Resilience in Networked Robotic Systems

Special Section on Resilience in Networked Robotic Systems 

Volume 38, Number 1, February 2022, Pages 2 - 301

Guest Editors

Amanda Prorok, University of Cambridge
Vijay Kumar, University of Pennsylvania
Brian Sadler, Army Research Laboratory
Gaurav Sukhatme, University of Southern California


Overseeing Editor

Paolo Robuffo Giordano, CNRS-IRISA



Original call for papers.

This Special Section of the IEEE TRANSACTIONS IN ROBOTICS is devoted to the topic of resilience in networked robotic systems. This collection of articles aims to provide a deeper understanding of resilience as it pertains to multirobot systems, and to disseminate the current advances in designing and operating networked robotic systems. We understand resilience to be a characteristic that enables a multirobot system to withstand or overcome unexpected adverse conditions or shocks, and unknown, unmodeled disturbances. It refers to the contingent nature of the robots’ behaviors that is aimed at preserving their functionality or minimizing the time periods during which their functionality is compromised.

The 17 articles in this section explore new algorithmic and mathematical foundations toward resilience. Jointly, they cover problems within perception, planning, and control. Individually, they focus on tackling specific types of stressors, including adversarial attacks, unmodeled noise, communication disruptions, and failure of resources and robot capabilities. As a guide to this Special Section, we have organized the papers into four groups:

1) articles that propose methods to deal with adversarial attacks;
2) articles that focus on methods that exploit structural changes and other multirobot coordination mechanisms;
3) articles that focus on issues due to communication network disruptions; and
4) articles that address policy synthesis and dynamic controller tuning.

Introduction to the Special Section on Resilience in Networked Robotic Systems

A. Prorok, V. Kumar, B. Sadler, and G. Sukhatme

Resilience to Adversarial Attacks

[1] Crowd Vetting: Rejecting Adversaries via Collaboration With Application to Multirobot Flocking, F. Mallmann-Trenn; M. Cavorsi; S. Gil 
[2] Attack-Resilient Path Planning Using Dynamic Games With Stopping States, S. Banik; S. D. Bopardikar
[3] Resilient Trajectory Propagation in Multirobot Networks, J. Usevitch; D. Panagou       
[4] Dynamic Resilient Containment Control in Multirobot Systems, M. Santilli; M. Franceschelli; A. Gasparri
[5] Characterizing Trust and Resilience in Distributed Consensus for Cyberphysical Systems, M. Yemini; A. Nedić; A. J. Goldsmith; S. Gil
[6] Detection of Nonrandom Sign-Based Behavior for Resilient Coordination of Robotic Swarms, P. J. Bonczek; R. Peddi; S. Gao; N. Bezzo
[7] Resilient Consensus in Robot Swarms With Periodic Motion and Intermittent Communication, X. Yu; D. Saldaña; D. Shishika; M. Ani Hsieh

Resilience by Exploiting Structural Changes and Other Coordination Mechanisms

Resilience to Communication Network Disruptions

Resilience by Policy Synthesis and Controller Tuning

[14] Resilient Supervisory Multiagent Systems, K. Baxevani; A. Zehfroosh; H. G. Tanner
[15] Resilient Active Information Acquisition With Teams of Robots, B. Schlotfeldt; V. Tzoumas; G. J. Pappas
[16] Congestion-Aware Policy Synthesis for Multirobot Systems, C. Street; S. Pütz; M. Mühlig; N. Hawes; B. Lacerda
[17] Outlier-Robust Estimation: Hardness, Minimally Tuned Algorithms, and Applications, P. Antonante; V. Tzoumas; H. Yang; L. Carlone