew positions (including nadir). We assume a concept of operations where the cubesats autonomously survey the surface of Mars and can autonomously detect dust devils from their surface imagery. When a dust devil is detected, the constellation is autonomously re-tasked through an on-board distributed scheduler to capture as many follow-on images of the event as possible, so as to study its evolution. The cubesat orbits are propagated assuming two-body dynamics and the ground tracks and camera field of view are computed assuming a spherical Mars. Realistic inter-agent communication link opportunities are computed and included in our optimization, which allow for real-time event detection information to be shared within the constellation. We compare against a powerful “omniscient” oracle which has a priori knowledge of all dust devil activity to show the gap between predicted performance and the best possible outcome. In particular, we show that the communications are especially important for acquiring follow-up observations, and that a realistic distributed scheduling mechanism is is able to capture a large fraction of all dust devil observations that are possible for a given orbit configuration, significantly outperforming a nadir-pointing heuristic.
BibTex:
```
@article{WoollandsRossiVaqueroSanchezNetBaeBickelHook21JASS,
  author = {Woollands, Robyn and Rossi, Federico and Stegun Vaquero, Tiago and Sanchez Net, Marc and Bae, Sandra and Bickel, Valentin and Vander Hook, Joshua},
  title = {Maximizing Dust Devil Follow-Up Observations on Mars Using CubeSats and On-Board Scheduling},
  year = {2022},
  keywords = {MOSAIC, distributed computing, JPL},
  url = {https://www.federico.io/pdf/Woollands.Rossi.Vaquero.SanchezNet.Bae.Bickel.Hook.JAS21.pdf},
  journal = {{ Journal of the Astronautical Sciences}},
  volume = {69},
  pages = {918--940},
  doi = {10.1007/s40295-022-00317-z},
  month = jun
}
```
Keywords: MOSAIC, distributed computing, JPL
A. Estandia, M. Schiffer, F. Rossi, E. C. Kara, R. Rajagopal, and M. Pavone, “On the Interaction between Autonomous Mobility on Demand Systems and Power Distribution Networks - An Optimal Power Flow Approach,” IEEE Transactions on Control of Network Systems, vol. 8, no. 3, pp. 1163–1176, Sep. 2021.
[Abstract] [BibTeX] [DOI] [Code ]

Abstract: In future transportation systems, the charging behavior of electric Autonomous Mobility on Demand (AMoD) fleets, i.e., fleets of self-driving cars that service on-demand trip requests, will likely challenge power distribution networks (PDNs), causing overloads or voltage drops. In this paper, we show that these challenges can be significantly attenuated if the PDNs’ operational constraints and exogenous loads (e.g., from homes or businesses) are considered when operating the electric AMoD fleet. We focus on a system-level perspective, assuming full cooperation between the AMoD and the PDN operators. Through this single entity perspective, we derive an upper bound on the benefits of coordination. We present an optimization-based modeling approach to jointly control an electric AMoD fleet and a series of PDNs, and analyze the benefit of coordination under load balancing constraints. For a case study in Orange County, CA, we show that coordinating the electric AMoD fleet and the PDNs helps to reduce 99% of overloads and 50% of voltage drops which the electric AMoD fleet causes without coordination. Our results show that coordinating electric AMoD and PDNs helps to level loads and can significantly postpone the point at which upgrading the network’s capacity to a larger scale becomes inevitable to preserve stability.
BibTex:
```
@article{EstandiaSchifferEtAl2019,
  author = {Estandia, Alvaro and Schiffer, Maximilian and Rossi, Federico and Kara, Emre C and Rajagopal, Ram and Pavone, Marco},
  title = {On the Interaction between Autonomous Mobility on Demand Systems and Power Distribution Networks - An Optimal Power Flow Approach},
  journal = {{IEEE Transactions on Control of Network Systems}},
  year = {2021},
  volume = {8},
  number = {3},
  pages = {1163--1176},
  code = {https://github.com/StanfordASL/unbalanced-opf-toolkit},
  keywords = {AMoD, Stanford},
  doi = {10.1109/TCNS.2021.3059225},
  url = {https://arxiv.org/abs/1905.00200},
  month = sep
}
```
Keywords: AMoD, Stanford
R. A. Brown, F. Rossi, K. Solovey, M. Tsao, M. T. Wolf, and M. Pavone, “On Local Computation for Network-Structured Convex Optimization in Multi-Agent Systems,” IEEE Transactions on Control of Network Systems, vol. 8, no. 2, pp. 542–554, Jun. 2021.
[Abstract] [BibTeX] [DOI]

Abstract: A number of prototypical optimization problems in multi-agent systems (e.g., task allocation and network load-sharing) exhibit a highly local structure: that is, each agent’s decision variables are only directly coupled to few other agent’s variables through the objective function or the constraints. In this paper, we develop a rigorous notion of "locality" that quantifies the degree to which agents can compute their portion of the global solution based solely on information in their local neighborhood. We build upon the results of Rebeschini and Tatikonda (2019) to develop a more general theory of locality that fully captures the importance of problem data to individual solution components, as opposed to a theory that only captures response to perturbations. This analysis provides a theoretical basis for a rather simple algorithm in which agents individually solve a truncated sub-problem of the global problem, where the size of the sub-problem used depends on the locality of the problem, and the desired accuracy. Numerical results show that the proposed theoretical bounds are remarkably tight for well-conditioned problems.
BibTex:
```
@article{BrownRossiEtAl20b,
  author = {Brown, Robin A. and Rossi, Federico and Solovey, Kiril and Tsao, Matt and Wolf, Michael T. and Pavone, Marco},
  title = {On Local Computation for Network-Structured Convex Optimization in Multi-Agent Systems},
  journal = {{IEEE Transactions on Control of Network Systems}},
  year = {2021},
  month = jun,
  volume = {8},
  number = {2},
  pages = {542--554},
  keywords = {JPL, Stanford, Distributed systems, Distributed computing},
  url = {https://www.federico.io/pdf/Brown.Rossi.ea.TCNS20v2.pdf},
  doi = {10.1109/TCNS.2021.3050129}
}
```
Keywords: JPL, Stanford, Distributed systems, Distributed computing
M. Salazar, N. Lanzetti, F. Rossi, M. Schiffer, and M. Pavone, “Intermodal Autonomous Mobility-on-Demand,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 9, pp. 3946–3960, Sep. 2020.
[Abstract] [BibTeX] [DOI]

Abstract: In this paper we study models and coordination policies for intermodal Autonomous Mobility-on-Demand (AMoD), wherein a fleet of self-driving vehicles provides on-demand mobility jointly with public transit. Specifically, we first present a network flow model for intermodal AMoD, where we capture the coupling between AMoD and public transit and the goal is to maximize social welfare. Second, leveraging such a model, we design a pricing and tolling scheme that allows to achieve the social optimum under the assumption of a perfect market with selfish agents. Finally, we present a real-world case study for New York City. Our results show that the coordination between AMoD fleets and public transit can yield significant benefits compared to an AMoD system operating in isolation.
BibTex:
```
@article{SalazarEtAl2019,
  author = {Salazar, Mauro and Lanzetti, Nicolas and Rossi, Federico and Schiffer, Maximilian and Pavone, Marco},
  title = {Intermodal Autonomous Mobility-on-Demand},
  journal = {{IEEE Transactions on Intelligent Transportation Systems}},
  year = {2020},
  volume = {21},
  number = {9},
  pages = {3946--3960},
  doi = {10.1109/TITS.2019.2950720},
  keywords = {AMoD, Stanford},
  owner = {frossi2},
  timestamp = {2018-06-15},
  url = {https://www.federico.io/pdf/Salazar.ea.T-ITS19.pdf},
  month = sep
}
```
Keywords: AMoD, Stanford
F. Rossi, R. Iglesias, M. Alizadeh, and M. Pavone, “On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms,” IEEE Transactions on Control of Network Systems, vol. 7, no. 1, pp. 384–397, Mar. 2020.
[Abstract] [BibTeX] [DOI] [Code ]

Abstract: We study the interaction between a fleet of electric, self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a joint linear model that captures the coupling between the two systems stemming from the vehicles’ charging requirements. The model subsumes existing network flow models for AMoD systems and linear models for the power network, and it captures time-varying customer demand and power generation costs, road congestion, and power transmission and distribution constraints. We then leverage the linear model to jointly optimize the operation of both systems. We propose an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by state-of-the-art linear programming solvers. Finally, we study the implementation of a hypothetical electric-powered AMoD system in Dallas-Fort Worth, and its impact on the Texas power network. We show that coordination between the AMoD system and the power network can reduce the overall energy expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of $78M/year compared to an uncoordinated scenario. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the electric power network, and shed additional light on the economic and societal value of AMoD.
BibTex:
```
@article{RossiIglesiasEtAl2018b,
  author = {Rossi, Federico and Iglesias, Ramon and Alizadeh, Mahnoosh and Pavone, Marco},
  title = {On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms},
  journal = {{IEEE Transactions on Control of Network Systems}},
  year = {2020},
  volume = {7},
  number = {1},
  pages = {384--397},
  code = {https://github.com/StanfordASL/pamod},
  doi = {10.1109/TCNS.2019.2923384},
  keywords = {AMoD, Stanford},
  owner = {frossi2},
  timestamp = {2018-10-17},
  url = {https://arxiv.org/abs/1709.04906},
  month = mar
}
```
Keywords: AMoD, Stanford
R. Iglesias, F. Rossi, R. Zhang, and M. Pavone, “A BCMP Network Approach to Modeling and Controlling Autonomous Mobility-on-Demand Systems,” Int. Journal of Robotics Research, vol. 38, no. 2-3, pp. 357–374, Mar. 2019.
[Abstract] [BibTeX] [DOI]

