Federico in front of Foucault's pendulum at the Pantheon, Paris. Image credit: Valentina Ricchiuti

I am a Postdoctoral Scholar with the Maritime and Multi-Agent Autonomy Group (347N) within the Robotics section of NASA’s Jet Propulsion Laboratory.

My current research focuses on optimal control and decision-making in multi-agent robotic systems, with applications to (i) swarms of unmanned aerial vehicles and surface vessels for patrolling and exploration and (ii) coordination of fleets of self-driving cars for autonomous mobility-on-demand in urban environments

I earned a Ph.D. in Aeronautics and Astronautics at Stanford University under the guidance of Prof. Marco Pavone, director of the Autonomous Systems Laboratory. I received a M.Sc. in Space Engineering from Politecnico di Milano, a M.Sc. in Aerospace Engineering from Politecnico di Torino and the Diploma from the Alta Scuola Politecnica in 2013. In 2011-2012, I spent seven months at SUPAERO as an exchange student (Admis Sur Titre). Prior to that, I received my B.Sc. in Aerospace Engineering from Politecnico di Milano in 2010.

You can find my resumé here.

When not in the lab, I enjoy astronomy, photography and reading.

Research

Autonomous Mobility on Demand

A congestion-aware rebalancing algorithm delivers lower customer waiting times and less congestion than a legacy algorithm. From RSS '16

Self-driving cars can greatly increase safety of our roads and enhance mobility for those unable or unwilling to drive. Today, private vehicles spend more than 90% of their lives idle, parked either at home or at work. With self-driving cars, this could change drastically: shared autonomous vehicles, part of an Autonomous Mobility-on-Demand (AMoD) system, could operate like taxis, driving from a passenger’s destination to the next passenger’s departure location. Fewer vehicles and, with no need to park, more room for homes and businesses!

But how should the passengers be assigned to the vehicles? Can we anticipate passenger demand and preemptively rebalance vehicles across a city to decrease waiting times? Can we ensure that self-driving vehicles won’t increase traffic congestion? If the vehicles are electric, when should we charge their batteries to make sure they are on the road when they are needed? Will thousands of vehicles recharging at once destabilize the power grid? How should these sistems interact with existing infrastructure, such as the subway and commuter trains? Our research strives to answer these questions.

Decentralized decision-making in robotic networks

Depending on the trade-off between robustness and energy consumption, agents connect in more or less hierarchical structures. From Allerton '13

How can networks of hundreds or thousands of robots make decisions quickly, efficiently and robustly? How much time, battery power and wireless bandwidth is required to reach an agreement in a robotic swarm?

Our research explores the fundamental performance limitations of distributed consensus and the tradeoffs between time complexity, message and byte complexity (both proxies to battery usage), bandwidth complexity and robustness of algorithms for decentralized decision-making. Applications include leader election, data fusion and distributed optimization in robotic networks.

GalapagosUAV: aerial patrolling for conservation

The Piquero UAV developed by USFQ with Jorge Pantoja. Photo by Santiago J. Gutierrez

In collaboration with Universidad San Francisco de Quito, we worked on the design, construction and deployment of a swarm of UAVs to protect sharks in the Galapagos Marine Reserve from illegal poaching.

Our contribution included design of deployment algorithms for dynamic coverage and hardware and software design of the communication subsystem to robustly transmit images and videos in real-time from the planes to a ground station. The project is currently in a hiatus (but sharks in the Galapagos are protected by a new marine sanctuary!).

Publications

Journal papers

Rossi F., Zhang R., Hindy Y. and Pavone M. (2018), "Routing autonomous vehicles in congested transportation networks: structural properties and coordination algorithms", Autonomous Robots, 42(7), 1427-1442.
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},
}
Iglesias, R., Rossi, F., Zhang, R. and Pavone, M. (2018), "A BCMP Network Approach to Modeling and Controlling Autonomous Mobility-on-Demand Systems", International Journal of Robotics Research.
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      = ,
  year         = {2018},
  keywords     = {press},
  timestamp    = {2017-05-18},
}
Zhang, R., Rossi, F., and Pavone, M. (2018), "Analysis, Control, and Evaluation of Mobility-on-Demand Systems: a Queueing-Theoretical Approach", IEEE Transactions on Control of Networked Systems.
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{ZhangRossiEtAl2018,
  author       = {Zhang, R., Rossi, F. and Pavone, M.},
  title        = {Analysis, Control, and Evaluation of Mobility-on-Demand Systems: a Queueing-Theoretical Approach},
  journal      = {IEEE Transactions on Control of Network Systems},
  year         = {2018},
  timestamp    = {2017-12-25},
}

