Adversarially Robust Coordination for Autonomous Multi-Vehicle Systems

Project started in 2018 and was completed Q1 2021.

This project envisions to increase the existing levels of autonomy of networked multi-vehicle systems in adversarial environments by fundamentally considering for the first time both the safety and the resilience aspects of dynamic multi-agent networks.

By safety we mean the generation of guaranteed collision-free trajectories for multiple vehicles (agents) so that they navigate efficiently in cluttered environments while collaborating towards a common task (e.g., data gathering). Multi-agent collaboration in principle requires coordination and negotiation mechanisms among the agents; these mechanisms are implemented using information shared over wireless communication links. However, wireless communication is vulnerable to cyber-attacks. By resilience we mean the guaranteed safe accomplishment of the mission despite the presence of possible adversaries that can send malicious data over compromised communication links.

Our goal is to establish robust (resilient) communication structures for the network, as well as estimation (filtering) and control (coordination/negotiation) mechanisms that will allow the multi-agent network to tolerate or mitigate the adversarial effects of malicious data in the communication structure, while still maintaining safety guarantees.

Publications:

• J. Usevitch and D. Panagou, “Adversarial Resilience for Sampled-Data Systems under High-Relative-Degree Safety Constraints,” in IEEE Transactions on Automatic Control, doi: 10.1109/TAC.2022.3157791.
• J. Usevitch and D. Panagou, “Adversarial Resilience for Sampled-Data Systems using Control Barrier Function Methods”, 2021 American Control Conference (ACC), New Orleans, Louisiana, May 2021, pp 758-763.
• J. Usevitch, K. Garg and D. Panagou, “Strong Invariance using Control Barrier Functions: A Clarke Tangent Cone Approach,” 59th IEEE Conference on Decsision and Control (CDC), Jeju Island, South Korea, December 2020
• J. Usevitch and D. Panagou, “Resilient Finite-Time Consensus: A Discontinuous Systems Perspective”, 2020 American Control Conference (ACC), Denver, CO, July 2020
• J. Usevitch and D. Panagou, “Resilient Leader-Follower Consensus to Arbitrary Reference Values in Time-Varying Graphs”, 58th IEEE Conference on Decision and Control (CDC, Nice, France, 2019
• J. Usevitch and D. Panagou, “Determining r-Robustness of Arbitrary Digraphs Using Zero-One Integer Linear Programming,” 2019 American Control Conference (ACC), Philadelphia, PA, July 2019
• J. Usevitch, K. Garg, D. Panagou, “Finite-Time Resilient Formation Control with Bounded Inputs”, 57th IEEE Conference on Decision and Control (CDC), Miami Beach, FL, December 2018
• J. Usevitch and D. Panagou, “Resilient Leader-Follower Consensus to Arbitrary Reference Values,” 2018 American Control Conference (ACC), Milwaukee, WI, 2018
• J. Usevitch and D. Panagou, “Resilient Leader-Follower Consensus to Arbitrary Reference Values in Time-Varying Graphs,” IEEE Transactions on Automatic Control, vol. 65, no. 4, pp. 1755-1762, April 2020
• J. Usevitch and D. Panagou, “Determining r- and (r,s)-robustness of Digraphs using Mixed Integer Linear Programming,” Automatica, vol. 111, 108586, pp. 1-13, January 2020

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