Abstract: In this paper we present a queuing network approach to the problem of routing and rebalancing a fleet of self-driving vehicles providing on- demand mobility within a capacitated road network. We refer to such systems as autonomous mobility-on-demand systems, or AMoD. We first cast an AMoD system into a closed, multi-class BCMP queuing network model capable of capturing the passenger arrival process, traffic, the state- of-charge of electric vehicles, and the availability of vehicles at the stations. Second, we propose a scalable method for the synthesis of routing and charging policies, with performance guarantees in the limit of large fleet sizes. Third, we validate the theoretical results on a case study of New York City. Collectively, this paper provides a unifying framework for the analysis and control of AMoD systems, which provides a large set of modeling options (e.g., the inclusion of road capacities and charging constraints), and subsumes earlier Jackson and network flow models.
BibTex:
```
@article{IglesiasRossiEtAl2017,
  author = {Iglesias, R. and Rossi, F. and Zhang, R. and Pavone, M.},
  title = {A {BCMP} Network Approach to Modeling and Controlling Autonomous Mobility-on-Demand Systems},
  journal = {{Int.\ Journal of Robotics Research}},
  year = {2019},
  volume = {38},
  number = {2-3},
  pages = {357--374},
  doi = {10.1177/0278364918780335},
  keywords = {AMoD, Stanford},
  url = {https://www.federico.io/pdf/Iglesias.Rossi.Zhang.Pavone.IJRR18.pdf},
  month = mar
}
```
Keywords: AMoD, Stanford
R. Zhang, F. Rossi, and M. Pavone, “Analysis, Control, and Evaluation of Mobility-on-Demand Systems: a Queueing-Theoretical Approach,” IEEE Transactions on Control of Network Systems, vol. 6, no. 1, pp. 115–126, Mar. 2019.
[Abstract] [BibTeX] [DOI]

Abstract: This paper presents a queueing-theoretical approach to the analysis, control, and evaluation of mobility-on-demand (MoD) systems for urban personal transportation. A MoD system consists of a fleet of vehicles providing one-way car sharing service and a team of drivers to rebalance such vehicles. The drivers then rebalance themselves by driving select customers similar to a taxi service. We model the MoD system as two coupled closed Jackson networks with passenger loss. We show that the system can be approximately balanced by solving two decoupled linear programs and exactly balanced through nonlinear optimization. The rebalancing techniques are applied to a system sizing example using taxi data in three neighborhoods of Manhattan. Lastly, we formulate a real-time closed-loop rebalancing policy for drivers and perform case studies of two hypothetical MoD systems in Manhattan and Hangzhou, China. We show that the taxi demand in Manhattan can be met with the same number of vehicles in a MoD system, but only require 1/3 to 1/4 the number of drivers; in Hangzhou, where customer demand is highly unbalanced, higher driver-to-vehicle ratios are required to achieve good quality of service.
BibTex:
```
@article{ZhangPavone2018,
  author = {Zhang, Rick and Rossi, Federico and Pavone, Marco},
  title = {Analysis, Control, and Evaluation of Mobility-on-Demand Systems: a Queueing-Theoretical Approach},
  journal = {{IEEE Transactions on Control of Network Systems}},
  year = {2019},
  volume = {6},
  number = {1},
  pages = {115--126},
  doi = {10.1109/TCNS.2018.2800403},
  keywords = {AMoD, Stanford},
  owner = {frossi2},
  timestamp = {2018-05-10},
  url = {https://www.federico.io/pdf/Zhang.Rossi.Pavone.TCNS18.pdf},
  month = mar
}
```
Keywords: AMoD, Stanford
F. Rossi, R. Zhang, Y. Hindy, and M. Pavone, “Routing Autonomous Vehicles in Congested Transportation Networks: Structural Properties and Coordination Algorithms,” Autonomous Robots, vol. 42, no. 7, pp. 1427–1442, May 2018.
[Abstract] [BibTeX] [DOI] [Code ]

Abstract: This paper considers the problem of routing and rebalancing a shared fleet of autonomous (i.e., self-driving) vehicles providing on-demand mobility within a capacitated transportation network, where congestion might disrupt throughput. We model the problem within a network flow framework and show that under relatively mild assumptions the rebalancing vehicles, if properly coordinated, do not lead to an increase in congestion (in stark contrast to common belief). From an algorithmic standpoint, such theoretical insight suggests that the problem of routing customers and rebalancing vehicles can be decoupled, which leads to a computationally-efficient routing and rebalancing algorithm for the autonomous vehicles. Numerical experiments and case studies corroborate our theoretical insights and show that the proposed algorithm outperforms state-of-the-art point-to-point methods by avoiding excess congestion on the road. Collectively, this paper provides a rigorous approach to the problem of congestion-aware, system-wide coordination of autonomously driving vehicles, and to the characterization of the sustainability of such robotic systems.
BibTex:
```
@article{RossiZhangEtAl2017,
  author = {Rossi, F. and Zhang, R. and Hindy, Y. and Pavone, M.},
  title = {Routing Autonomous Vehicles in Congested Transportation Networks: Structural Properties and Coordination Algorithms},
  journal = {{Autonomous Robots}},
  year = {2018},
  volume = {42},
  number = {7},
  pages = {1427--1442},
  code = {https://github.com/StanfordASL/matsim-amod},
  doi = {10.1007/s10514-018-9750-5},
  keywords = {AMoD, Stanford},
  url = {https://www.federico.io/pdf/Rossi.Zhang.Hindy.Pavone.AURO17.pdf},
  month = may
}
```
Keywords: AMoD, Stanford

Conference Articles

P. Zhong, F. Rossi, and D. Shell, “Optimizing pre-scheduled, intermittently-observed MDPs,” in Proc. IEEE Conf. on Decision and Control, Singapore, 2023. (In Press)
[Abstract] [BibTeX]

Abstract: A challenging category of robotics problems arises when sensing incurs substantial costs. This paper examines settings in which a robot wishes to limit its observations of state, for instance, motivated by specific considerations of energy management, stealth, or implicit coordination. We formulate the problem of planning under uncertainty when the robot’s observations are intermittent but their timing is known via a pre-declared schedule. After having established the appropriate notion of an optimal policy for such settings, we tackle the problem of joint optimization of the cumulative execution cost and the number of state observations, both in expectation under discounts. To approach this multi-objective optimization problem, we introduce an algorithm that can identify the Pareto front for a class of schedules that are advantageous in the discounted setting. The algorithm proceeds in an accumulative fashion, prepending additions to a working set of schedules and then computing incremental changes to the value functions. Because full exhaustive construction becomes computationally prohibitive for moderate-sized problems, we propose a filtering approach to prune the working set. Empirical results demonstrate that this filtering is effective at reducing computation while incurring only negligible reduction in quality. In summarizing our findings, we provide some characterization of the run-time vs quality trade-off involved.
BibTex:
```
@inproceedings{ZhongRossiEa2023,
  author = {Zhong, Patrick and Rossi, Federico and Shell, Dylan},
  title = {Optimizing pre-scheduled, intermittently-observed MDPs},
  booktitle = {{Proc.\ IEEE Conf.\ on Decision and Control}},
  year = {2023},
  month = dec,
  address = {Singapore},
  keywords = {autonomy, POMDP, MDP, JPL, TAMU, press},
  url = {https://arxiv.org/pdf/2305.09105}
}
```
Keywords: autonomy, POMDP, MDP, JPL, TAMU, press
F. Rossi, T. Stegun Vaquero, M. Jorritsma, et al., “Workflows, user interfaces, and algorithms for operations of autonomous spacecraft,” in IEEE Aerospace Conference, Big Sky, MT, 2023.
[Abstract] [BibTeX] [DOI]

Abstract: Autonomous planning and scheduling is a key enabling technology for future robotic Solar System explorers: as missions venture farther in the Solar System, light-speed delays and low available bandwidth make on-board autonomy increasingly attractive to maximize science returns and enable otherwise-infeasible observations of transient phenomena, e.g. storms on gas giants and plumes on icy worlds. However, ground operations of future autonomous explorers will require a paradigm shift, moving from the current practice of specifying timed sequences of commands, to specifying high-level goals that on-board autonomy should elaborate based on the spacecraft’s state and the sensed environment. In this paper, we explore the problem of adapting ground operations processes, roles, and tools to accommodate on-board planning and scheduling. We design and prototype a framework of user interfaces and algorithmic tools to support uplink and downlink processes of future autonomous spacecraft. The framework’s goals are to allow scientists and engineers to both convey their desired intent to the spacecraft in a format compatible with the on-board planner, and reconstruct and explain the decisions made on-board and their impact on the state of the spacecraft. We assess the performance of the framework through a design simulation where JPL scientists and operators simulate realistic operations of an Ice Giant multi-flyby mission concept, aided by the proposed framework. The design simulation confirms that the proposed approach holds promise to enable operators to interact with on-board autonomy, and suggest a number of recommendations for the next generation of operations tools supporting autonomous spacecraft.
BibTex:
```
@inproceedings{RossiVaqueroea2023,
  author = {Rossi, Federico and Stegun Vaquero, Tiago and Jorritsma, Marijke and Van Wyk, Ellen and Huffmann, Bennett and Allard, Dan and Dhamani, Nihal and Davidoff, Scott and Jasour, Ashkan and Barrett, Anthony and Amini, Rashied and Choukroun, Mathieu and Francis, Raymond and Hofstadter, Mark and Ingham, Michel and Verma, Vandi and Castano, Rebecca},
  title = {Workflows, user interfaces, and algorithms for operations of autonomous spacecraft},
  year = {2023},
  keywords = {autonomy, UX, spacecraft, operations, JPL},
  note = {In Press},
  booktitle = {{IEEE Aerospace Conference}},
  address = {Big Sky, MT},
  month = mar,
  url = {https://www.federico.io/pdf/Rossi.Vaquero.ea.AERO23.pdf},
  doi = {10.1109/AERO55745.2023.10115605}
}
```
Keywords: autonomy, UX, spacecraft, operations, JPL
F. Rossi and D. A. Shell, “Planning under periodic observations: bounds and bounding-based solutions,” in IEEE/RSJ Int. Conf. on Intelligent Robots & Systems, Kyoto, Japan, 2022, pp. 11751–11758.
[Abstract] [BibTeX] [DOI] [Code ]