Conference papers

Salazar, M., Rossi, F., Schiffer, M., Onder, C. H. and Pavone, M. (2018), "On the Interaction between Autonomous Mobility-on-Demand and the Public Transportation Systems". In Proc. IEEE Conf. on Intelligent Transportation Systems. Maui, HI, November, 2018. Best Student Paper Award. In Press.
Abstract: Continuously increasing congestion in urban environments is a major source of ecological and stress-related health hazards. Besides an increase in traffic volumes and mobility demands, a lack of alternatives to extend the current transportation system is one of the major drivers for this pressing environmental problem. Hence, new mobility concepts that allow for a more flexible, efficient, and sustainable passenger transportation which mainly uses the current infrastructure are urgently needed. Against this background, we analyze the benefits of implementing intermodal autonomous mobility-on-demand systems in urban environments. Specifically, we study the coordination of a fleet of self-driving cars with the public transportation system. We present a flow based optimization approach that can be leveraged to maximize social welfare. Additionally, we design a pricing and tolling scheme that allows to realize the social optimum in a market with selfish agents. Finally, we provide a real-world case study for Manhattan, a district in New York that is currently facing serious congestion problems. Our results show that the coordination between autonomous mobility-on-demand fleets and public transit can yield significant benefits to the urban transportation network.
BibTeX:
@Inproceedings{SalazarRossiEtAl2018,
  author       = {Salazar, M. and Rossi, F. and Schiffer, M. and Onder, C. H. and Pavone, M.},
  title        = {On the Interaction between Autonomous Mobility-on-Demand and the Public Transportation Systems},
  booktitle    = proc_IEEE_ITSC,
  year         = {2018},
  note         = {Submitted. {Extended Version, Available} at \url{https://arxiv.org/abs/1804.11278}},
  address  = {Maui, HI},
  month    = nov,
  url      = {https://arxiv.org/pdf/1804.11278},
  keywords     = {sub},
  owner        = {frossi2},
  timestamp    = {2018-04-30},
}
Rossi F., Iglesias R., Alizadeh M. and Pavone M. (2018), "On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms", In Robotics: Science and Systems. Pittsburgh, PA, June, 2018.
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    = proc_RSS,
  year         = {2018},
  note         = {In Press},
  asl_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.},
  asl_address  = {Pittsburgh, Pennsylvania},
  asl_month    = jun,
  keywords     = {press},
  owner        = {frossi2},
  timestamp    = {2018-04-12},
}
Rossi F., Bandyopadhyay, S., Wolf, M., and Pavone, M. (2018), "Review of Multi-Agent Algorithms for Collective Behavior: a Structural Taxonomy", In 2018 IFAC Aerospace Controls TC Workshop: Networked & Autonomous Air & Space Systems (NAASS 2018). In Press.
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, F. and Bandyopadhyay, S. and Wolf, M. and Pavone, M.},
  title        = {Review of Multi-Agent Algorithms for Collective Behavior: a Structural Taxonomy},
  booktitle    = proc_IFAC_NAASS,
  year         = {2018},
  note         = {In Press},
  asl_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.},
  keywords     = {press},
  owner        = {frossi2},
  timestamp    = {2018-02-01},
}
Iglesias R., Rossi F., Wang, K., Hallac, D., Leskovec, J. and Pavone, M. (2018), "Data-Driven Model Predictive Control of Autonomous Mobility-on-Demand Systems", In 2018 International Conference on Robotics and Automation (ICRA). In Press.
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, R. and Rossi, F. and Wang, K. and Hallac, D. and Leskovec, J. and Pavone, M.},
  title        = {Data-Driven Model Predictive Control of Autonomous Mobility-on-Demand Systems},
  booktitle    = proc_IEEE_ICRA,
  year         = {2018},
  note         = {In Press},
  asl_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%.},
  asl_address  = {Brisbane, Australia},
  asl_month    = dec,
  keywords     = {press},
  owner        = {frossi2},
  timestamp    = {2017-09-16},
}
Iglesias R., Rossi F., Zhang R. and Pavone M. (2016), "A BCMP Network Approach to Modeling and Controlling Autonomous Mobility-on-Demand Systems", In Workshop on Algorithmic Foundations of Robotics. San Francisco, CA, December, 2016.
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, Ramon and Rossi, Federico and Zhang, Rick and Pavone, Marco},
  title = {A BCMP Network Approach to Modeling and Controlling Autonomous Mobility-on-Demand Systems},
  booktitle = {Workshop on Algorithmic Foundations of Robotics},
  year = {2016},
  url = {http://arxiv.org/abs/1607.04357}
}
Zhang R.*, Rossi F.* and Pavone M. (2016), "Routing Autonomous Vehicles in Congested Transportation Networks: Structural Properties and Coordination Algorithms", In Robotics: Science and Systems. Ann Arbor, MI, July, 2016.
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{ZhangRossiEtAl2016b,
  author = {Zhang, Rick and Rossi, Federico and Pavone, Marco},
  title = {Routing Autonomous Vehicles in Congested Transportation Networks: Structural Properties and Coordination Algorithms},
  booktitle = {Robotics: Science and Systems},
  year = {2016},
  url = {http://arxiv.org/abs/1603.00939},
  doi = {10.15607/rss.2016.xii.032}
}
Zhang R., Rossi F. and Pavone M. (2016), "Model Predictive Control of Autonomous Mobility-on-Demand Systems", In Proc. IEEE Conf. on Robotics and Automation. Stockholm, Sweden, May, 2016. , pp. 1382 - 1389.
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{ZhangRossiEtAl2016a,
  author = {Zhang, Rick and Rossi, Federico and Pavone, Marco},
  title = {Model Predictive Control of Autonomous Mobility-on-Demand Systems},
  booktitle = {Proc. IEEE Conf. on Robotics and Automation},
  year = {2016},
  pages = {1382 - 1389},
  url = {http://arxiv.org/abs/1509.03985},
  doi = {10.1109/ICRA.2016.7487272}
}
Rossi F. and Pavone M. (2014), "On the Fundamental Limitations of Performance for Distributed Decision-making in Robotic Networks", In Proc. IEEE Conf. on Decision and Control. Los Angeles, CA, December, 2014. , pp. 2433-2440.
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{RossiPavone14a,
  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},
  pages = {2433-2440},
  url = {http://arxiv.org/pdf/1409.4863},
  doi = {10.1109/CDC.2014.7039760}
}
Rossi F. and Pavone M. (2014), "Distributed Consensus with Mixed Time/Communication Bandwidth Performance Metrics", In Allerton Conf. on Communications, Control and Computing., September, 2014. , pp. 286-293.
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{RossiPavone14b,
  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},
  pages = {286-293},
  url = {http://arxiv.org/pdf/1410.0956},
  doi = {10.1109/ALLERTON.2014.7028468}
}
Rossi F. and Pavone M. (2013), "Decentralized Decision-making on Robotic Networks with Hybrid Performance Metrics", In Allerton Conf. on Communications, Control and Computing., October, 2013. , pp. 358-365.
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{RossiPavone13,
  author = {Federico Rossi and Marco Pavone},
  title = {Decentralized Decision-making on Robotic Networks with Hybrid Performance Metrics},
  booktitle = {Allerton Conf. on Communications, Control and Computing},
  year = {2013},
  pages = {358-365},
  url = {../pdf/Rossi.Pavone.Allerton13.pdf},
  doi = {10.1109/allerton.2013.6736546}
}