Abstract: We study planning problems faced by robots operating in uncertain environments with incomplete knowledge of state, and actions that are noisy and/or imprecise. This paper identifies a new problem sub-class that models settings in which information is revealed only intermittently through some exogenous process that provides state information periodically. Several practical domains fit this model, including the specific scenario that motivates our research: autonomous navigation of a planetary exploration rover augmented by remote imaging. With an eye to efficient specialized solution methods, we examine the structure of instances of this sub-class. They lead to Markov Decision Processes with exponentially large action-spaces but for which, as those actions comprise sequences of more atomic elements, one may establish performance bounds by comparing policies under different information assumptions. For instance, policies with actions that have a prefix form entail waiting for information and they give lower bounds. Or instead, one might imagine extra information becoming available so that the robot obtains its state estimate early; these yield upper bounds. Moving beyond the pattern apparent in these basic intuitions, we also describe a way to construct bounds more systematically. Bounds are useful because, in conjunction with the insights they confer, they can be employed in bounding-based methods to obtain high-quality solutions efficiently; the empirical results we present demonstrate their effectiveness for the considered problems. The foregoing has also alluded to the distinctive role that time plays for these problems (more precisely: the specific time until information is revealed). We uncover several interesting subtleties: knowing when information arrives has value, distinct from the value of information itself. Also, obtaining information earlier is not always better, even under exponential discounting.
BibTex:
```
@inproceedings{RossiShell22,
  author = {Rossi, Federico and Shell, Dylan A.},
  title = {Planning under periodic observations: bounds and bounding-based solutions},
  url = {https://www.federico.io/pdf/Rossi.Shell.IROS22.pdf},
  keywords = {JPL, TAMU, periodic observations},
  code = {https://github.com/dylanshell/periodicplans},
  year = {2022},
  booktitle = {{IEEE/RSJ Int.\ Conf.\ on Intelligent Robots \& Systems}},
  address = {Kyoto, Japan},
  month = oct,
  pages = {11751-11758},
  doi = {10.1109/IROS47612.2022.9982056}
}
```
Keywords: JPL, TAMU, periodic observations
R. Castano, T. Stegun Vaquero, F. Rossi, et al., “Operations for Autonomous Spacecraft,” in IEEE Aerospace Conference, Big Sky, MT, 2022.
[Abstract] [BibTeX] [DOI]

Abstract: Onboard autonomy technologies such as planning and scheduling, identification of scientific targets, and content-based data summarization, will lead to exciting new space science missions. However, the challenge of operating missions with such onboard autonomous capabilities has not been studied to a level of detail sufficient for consideration in mission concepts. These autonomy capabilities will require changes to current operations processes, practices, and tools. We have developed a case study to assess the changes needed to enable operators and scientists to operate an autonomous spacecraft by facilitating a common model between the ground personnel and the onboard algorithms. We assess the new operations tools and workflows necessary to enable operators and scientists to convey their desired intent to the spacecraft, and to be able to reconstruct and explain the decisions made onboard and the state of the spacecraft. Mock-ups of these tools were used in a user study to understand the effectiveness of the processes and tools in enabling a shared framework of understanding, and in the ability of the operators and scientists to effectively achieve mission science objectives.
BibTex:
```
@inproceedings{CastanoVaqueroRossiEtAl2021,
  author = {Castano, Rebecca and Stegun Vaquero, Tiago and Rossi, Federico and Verma, Vandi and Van Wyk, Ellen and Allard, Dan and Huffmann, Bennett and Murphy, Erin M. and Dhamani, Nihal and Hewitt, Robert A. and Davidoff, Scott and Amini, Rashied and Barrett, Anthony and Castillo-Rogez, Julie and Choukroun, Mathieu and Dadaian, Alain and Francis, Raymond and Gorr, Benjamin and Hofstadter, Mark and Ingham, Mitch and Sorice, Cristina and Tierney, Iain},
  title = {Operations for Autonomous Spacecraft},
  year = {2022},
  keywords = {autonomy, UX, spacecraft, operations, JPL},
  url = {https://arxiv.org/abs/2111.10970},
  booktitle = {{IEEE Aerospace Conference}},
  address = {Big Sky, MT},
  month = mar,
  doi = {10.1109/AERO53065.2022.9843352}
}
```
Keywords: autonomy, UX, spacecraft, operations, JPL
K. Solovey, S. Bandyopadhyay, F. Rossi, M. T. Wolf, and M. Pavone, “Fast Near-Optimal Heterogeneous Task Allocation via Flow Decomposition,” in Proc. IEEE Conf. on Robotics and Automation, Xi’an, China, 2021.
[Abstract] [BibTeX] [DOI] [Code ]

Abstract: Multi-robot systems are uniquely well-suited to performing complex tasks such as patrolling and tracking, information gathering, and pick-up and delivery problems, offering significantly higher performance than single-robot systems. A fundamental building block in most multi-robot systems is task allocation: assigning robots to tasks (e.g., patrolling an area, or servicing a transportation request) as they appear based on the robots’ states to maximize reward. In many practical situations, the allocation must account for heterogeneous capabilities (e.g., availability of appropriate sensors or actuators) to ensure the feasibility of execution, and to promote a higher reward, over a long time horizon. To this end, we present the FlowDec algorithm for efficient heterogeneous task-allocation achieving an approximation factor of at least 1/2 of the optimal reward. Our approach decomposes the heterogeneous problem into several homogeneous subproblems that can be solved efficiently using min-cost flow. Through simulation experiments, we show that our algorithm is faster by several orders of magnitude than a MILP approach.
BibTex:
```
@inproceedings{SoloveyBandyopadhyayEtAl2021,
  author = {Solovey, Kiril and Bandyopadhyay, Saptarshi and Rossi, Federico and Wolf, Michael T. and Pavone, Marco},
  title = {Fast Near-Optimal Heterogeneous Task Allocation via Flow Decomposition},
  year = {2021},
  booktitle = {{Proc.\ IEEE Conf.\ on Robotics and Automation}},
  month = may,
  address = {Xi'an, China},
  keywords = {JPL, Stanford, Distributed systems},
  url = {https://arxiv.org/abs/2011.03603},
  code = {https://github.com/kirilsol/task-allocation},
  doi = {10.1109/ICRA48506.2021.9560880}
}
```
Keywords: JPL, Stanford, Distributed systems
E. B. Clark, A. Branch, R. Castano, et al., “IceNode: a Buoyant Vehicle for Acquiring Well-Distributed, Long-Duration Melt Rate Measurements under Ice Shelves,” in OCEANS, San Diego, CA, USA, 2021, pp. 1–10.
[Abstract] [BibTeX] [DOI]

Abstract: Antarctic ice shelves buttress the Antarctic Ice Sheet from sliding into the ocean and significantly raising global sea level. However, the accelerating dynamics of ice shelf melt in a warming environment are poorly understood, and the collapse of Antarctic ice shelves remains one of the largest sources of uncertainty in global sea level rise projections. The cavities below Antarctic ice shelves are notoriously difficult to access, making model-based hypotheses about the relationship between ocean warming and greater ice shelf melting difficult to verify because of a lack of in-situ data to constrain model parameters and examine key assumptions. We present early progress on IceNode, a novel vehicle under development at the NASA Jet Propulsion Laboratory designed to acquire well-distributed, concurrent, long-duration melt rate measurements under ice shelves. IceNodes are deployed as an array from a ship at the shelf edge, and use variable buoyancy to ride melt-driven exchange currents far into the cavity. Once underneath their target landing area, they release a ballast weight to gain high positive buoyancy and attach to the underside of the ice shelf, where they acquire in-situ measurements of basal melt rate directly at the ice-ocean interface for a year or more. Finally, IceNodes detach from their landing structure and use variable buoyancy to ride melt-driven exchange currents back to open water, where they surface and transmit their mission data home. IceNodes are designed to be relatively low-cost, expendable, and have simple logistics, enabling scientists to deploy scalable arrays that acquire simultaneous, distributed measurements of co-varying ice shelf melt and ocean conditions over large spatial areas, thereby providing an unprecedented view of ice shelf melt rate variability and its drivers.
BibTex:
```
@inproceedings{ClarkBranchEA21,
  author = {Clark, Evan Bock and Branch, Andrew and Castano, Rebecca and Fenty, Ian and Gebara, Christine and Kourchians, Ara and Limonadi, Daniel and Madhok, Gauri and McGarey, Patrick and Mechentel, Flora and Nguyen, Kelly and Okamoto, Tyler and Rignot, Eric and Rossi, Federico and Santos, Brendan and Schachter, Justin and Schodlok, Michael and Schoelen, Dane and Stanton, Timothy and Hook, Joshua Vander and Wolsieffer, Ben and Zapien, Xavier},
  booktitle = {{OCEANS}},
  address = {San Diego, CA, USA},
  title = {IceNode: a Buoyant Vehicle for Acquiring Well-Distributed, Long-Duration Melt Rate Measurements under Ice Shelves},
  year = {2021},
  volume = {},
  number = {},
  pages = {1--10},
  doi = {10.23919/OCEANS44145.2021.9705733},
  url = {https://www.federico.io/pdf/Clark.Branch.Castano.ea.OCEANS21.pdf}
}
```
Keywords:
F. Rossi, A. Branch, M. P. Schodlok, T. Stanton, I. G. Fenty, J. Vander Hook, and E. B. Clark, “Stochastic Guidance of Buoyancy Controlled Vehicles under Ice Shelves using Ocean Currents,” in IEEE/RSJ Int. Conf. on Intelligent Robots & Systems, Prague, Czech Republic, 2021.
[Abstract] [BibTeX] [DOI]

Abstract: We propose a novel technique for guidance of buoyancy-controlled vehicles in uncertain under-ice ocean flows. In-situ melt rate measurements collected at the grounding zone of Antarctic ice shelves, where the ice shelf meets the underlying bedrock, are essential to constrain models of future sea level rise. Buoyancy-controlled vehicles, which control their vertical position in the water column through internal actuation but have no means of horizontal propulsion, offer an affordable and reliable platform for such in-situ data collection. However, reaching the grounding zone requires vehicles to traverse tens of kilometers under the ice shelf, with approximate position knowledge and no means of communication, in highly variable and uncertain ocean currents. To address this challenge, we propose a partially observable MDP approach that exploits model-based knowledge of the under-ice currents and, critically, of their uncertainty, to synthesize effective guidance policies. The approach uses approximate dynamic programming to model uncertainty in the currents, and QMDP to address localization uncertainty. Numerical experiments show that the policy can deliver up to 88.8% of underwater vehicles to the grounding zone - a 33% improvement compared to state-of-the-art guidance techniques, and a 262% improvement over uncontrolled drifters. Collectively, these results show that model-based under-ice guidance is a highly promising technique for exploration of under-ice cavities, and has the potential to enable cost-effective and scalable access to these challenging and rarely observed environments.
BibTex:
```
@inproceedings{RossiBranchEtAl2021,
  author = {Rossi, Federico and Branch, Andrew and Schodlok, Michael P. and Stanton, Timothy and Fenty, Ian G. and Vander Hook, Joshua and Clark, Evan B.},
  title = {Stochastic Guidance of Buoyancy Controlled Vehicles under Ice Shelves using Ocean Currents},
  keywords = {Distributed systems, JPL, IceNode},
  booktitle = {{IEEE/RSJ Int.\ Conf.\ on Intelligent Robots \& Systems}},
  year = {2021},
  address = {Prague, Czech Republic},
  month = oct,
  url = {https://www.federico.io/pdf/Rossi.Branch.Schodlok.Stanton.Fenty.VanderHook.Clark.IceNode21.pdf},
  doi = {10.1109/IROS51168.2021.9635987}
}
```
Keywords: Distributed systems, JPL, IceNode
F. Rossi *, T. Stegun Vaquero *, M. Sanchez Net, M. Saboia, and J. Vander Hook, “The Pluggable Distributed Resource Allocator (PDRA): a Middleware for Distributed Computing in Mobile Robotic Networks,” in IEEE/RSJ Int. Conf. on Intelligent Robots & Systems, Las Vegas, NV, 2020, pp. 4337–4344.
[Abstract] [BibTeX] [DOI] [Code ]

Abstract: We present the Pluggable Distributed Resource Allocator (PDRA), a middleware for distributed computing in heterogeneous mobile robotic networks. PDRA enables autonomous agents to share computational resources for computationally expensive tasks such as localization and path planning. It sits between an existing single-agent planner/executor and existing computational resources (e.g. ROS packages), intercepts the executor’s requests and, if needed, transparently routes them to other nodes for execution. PDRA is pluggable: it can be integrated in an existing single-robot autonomy stack with minimal modifications. Task allocation decisions are performed by a mixed-integer programming algorithm, solved in a shared-world fashion, that models CPU resources, network bandwidth, and latency requirements, and minimizes overall energy usage or maximizes reward for completing optional tasks. Simulation results show that PDRA can reduce energy and CPU usage by over 50% in representative multi-robot scenarios compared to a naive scheduler; runs on embedded platforms; and performs well in delay- and disruption-tolerant networks (DTNs). PDRA is available to the community under an open-source license.
BibTex:
```
@inproceedings{RossiVaqueroEtAl2020,
  author = {Rossi *, Federico and Stegun Vaquero *, Tiago and Sanchez Net, Marc and Saboia, Ma\'ira and Vander Hook, Joshua},
  title = {The Pluggable Distributed Resource Allocator (PDRA): a Middleware for Distributed Computing in Mobile Robotic Networks},
  booktitle = {{IEEE/RSJ Int.\ Conf.\ on Intelligent Robots \& Systems}},
  year = {2020},
  address = {Las Vegas, NV},
  month = oct,
  pages = {4337--4344},
  code = {https://github.com/nasa/mosaic},
  keywords = {JPL, Distributed systems, Distributed computing, MOSAIC},
  url = {http://arxiv.org/abs/2003.13813},
  doi = {10.1109/IROS45743.2020.9341205}
}
```
Keywords: JPL, Distributed systems, Distributed computing, MOSAIC
R. A. Brown, F. Rossi, K. Solovey, M. T. Wolf, and M. Pavone, “Exploiting Locality and Structure for Distributed Optimization in Multi-Agent Systems,” in European Control Conference, St. Petersburg, Russia, 2020.
[Abstract] [BibTeX] [DOI]

Abstract: A number of prototypical optimization problems in multi-agent systems (e.g. task allocation and network load-sharing) exhibit a highly local structure: that is, each agent’s decision variables are only directly coupled to few other agent’s variables through the objective function or the constraints. Nevertheless, existing algorithms for distributed optimization generally do not exploit the locality structure of the problem, requiring all agents to compute or exchange the full set of decision variables. In this paper, we develop a rigorous notion of "locality" that relates the structural properties of a linearly-constrained convex optimization problem (in particular, the sparsity structure of the constraint matrix and the objective function) to the amount of information that agents should exchange to compute an arbitrarily high-quality approximation to the problem from a cold-start. We leverage the notion of locality to develop a locality-aware distributed optimization algorithm, and we show that, for problems where individual agents only require to know a small portion of the optimal solution, the algorithm requires very limited inter-agent communication. Numerical results show that the convergence rate of our algorithm is directly explained by the locality parameter proposed, and that the proposed theoretical bounds are remarkably tight.
BibTex:
```
@inproceedings{BrownRossiEtAl20,
  author = {Brown, R. A. and Rossi, F. and Solovey, K. and Wolf, M. T. and Pavone, M.},
  title = {Exploiting Locality and Structure for Distributed Optimization in Multi-Agent Systems},
  booktitle = {{European Control Conference}},
  year = {2020},
  address = {St. Petersburg, Russia},
  month = may,
  keywords = {Distributed systems, Stanford, JPL},
  url = {https://www.federico.io/pdf/Brown.Rossi.ea.ECC20.pdf},
  doi = {10.23919/ECC51009.2020.9143722}
}
```
Keywords: Distributed systems, Stanford, JPL
S. Bae, F. Rossi, J. Vander Hook, S. Davidoff, and K.-L. Ma, “A Visual Analytics Approach to Debugging Cooperative, Autonomous Multi-Robot Systems’ Worldviews,” in IEEE Conference on Visual Analytics Science and Technology (VAST), Salt Lake City, UT, USA (virtual), 2020.
[Abstract] [BibTeX] [DOI]

Abstract: Autonomous multi-robot systems, where a team of robots shares information to perform tasks that are beyond an individual robot’s abilities, hold great promise for a number of applications, such as planetary exploration missions. Each robot in a multi-robot system that uses the shared-world coordination paradigm autonomously schedules which robot should perform a given task, and when, using its worldview–the robot’s internal representation of its belief about both its own state, and other robots’ states. A key problem for operators is that robots’ worldviews can fall out of sync (often due to weak communication links), leading to desynchronization of the robots’ scheduling decisions and inconsistent emergent behavior (e.g., tasks not performed, or performed by multiple robots). Operators face the time-consuming and difficult task of making sense of the robots’ scheduling decisions, detecting de-synchronizations, and pinpointing the cause by comparing every robot’s worldview. To address these challenges, we introduce MOSAIC Viewer, a visual analytics system that helps operators (i) make sense of the robots’ schedules and (ii) detect and conduct a root cause analysis of the robots’ desynchronized worldviews. Over a year-long partnership with roboticists at the NASA Jet Propulsion Laboratory, we conduct a formative study to identify the necessary system design requirements and a qualitative evaluation with 12 roboticists. We find that MOSAIC Viewer is faster- and easier-to-use than the users’ current approaches, and it allows them to stitch low-level details to formulate a high-level understanding of the robots’ schedules and detect and pinpoint the cause of the desynchronized worldviews.
BibTex:
```
@inproceedings{BaeRossiHookDavidoffMa20,
  author = {Bae, Sandra and Rossi, Federico and Vander Hook, Joshua and Davidoff, Scott and Ma, Kwan-Liu},
  title = {A Visual Analytics Approach to Debugging Cooperative, Autonomous Multi-Robot Systems' Worldviews},
  booktitle = {{IEEE Conference on Visual Analytics Science and Technology (VAST)}},
  year = {2020},
  month = oct,
  address = {Salt Lake City, UT, USA (virtual)},
  keywords = {MOSAIC, visualization, JPL},
  url = {https://arxiv.org/abs/2009.01921},
  doi = {10.1109/VAST50239.2020.00008}
}
```
Keywords: MOSAIC, visualization, JPL
J. Vander Hook, T. Stegun Vaquero, F. Rossi, M. Troesch, M. Sanchez-Net, J. Schoolcraft, J.-P. de la Croix, and S. Chien, “Mars On-Site Shared Analytics Information and Computing,” in International Conference on Automated Planning and Scheduling (ICAPS), Berkeley, CA, 2019, vol. 29, no. 1, pp. 707–715.
[Abstract] [BibTeX] [Code ]