Theses

Rossi F. (2018), "On the Interaction between Autonomous Mobility-on-Demand Systems and the Built Environment: Models and Large Scale Coordination Algorithms". Ph.D. Thesis at: Stanford University, Dept. of Aeronautics and Astronautics. Stanford, California, March, 2018.
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 = {Federico Rossi},
  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},
  month = mar,
  address  = {Stanford, California},
}
Rossi F. (2013), "Decision making on robotic networks with hybrid performance metrics for planetary exploration applications". M.Sc. Thesis at: Politecnico di Milano, Dipartimento di Scienze e Tecnologie Aerospaziali. Milano, Italy, July, 2013. , pp. 132.
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 = {Federico Rossi},
  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},
  pages = {132},
  doi = {10589/81446}
  url = {http://hdl.handle.net/10589/81446}
}

Teaching and mentorship

I was the Teaching Assistant for Stanford’s Optimal Control and Introduction to Dynamic Optimization class (AA 203) in 2015, 2016, and 2017.

AA 203 includes a large final project: I immensely enjoyed advising students on projects including identifying the root cause of mobility impairment in patients suffering from stroke, controlling flexible surgical robots and tensegrity structures, and computing maximum-drift trajectories for Marty, Stanford’s self-driving DeLorean.

I have also been very fortunate to mentor several brilliant high school and undergraduate students who participated in internships with the Autonomous Systems Lab.

Past mentees include:

  • Quinn Wu (Gunn High School), now at UPenn
  • Ivan Maric (UC Berkeley), now at Apple
  • Luke Shimanuki (Amador Valley High School), now at MIT
  • Leonardo Franco-Munoz (Woodside High School), now at Cal Poly San Luis Obispo
  • Maggie Wang (Gunn High School), now at Harvard
  • Daniel Torres (Eastside College Preparatory School)
  • Yousef Hindy (Stanford University)

Contact Me