Abstract: We study the use of distributed computation in a representative multi-robot planetary exploration mission. We model a network of small rovers with access to computing resources from a static base station based on current design efforts and extrapolation from the Mars 2020 rover autonomy. The key algorithmic problem is simultaneous scheduling of computation, communication, and caching of data, as informed by an autonomous mission planner. We consider scheduling of a dependency chain of required and optional (but rewarding) tasks and present a consensus-backed scheduler for shared-world, distributed scheduling based on an Integer Linear Program. We validate the pipeline with simulation and field results. Our results are intended to provide a baseline comparison and motivating application domain for future research into network-aware decentralized scheduling and resource allocation.
BibTex:
```
@inproceedings{Hook2019,
  author = {Vander Hook, Joshua and Stegun Vaquero, Tiago and Rossi, Federico and Troesch, Martina and Sanchez-Net, Marc and Schoolcraft, Joshua and de la Croix, Jean-Pierre and Chien, Steve},
  title = {Mars On-Site Shared Analytics Information and Computing},
  booktitle = {{International Conference on Automated Planning and Scheduling (ICAPS)}},
  year = {2019},
  volume = {29},
  number = {1},
  pages = {707--715},
  address = {Berkeley, CA},
  month = jul,
  organization = {AAAI},
  code = {https://github.com/nasa/mosaic},
  keywords = {Distributed systems, JPL, MOSAIC},
  url = {https://www.federico.io/pdf/Hook.Vaquero.Rossi.ea.ICAPS19.pdf}
}
```
Keywords: Distributed systems, JPL, MOSAIC
F. Rossi, R. Iglesias, M. Alizadeh, and M. Pavone, “On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms,” in Robotics: Science and Systems, 2018.
[Abstract] [BibTeX] [DOI] [Code ]

Abstract: We study the interaction between a fleet of electric, self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a joint linear model that captures the coupling between the two systems stemming from the vehicles’ charging requirements. The model subsumes existing network flow models for AMoD systems and linear models for the power network, and it captures time-varying customer demand and power generation costs, road congestion, and power transmission and distribution constraints. We then leverage the linear model to jointly optimize the operation of both systems. We propose an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by state-of-the-art linear programming solvers. Finally, we study the implementation of a hypothetical electric-powered AMoD system in Dallas-Fort Worth, and its impact on the Texas power network. We show that coordination between the AMoD system and the power network can reduce the overall energy expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of $78M/year compared to an uncoordinated scenario. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the electric power network, and shed additional light on the economic and societal value of AMoD.
BibTex:
```
@inproceedings{RossiIglesiasEtAl2018,
  author = {Rossi, F. and Iglesias, R. and Alizadeh, M. and Pavone, M.},
  title = {On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms},
  booktitle = {{Robotics: Science and Systems}},
  year = {2018},
  month = dec,
  aaddress = {Pittsburgh, PA},
  code = {https://github.com/StanfordASL/pamod},
  doi = {10.15607/RSS.2018.XIV.037},
  keywords = {AMoD, Stanford},
  owner = {frossi2},
  timestamp = {2018-04-19},
  url = {https://www.federico.io/pdf/Rossi.Iglesias.Alizadeh.Pavone.RSS18.pdf}
}
```
Keywords: AMoD, Stanford
F. Rossi, S. Bandyopadhyay, M. T. Wolf, and M. Pavone, “Review of Multi-Agent Algorithms for Collective Behavior: a Structural Taxonomy,” in IFAC Aerospace Controls TC Workshop: Networked & Autonomous Air & Space Systems (NAASS 2018), 2018.
[Abstract] [BibTeX] [DOI]

Abstract: In this paper, we review multi-agent collective behavior algorithms in the literature and classify them according to their underlying mathematical structure. For each mathematical technique, we identify the multi-agent coordination tasks it can be applied to, and we analyze its scalability, bandwidth use, and demonstrated maturity. We highlight how versatile techniques such as artificial potential functions can be used for applications ranging from low-level position control to high-level coordination and task allocation, we discuss possible reasons for the slow adoption of complex distributed coordination algorithms in the field, and we highlight areas for further research and development.
BibTex:
```
@inproceedings{RossiBandyopadhyayEtAl2018,
  author = {Rossi, Federico and Bandyopadhyay, Saptarshi and Wolf, Michael T. and Pavone, Marco},
  title = {Review of Multi-Agent Algorithms for Collective Behavior: a Structural Taxonomy},
  booktitle = {{IFAC Aerospace Controls TC Workshop: Networked \& Autonomous Air \& Space Systems (NAASS 2018)}},
  year = {2018},
  month = jun,
  keywords = {Distributed systems, Survey, Stanford, JPL},
  owner = {frossi2},
  timestamp = {2018-02-01},
  url = {https://arxiv.org/abs/1803.05464},
  doi = {10.1016/j.ifacol.2018.07.097}
}
```
Keywords: Distributed systems, Survey, Stanford, JPL
R. Iglesias, F. Rossi, K. Wang, D. Hallac, J. Leskovec, and M. Pavone, “Data-Driven Model Predictive Control of Autonomous Mobility-on-Demand Systems,” in Proc. IEEE Conf. on Robotics and Automation, Brisbane, Australia, 2018.
[Abstract] [BibTeX] [DOI]

Abstract: The goal of this paper is to present an end-to-end, data-driven framework to control Autonomous Mobility-on-Demand systems (AMoD, i.e. fleets of self-driving vehicles). We first model the AMoD system using a time-expanded network, and present a formulation that computes the optimal rebalancing strategy (i.e., preemptive repositioning) and the minimum feasible fleet size for a given travel demand. Then, we adapt this formulation to devise a Model Predictive Control (MPC) algorithm that leverages short-term demand forecasts based on historical data to compute rebalancing strategies. We test the end-to-end performance of this controller with a state-of-the-art LSTM neural network to predict customer demand and real customer data from DiDi Chuxing: we show that this approach scales very well for large systems (indeed, the computational complexity of the MPC algorithm does not depend on the number of customers and of vehicles in the system) and outperforms state-of-the-art rebalancing strategies by reducing the mean customer wait time by up to to 89.6%.
BibTex:
```
@inproceedings{IglesiasRossiEtAl2018,
  author = {Iglesias, Ramon and Rossi, Federico and Wang, Kevin and Hallac, David and Leskovec, Jure and Pavone, Marco},
  title = {Data-Driven Model Predictive Control of Autonomous Mobility-on-Demand Systems},
  booktitle = {{Proc.\ IEEE Conf.\ on Robotics and Automation}},
  year = {2018},
  address = {Brisbane, Australia},
  month = may,
  doi = {10.1109/ICRA.2018.8460966},
  keywords = {AMoD},
  timestamp = {2018-01-14},
  url = {https://www.federico.io/pdf/Iglesias.Rossi.Wang.ea.ICRA18.pdf}
}
```
Keywords: AMoD
M. Salazar, F. Rossi, M. Schiffer, C. H. Onder, and M. Pavone, “On the Interaction between Autonomous Mobility-on-Demand and the Public Transportation Systems,” in Proc. IEEE Int. Conf. on Intelligent Transportation Systems, Maui, Hawaii, 2018.
[Abstract] [BibTeX] [DOI]

Abstract: In this paper we study models and coordination policies for intermodal Autonomous Mobility-on-Demand (AMoD), wherein a fleet of self-driving vehicles provides on-demand mobility jointly with public transit. Specifically, we first present a network flow model for intermodal AMoD, where we capture the coupling between AMoD and public transit and the goal is to maximize social welfare. Second, leveraging such a model, we design a pricing and tolling scheme that allows to achieve the social optimum under the assumption of a perfect market with selfish agents. Finally, we present a real-world case study for New York City. Our results show that the coordination between AMoD fleets and public transit can yield significant benefits compared to an AMoD system operating in isolation.
BibTex:
```
@inproceedings{SalazarRossiEtAl2018,
  author = {Salazar, Mauro and Rossi, Federico and Schiffer, Maximilian and Onder, Christopher H. and Pavone, Marco},
  title = {On the Interaction between Autonomous Mobility-on-Demand and the Public Transportation Systems},
  booktitle = {{Proc.\ IEEE Int.\ Conf.\ on Intelligent Transportation Systems}},
  year = {2018},
  address = {Maui, Hawaii},
  month = nov,
  note = {\textbf{Best Student Paper Award}},
  doi = {10.1109/ITSC.2018.8569381},
  keywords = {AMoD, Stanford},
  timestamp = {2018-06-15},
  url = {https://arxiv.org/abs/1804.11278}
}
```
Keywords: AMoD, Stanford
R. Iglesias, F. Rossi, R. Zhang, and M. Pavone, “A BCMP Network Approach to Modeling and Controlling Autonomous Mobility-on-Demand Systems,” in Workshop on Algorithmic Foundations of Robotics, 2016.
[Abstract] [BibTeX] [DOI]

Abstract: In this paper, we present a queueing network approach to the problem of routing and rebalancing a fleet of self-driving vehicles providing on-demand mobility within a capacitated road network. We refer to such systems as autonomous mobility-on-demand systems, or AMoD. We first cast an AMoD system into a closed, multi-class BCMP queueing network model. Second, we present analysis tools that allow the characterization of performance metrics for a given routing policy, in terms, e.g., of vehicle availabilities and second-order moments of vehicle throughput. Third, we propose a scalable method for the synthesis of routing policies, with performance guarantees in the limit of large fleet sizes. Finally, we validate our theoretical results on a case study of New York City. Collectively, this paper provides a unifying framework for the analysis and control of AMoD systems, which subsumes earlier Jackson and flow network models, provides a quite large set of modeling options (e.g., the inclusion of road capacities and general travel time distributions), and allows the analysis of second and higher-order moments for the performance metrics.
BibTex:
```
@inproceedings{IglesiasRossiEtAl2016,
  author = {Iglesias, R. and Rossi, F. and Zhang, R. and Pavone, M.},
  title = {A {BCMP} Network Approach to Modeling and Controlling {Autonomous} {Mobility-on-Demand} Systems},
  booktitle = {{Workshop on Algorithmic Foundations of Robotics}},
  year = {2016},
  month = dec,
  keywords = {AMoD, Stanford},
  url = {http://arxiv.org/abs/1607.04357},
  doi = {10.1007/978-3-030-43089-4_53}
}
```
Keywords: AMoD, Stanford
R. Zhang, F. Rossi, and M. Pavone, “Routing Autonomous Vehicles in Congested Transportation Networks: Structural Properties and Coordination Algorithms,” in Robotics: Science and Systems, 2016.
[Abstract] [BibTeX] [DOI]

Abstract: This paper considers the problem of routing and rebalancing a shared fleet of autonomous (i.e., self-driving) vehicles providing on-demand mobility within a capacitated transportation network, where congestion might disrupt throughput. We model the problem within a network flow framework and show that under relatively mild assumptions the rebalancing vehicles, if properly coordinated, do not lead to an increase in congestion (in stark contrast to common belief). From an algorithmic standpoint, such theoretical insight suggests that the problem of routing customers and rebalancing vehicles can be decoupled, which leads to a computationally-efficient routing and rebalancing algorithm for the autonomous vehicles. Numerical experiments and case studies corroborate our theoretical insights and show that the proposed algorithm outperforms state-of-the-art point-to-point methods by avoiding excess congestion on the road. Collectively, this paper provides a rigorous approach to the problem of congestion-aware, system-wide coordination of autonomously driving vehicles, and to the characterization of the sustainability of such robotic systems.
BibTex:
```
@inproceedings{ZhangRossiEtAl2016,
  author = {Zhang, R. and Rossi, F. and Pavone, M.},
  title = {Routing Autonomous Vehicles in Congested Transportation Networks: Structural Properties and Coordination Algorithms},
  booktitle = {{Robotics: Science and Systems}},
  year = {2016},
  month = jul,
  doi = {10.15607/rss.2016.xii.032},
  keywords = {AMoD, Stanford},
  timestamp = {2017-01-28},
  url = {http://arxiv.org/abs/1603.00939}
}
```
Keywords: AMoD, Stanford
R. Zhang, F. Rossi, and M. Pavone, “Model Predictive Control of Autonomous Mobility-on-Demand Systems,” in Proc. IEEE Conf. on Robotics and Automation, Stockholm, Sweden, 2016.
[Abstract] [BibTeX] [DOI]

Abstract: In this paper we present a model predictive control (MPC) approach to optimize vehicle scheduling and routing in an autonomous mobility-on-demand (AMoD) system. In AMoD systems, robotic, self-driving vehicles transport customers within an urban environment and are coordinated to optimize service throughout the entire network. Specifically, we first propose a novel discrete-time model of an AMoD system and we show that this formulation allows the easy integration of a number of real-world constraints, e.g., electric vehicle charging constraints. Second, leveraging our model, we design a model predictive control algorithm for the optimal coordination of an AMoD system and prove its stability in the sense of Lyapunov. At each optimization step, the vehicle scheduling and routing problem is solved as a mixed integer linear program (MILP) where the decision variables are binary variables representing whether a vehicle will 1) wait at a station, 2) service a customer, or 3) rebalance to another station. Finally, by using real-world data, we show that the MPC algorithm can be run in real-time for moderately-sized systems and outperforms previous control strategies for AMoD systems.
BibTex:
```
@inproceedings{ZhangRossiEtAl2016b,
  author = {Zhang, R. and Rossi, F. and Pavone, M.},
  title = {Model Predictive Control of {Autonomous} {Mobility-on-Demand} Systems},
  booktitle = {{Proc.\ IEEE Conf.\ on Robotics and Automation}},
  year = {2016},
  address = {Stockholm, Sweden},
  month = may,
  doi = {10.1109/ICRA.2016.7487272},
  keywords = {AMoD, Stanford},
  url = {http://arxiv.org/abs/1509.03985}
}
```
Keywords: AMoD, Stanford
F. Rossi and M. Pavone, “On the Fundamental Limitations of Performance for Distributed Decision-Making in Robotic Networks,” in Proc. IEEE Conf. on Decision and Control, Los Angeles, California, 2014.
[Abstract] [BibTeX] [DOI]

Abstract: This paper studies fundamental limitations of performance for distributed decision-making in robotic networks. The class of decision-making problems we consider encompasses a number of prototypical problems such as average-based consensus as well as distributed optimization, leader election, majority voting, MAX, MIN, and logical formulas. We first propose a formal model for distributed computation on robotic networks that is based on the concept of I/O automata and is inspired by the Computer Science literature on distributed computing clusters. Then, we present a number of bounds on time, message, and byte complexity, which we use to discuss the relative performance of a number of approaches for distributed decision-making. From a methodological standpoint, our work sheds light on the relation between the tools developed by the Computer Science and Controls communities on the topic of distributed algorithms.
BibTex:
```
@inproceedings{RossiPavone2014,
  author = {Rossi, Federico and Pavone, Marco},
  title = {On the Fundamental Limitations of Performance for Distributed Decision-Making in Robotic Networks},
  booktitle = {{Proc.\ IEEE Conf.\ on Decision and Control}},
  year = {2014},
  address = {Los Angeles, California},
  month = dec,
  doi = {10.1109/CDC.2014.7039760},
  keywords = {Distributed systems, Stanford},
  url = {http://arxiv.org/abs/1409.4863}
}
```
Keywords: Distributed systems, Stanford
F. Rossi and M. Pavone, “Decentralized Decision-Making on Robotic Networks with Hybrid Performance Metrics,” in Allerton Conf. on Communications, Control and Computing, 2013.
[Abstract] [BibTeX] [DOI]

Abstract: The past decade has witnessed a rapidly growing interest in decentralized algorithms for collective decision-making in cyber-physical networks. For a large variety of settings, control strategies are now known that either minimize time complexity (i.e., convergence time) or optimize communication complexity (i.e., number and size of exchanged messages). Yet, little attention has beed paid to the problem of studying the inherent trade-off between time and communication complexity. Generally speaking, time-optimal algorithms are fast and robust, but require a large (and sometimes impractical) number of exchanged messages; in contrast, communication optimal algorithms minimize the amount of information routed through the network, but are slow and sensitive to link failures. In this paper we address this gap by focusing on a generalized version of the decentralized consensus problem (that includes voting and mediation) on undirected network topologies and in the presence of "infrequent" link failures. We present and rigorously analyze a tunable, semi-hierarchical algorithm, where the tuning parameter allows a graceful transition from time-optimal to communication-optimal performance (hence, allowing hybrid performance metrics), and determines the algorithm’s robustness, measured as the time required to recover from a failure. An interesting feature of our algorithm is that it leads the decision-making agents to self-organize into a semi-hierarchical structure with variable-size clusters, among which information is flooded. Our results make use of a novel connection between the consensus problem and the theory of gamma synchronizers. Simulation experiments are presented and discussed.
BibTex:
```
@inproceedings{RossiPavone2013,
  author = {Rossi, Federico and Pavone, Marco},
  title = {Decentralized Decision-Making on Robotic Networks with Hybrid Performance Metrics},
  booktitle = {{Allerton Conf.\ on Communications, Control and Computing}},
  year = {2013},
  month = oct,
  doi = {10.1109/Allerton.2013.6736546},
  keywords = {Distributed systems, Stanford},
  url = {https://www.federico.io/pdf/Rossi.Pavone.Allerton13.pdf}
}
```
Keywords: Distributed systems, Stanford

Lightly Refereed and Tech Reports

R. Castano, F. Rossi, T. Stegun Vaquero, et al., “Operating Deep Space Autonomous Spacecraft: Ground Processes and Tools for Operability and Trust,” in Int. Conf. Space Operations (SpaceOps), Dubai, UAE, 2023. (In Press)
[Abstract] [BibTeX]

Abstract: Future deep-space robotic explorers will use advanced onboard autonomy to address high-priority science questions, e.g., observing fast-changing phenomena and adapting to dynamic environmental circumstances. Onboard autonomy technologies such as planning and scheduling, identification of scientific targets, and content-based data summarization will lead to exciting new deep space science missions. However, traditional operations practices, skills, and processes were not designed for spacecraft with such onboard autonomous capabilities. This paper summarizes the results of a two-year investigation conducted at JPL to explore how ground operations processes, practices, and tools will need to adapt to support effective use of onboard autonomy. In particular, we identify areas where current workflows and tools will need to be enhanced to accommodate commanding and analysis onboard planning and scheduling software for deep space exploration. Our focus is on onboard planning and scheduling: we identify the required changes necessary to enable operators and scientists to convey their desired intent to future autonomous spacecraft’s planning and execution systems via goals and priorities rather than sequences of commands, and to be able to reconstruct and explain the decisions made onboard and the state of the spacecraft - providing a practical path to users trusting the autonomy, which is one of the most significant barriers to full adoption. Collectively, these results form key steps toward adoption of onboard spacecraft autonomy, which will enable new, bolder exploration of the outer solar system, small bodies, and the surface of ocean worlds.
BibTex:
```
@inproceedings{CastanoRossiEa2023,
  author = {Castano, Rebecca and Rossi, Federico and Stegun Vaquero, Tiago and Verma, Vandi and Allard, Dan and Amini, Rashied and Barrett, Anthony and Choukroun, Mathieu and Davidoff, Scott and Dhamani, Nihal and Francis, Raymond and Hofstadter, Mark and Huffmann, Bennett and Ingham, Michel and Jasour, Ashkan and Jorritsma, Marijke and Van Wyk, Ellen and Rabideau, Gregg},
  title = {Operating Deep Space Autonomous Spacecraft: Ground Processes and Tools for Operability and Trust},
  booktitle = {Int. Conf. Space Operations (SpaceOps)},
  year = {2023},
  month = mar,
  address = {Dubai, UAE},
  keywords = {autonomy, UX, spacecraft, operations, JPL, light, press},
  review = {light},
  url = {https://www.federico.io/pdf/Castano.Rossi.ea.SpaceOps23.pdf}
}
```
Keywords: autonomy, UX, spacecraft, operations, JPL, light, press

R. Woollands, F. Rossi, T. Stegun Vaquero, M. Sanchez Net, S. Bae, V. Bickel, and J. Vander Hook, “Enabling Space-based Computed Cloud Tomography with a Mixed Integer Linear Programming Scheduler,” in AAS/AIAA Space Flight Mechanics Meeting, 2023.

[Abstract] [BibTeX]

Abstract:

BibTex:

@inproceedings{WoollandsRossiVaqueroSanchezNetBaeBickelHook22,
  author = {Woollands, Robyn and Rossi, Federico and Stegun Vaquero, Tiago and Sanchez Net, Marc and Bae, Sandra and Bickel, Valentin and Vander Hook, Joshua},
  title = {Enabling Space-based Computed Cloud Tomography with a Mixed Integer Linear Programming Scheduler,},
  booktitle = {{AAS/AIAA Space Flight Mechanics Meeting}},
  year = {2023},
  month = jan,
  keywords = {MOSAIC, distributed computing, JPL, light},
  review = {light},
  url = {https://www.federico.io/pdf/Woollands.Rossi.Vaquero.SanchezNet.Bae.Bickel.Hook.AAS_SFM21.pdf}
}

Keywords: MOSAIC, distributed computing, JPL, light

A. Candela, T. Vaquero, B. Huffman, N. Dhamani, F. Rossi, and R. Castano, “Outcome Prediction and Explainability for Mission Operations of Autonomous Spacecraft,” in ICAPS Workshop on Human-Aware Explainable Planning (HAXP), Prague, Czech Republic, 2023. (In Press)
[Abstract] [BibTeX]

Abstract: As planning and autonomy in general become increasingly deployed onboard spacecraft, missions will face a paradigm shift in how ground operations teams command and interact with the spacecraft: moving from specifying timed sequences of commands to high level goals that on-board autonomy will elaborate based on the spacecraft’s state and sensed environment. In this paper we present an ongoing effort to develop an integrated framework for supporting ground operations through modeling science and engineering intent/goals, predicting outcomes, assessing spacecraft state and performance, and maintaining models used for on-board decision-making and ground-based monitoring. Specifically, we describe the specific knowledge engineering aspects that are key in the operations of autonomous spacecraft, and how we propose to addressed the challenges posed by operations of on-board autonomy.
BibTex:
```
@inproceedings{CandelaVaqueroEa23,
  title = {Outcome Prediction and Explainability for Mission Operations of Autonomous Spacecraft},
  author = {Candela, Alberto and Vaquero, Tiago and Huffman, Bennett and Dhamani, Nihal and Rossi, Federico and Castano, Rebecca},
  booktitle = {{ICAPS Workshop on Human-Aware Explainable Planning (HAXP)}},
  year = {2023},
  month = jul,
  address = {Prague, Czech Republic},
  keywords = {autonomy, UX, spacecraft, operations, JPL, light, press},
  review = {light},
  url = {https://www.federico.io/pdf/Candela.Vaquero.Huffman.Dhamani.Rossi.Castano.HAXP23}
}
```
Keywords: autonomy, UX, spacecraft, operations, JPL, light, press
T. Stegun Vaquero, F. Rossi, R. Castano, A. Jasour, E. van Wyk, N. Dhamani, B. Huffman, and M. Jorritsma, “A Knowledge Engineering Framework for Mission Operations of Increasingly Autonomous Spacecraft,” in ICAPS Workshop on Knowledge Engineering for Planning and Scheduling (KEPS), Singapore, 2022.
[Abstract] [BibTeX]

Abstract: As planning and autonomy in general become increasingly deployed onboard spacecraft, missions will face a paradigm shift in how ground operations teams command and interact with the spacecraft: moving from specifying timed sequences of commands to high level goals that on-board autonomy will elaborate based on the spacecraft’s state and sensed environment. In this paper we present an ongoing effort to develop an integrated framework for supporting ground operations through modeling science and engineering intent/goals, predicting outcomes, assessing spacecraft state and performance, and maintaining models used for on-board decision-making and ground-based monitoring. Specifically, we describe the specific knowledge engineering aspects that are key in the operations of autonomous spacecraft, and how we propose to addressed the challenges posed by operations of on-board autonomy.
BibTex:
```
@inproceedings{VaqueroRossiCastanoJasourVanWykDhamaniBarrettHuffmannJorritsma22,
  author = {Stegun Vaquero, Tiago and Rossi, Federico and Castano, Rebecca and Jasour, Ashkan and van Wyk, Ellen and Dhamani, Nihal and Huffman, Bennett and Jorritsma, Marijke},
  title = {A Knowledge Engineering Framework for Mission Operations of Increasingly Autonomous Spacecraft},
  booktitle = {{ICAPS Workshop on Knowledge Engineering for Planning and Scheduling (KEPS)}},
  year = {2022},
  month = jun,
  address = {Singapore},
  keywords = {autonomy, UX, spacecraft, operations, JPL, light},
  review = {light},
  url = {https://www.federico.io/pdf/Vaquero.Rossi.ea.KEPS22.pdf}
}
```
Keywords: autonomy, UX, spacecraft, operations, JPL, light
R. Woollands, F. Rossi, T. Stegun Vaquero, M. Sanchez Net, S. Bae, V. Bickel, and J. Vander Hook, “Maximizing Dust Devil Follow-Up Observations on Mars Using CubeSats and On-Board Scheduling,” in AAS/AIAA Space Flight Mechanics Meeting, 2021.
[Abstract] [BibTeX]

Abstract: Several million dust devil events occur on Mars every day. These events last, on average, about 30 minutes and range in size from meters to hundreds of meters in diameter. Designing low-cost missions that will improve our knowledge of dust devil formation and evolution, and their connection to atmospheric dynamics and the dust cycle, is fundamental to informing future crewed Mars lander missions about surface conditions. In this paper we present a mission for a constellation of low orbiting Mars cubesats, each carrying imagers with agile pointing capabilities. The goal is to maximize the number of dust devil follow-up observations through real-time, on-board scheduling. We study scenarios where cubesats are equipped with a 2.5 degree boresight angle camera that accommodates five slew positions (including nadir). We assume a concept of operations where the cubesats autonomously survey the surface of Mars and can autonomously detect dust devils from their surface imagery. When a dust devil is detected, the constellation is autonomously re-tasked through an on-board distributed scheduler to capture as many follow-on images of the event as possible, so as to study its evolution. The cubesat orbits are propagated assuming two-body dynamics and the ground tracks and camera field of view are computed assuming a spherical Mars. Realistic inter-agent communication link opportunities are computed and included in our optimization, which allow for real-time event detection information to be shared within the constellation. We compare against a powerful “omniscient” oracle which has a priori knowledge of all dust devil activity to show the gap between predicted performance and the best possible outcome. In particular, we show that the communications are especially important for acquiring follow-up observations, and that a realistic distributed scheduling mechanism is is able to capture a large fraction of all dust devil observations that are possible for a given orbit configuration, significantly outperforming a nadir-pointing heuristic.
BibTex:
```
@inproceedings{WoollandsRossiVaqueroSanchezNetBaeBickelHook21,
  author = {Woollands, Robyn and Rossi, Federico and Stegun Vaquero, Tiago and Sanchez Net, Marc and Bae, Sandra and Bickel, Valentin and Vander Hook, Joshua},
  title = {Maximizing Dust Devil Follow-Up Observations on Mars Using CubeSats and On-Board Scheduling},
  booktitle = {{AAS/AIAA Space Flight Mechanics Meeting}},
  year = {2021},
  month = jan,
  keywords = {MOSAIC, distributed computing, JPL, light},
  review = {light},
  url = {https://www.federico.io/pdf/Woollands.Rossi.Vaquero.SanchezNet.Bae.Bickel.Hook.AAS_SFM21.pdf}
}
```
Keywords: MOSAIC, distributed computing, JPL, light
F. Rossi, S. Bandyopadhyay, M. Mote, J.-P. de la Croix, and A. Rahmani, “Communication-Aware Orbit Design for Small Spacecraft Swarms around Small Bodies,” in AIAA/AAS Astrodynamics Specialist Conference, 2020.
[Abstract] [BibTeX]

Abstract: Exploration of small Solar System bodies has traditionally been performed by single monolithic spacecraft carrying a number of science instruments. However, science instruments typically cannot be operated simultaneously due to the instrument requirements including optimal viewing angle, surface illumination, altitude and ground resolution, power, and data constraints. This observation has motivated interest in multi-spacecraft architectures where a swarm of small spacecraft, each carrying a single science instrument, studies a small body after being deployed by a carrier spacecraft, which then collects data from the vehicles and relays it to Earth. Such architectures hold promise to yield significant improvements in mission efficiency, increases in data quality, and shorter mission duration. A key difficulty in the design of such missions is the selection of orbits for the small spacecraft, which must satisfy not only instrument requirements, but also strict inter-spacecraft communication and on-board storage constraints. To address this, in this paper, we present a novel computationally-efficient optimization algorithm for communication-aware design of the orbits of a small spacecraft swarm orbiting a small body. The proposed approach captures constraints including instrument requirements, inter-spacecraft communication bandwidths, and on-board memory usage, and it can accommodate highly irregular gravity field models and surface geometries. We propose an efficient algorithm for optimization of instrument observations and inter-spacecraft communications; we then leverage the differentiable nature of the proposed algorithm to accelerate a gradient-based global search algorithm. Numerical simulations of a six-spacecraft swarm studying 433 Eros show that the proposed approach successfully identifies high-quality orbits, and significantly outperform communication-agnostic optimization techniques, resulting in a 10% increase in scientific returns and a 30% increase in the quality of the collected data.
BibTex:
```
@inproceedings{RossiBandyopadhyayMoteDeLaCroixRahmani20,
  author = {Rossi, Federico and Bandyopadhyay, Saptarshi and Mote, Mark and de la Croix, Jean-Pierre and Rahmani, Amir},
  title = {Communication-Aware Orbit Design for Small Spacecraft Swarms around Small Bodies},
  booktitle = {{AIAA/AAS Astrodynamics Specialist Conference}},
  year = {2020},
  month = aug,
  keywords = {ICC, orbit design, JPL, light},
  review = {light},
  url = {https://www.federico.io/pdf/Rossi.Bandyopadhyay.Mote.delaCroix.Rahmani.AAS20.pdf}
}
```
Keywords: ICC, orbit design, JPL, light
A. Rahmani, S. Bandyopadhyay, F. Rossi, J.-P. de la Croix, J. Vander Hook, and M. T. Wolf, “Space Vehicle Swarm Exploration Missions: A Study of Key Enabling Technologies and Gaps,” in Int. Astronautical Congress, Washington D.C., U.S.A., 2019.
[Abstract] [BibTeX]

Abstract: Multi-agent robot teams and spacecraft swarms will play an important role in future space missions. In this paper, we propose a comprehensive taxonomy of proposed applications of systems in space, planetary-surface, and terrestrial domains. We identify the key enabling technologies that will enable such applications and identify the technology gaps that have to be overcome. We envisage that the broader community will strive to address these technology challenges to make multi-agent and swarm based space exploration missions a reality.
BibTex:
```
@inproceedings{Rahmani2019,
  author = {Rahmani, Amir and Bandyopadhyay, Saptarshi and Rossi, Federico and de la Croix, Jean-Pierre and Vander Hook, Joshua and Wolf, Michael T.},
  title = {Space Vehicle Swarm Exploration Missions: A Study of Key Enabling Technologies and Gaps},
  booktitle = {{Int.\ Astronautical Congress}},
  year = {2019},
  address = {Washington D.C., U.S.A.},
  month = nov,
  keywords = {Survey, JPL, light},
  review = {light},
  url = {https://www.federico.io/pdf/Rahmani.Bandyopadhyay.Rossi.ea.IAC19.pdf}
}
```
Keywords: Survey, JPL, light
F. Rossi and M. Pavone, “Distributed Consensus with Mixed Time/Communication Bandwidth Performance Metrics,” in Allerton Conf. on Communications, Control and Computing, 2014.
[Abstract] [BibTeX] [DOI]

Abstract: In this paper we study the inherent trade-off between time and communication complexity for the distributed consensus problem. In our model, communication complexity is measured as the maximum data throughput (in bits per second) sent through the network at a given instant. Such a notion of communication complexity, referred to as bandwidth complexity, is related to the frequency bandwidth a designer should collectively allocate to the agents if they were to communicate via a wireless channel, which represents an important constraint for dense robotic networks. We prove a lower bound on the bandwidth complexity of the consensus problem and provide a consensus algorithm that is bandwidth-optimal for a wide class of consensus functions. We then propose a distributed algorithm that can trade communication complexity versus time complexity as a function of a tunable parameter, which can be adjusted by a system designer as a function of the properties of the wireless communication channel. We rigorously characterize the tunable algorithm’s worst-case bandwidth complexity and show that it compares favorably with the bandwidth complexity of well-known consensus algorithm.
BibTex:
```
@inproceedings{RossiPavone2014b,
  author = {Rossi, Federico and Pavone, Marco},
  title = {Distributed Consensus with Mixed Time/Communication Bandwidth Performance Metrics},
  booktitle = {{Allerton Conf.\ on Communications, Control and Computing}},
  year = {2014},
  month = oct,
  doi = {10.1109/ALLERTON.2014.7028468},
  keywords = {Distributed systems, light, Stanford},
  review = {light},
  url = {http://arxiv.org/abs/1410.0956}
}
```
Keywords: Distributed systems, light, Stanford

Theses

F. Rossi, “On the Interaction between Autonomous Mobility-on-Demand Systems and the Built Environment: Models and Large Scale Coordination Algorithms,” PhD thesis, Stanford University, Dept. of Aeronautics and Astronautics, Stanford, California, 2018.
[Abstract] [BibTeX] [Permalink]

Abstract: Autonomous Mobility-on-Demand systems (that is, fleets of self-driving cars offering on-demand transportation) hold promise to reshape urban transportation by offering high quality of service at lower cost compared to private vehicles. However, the impact of such systems on the infrastructure of our cities (and in particular on traffic congestion and the electric power network) is an active area of research. In particular, Autonomous Mobility-on-Demand (AMoD) systems could greatly increase traffic congestion due to additional "rebalancing" trips required to re-align the distribution of available vehicles with customer demand; furthermore, charging of large fleets of electric vehicles can induce significantly stress in the electric power network, leading to high electricity prices and potential network instability. In this thesis, we build analytical tools and algorithms to model and control the interaction between AMoD systems and our cities. We open our work by exploring the interaction between AMoD systems and urban congestion. Leveraging the theory of network flows, we devise models for AMoD systems that capture endogenous traffic congestion and are amenable to efficient optimization. These models allow us to show the key theoretical result that, under mild assumptions that are substantially verified for U.S. cities, AMoD systems do not increase congestion compared to privately-owned vehicles for a given level of customer demand if empty-traveling vehicles are properly routed. We leverage this insight to design a real-time congestion-aware routing algorithm for empty vehicles; microscopic agent-based simulations with New York City taxi data show that the algorithm significantly reduces congestion compared to a state-of-the-art congestion-agnostic rebalancing algorithm, resulting in 22% lower wait times for AMoD customers. We then devise a randomized congestion-aware routing algorithm for customer-carrying vehicles and prove rigorous analytical bounds on its performance. Preliminary results based on New York City taxi data show that the algorithm could yield a further reduction in congestion and, as a result, 5% lower service times for AMoD customers. We then turn our attention to the interaction between AMoD fleets with electric vehicles and the power network. We extend the network flow model developed in the first part of the thesis to capture the vehicles’ state-of-charge and their interaction with the power network (including charging and the ability to inject power in the network in exchange for a payment, denoted as "vehicle-to-grid"). We devise an algorithmic procedure to losslessly reduce the size of the resulting model, making it amenable to efficient optimization, and test our models and optimization algorithms on a hypothetical deployment of an AMoD system in Dallas-Fort Worth, TX with the goal of maximizing social welfare. Simulation results show that coordination between the AMoD system and the power network can reduce electricity prices by over 180M/year, with savings of 120M/year for local power network customers and $35M/year for the AMoD operator. In order to realize such benefits, the transportation operator must cooperate with the power network: we prove that a pricing scheme can be used to enforce the socially optimal solution as a general equilibrium, aligning the interests of a self-interested transportation operator and self-interested power generators with the goal of maximizing social welfare. We then design privacy-preserving algorithms to compute such coordination-promoting prices in a distributed fashion. Finally, we propose a receding-horizon implementation that trades off optimality for speed and demonstrate that it can be deployed in real-time with microscopic simulations in Dallas-Fort Worth. Collectively, these results lay the foundations for congestion-aware and power-aware control of AMoD systems; in particular, the models and algorithms in this thesis provide tools that will enable transportation network operators and urban planners to foster the positive externalities of AMoD and avoid the negative ones, thus fully realizing the benefits of AMoD systems in our cities.
BibTex:
```
@phdthesis{Rossi2018,
  author = {Rossi, Federico},
  title = {On the Interaction between {Autonomous Mobility-on-Demand} Systems and the Built Environment: Models and Large Scale Coordination Algorithms},
  school = {Stanford University, Dept.\ of Aeronautics and Astronautics},
  year = {2018},
  address = {Stanford, California},
  month = mar,
  permalink = {https://purl.stanford.edu/fk416tj3277},
  keywords = {AMoD, Stanford},
  url = {https://www.federico.io/dissertation/}
}
```
Keywords: AMoD, Stanford
F. Rossi, “Decision making on robotic networks with hybrid performance metrics for planetary exploration applications,” Master's thesis, Politecnico di Milano, Dipartimento di Scienze e Tecnologie Aerospaziali, 2013.
[Abstract] [BibTeX] [DOI]

Abstract: This thesis is about distributed consensus on robotic networks for planetary exploration. The advantages of distributed architectures for space exploration have long been studied; furthermore, multiagent architectures are extremely advantageous on small solar system bodies, whose low gravity and uncertain dynamic environment make traditional mobility paradigms unapplicable. Relativistic delays make autonomy paramount for all probes operating beyond Earth orbit. Yet no energy-efficient procedures for autonomous consensus on robotic networks exist: current algorithms are either optimized for ground-based applications or largely inefficient. The purpose of this thesis is to design efficient algorithms to reach an agreement between cooperative stationary or slow-moving robotic agents. We explore metrics describing time performance, power consumption and robustness; we propose time-optimal and energy-optimal algorithms and show how optimality with respect to one parameter typically leads to very bad performance with respect to other metrics. We then design a novel hybrid algorithm that scales from time-optimal to message-optimal behavior, trading time performance and robustness for energy efficiency, according to an user-defined tuning parameter. Worst- case performance of the algorithm is investigated analytically; real-world performance on a simplified space exploration scenario is explored through numerical simulations with satisfactory results. Future research directions will include extension of our work to fast-moving robotic networks such as swarms of planetary hoppers, optimization with respect to legacy omnidirectional (broadcast) communication protocols and application to problems such as UAV deployment for patrolling and ATC conflict resolution.
BibTex:
```
@mastersthesis{FR:13,
  author = {Rossi, Federico},
  title = {Decision making on robotic networks with hybrid performance metrics for planetary exploration applications},
  school = {Politecnico di Milano, Dipartimento di Scienze e Tecnologie Aerospaziali},
  year = {2013},
  doi = {10589/81446},
  keywords = {Distributed systems},
  pages = {132},
  url = {https://www.federico.io/pdf/Rossi.MSc.pdf}
}
```
Keywords: Distributed systems

Research

Autonomous Mobility on Demand

Decentralized decision-making in robotic networks

GalapagosUAV: aerial patrolling for conservation

Publications

Manuscripts

Journal Articles

Conference Articles

Lightly Refereed and Tech Reports

Theses

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Green Card: EB-2 NIW

Working in the U.S. aerospace industry as a foreign national

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