Distributed Adaptive and Resilient Control of Multi-Robot Systems with Limited Field of View Interactions

TL;DR

This paper develops a distributed control framework for multi-robot systems with limited field of view sensors, incorporating adaptive gain tuning and attack resilience to improve robustness and performance.

Contribution

It introduces a novel adaptive gain tuning method combined with H infinity attack resilience protocols for multi-robot systems with limited FOV sensors.

Findings

Adaptive gain tuning balances interaction strengths dynamically.

H infinity control guarantees bounded error under sensor attacks.

Simulation results validate theoretical robustness and effectiveness.

Abstract

In this paper, we consider two coupled problems for distributed multi-robot systems (MRSs) coordinating with limited field of view (FOV) sensors: adaptive tuning of interaction gains and rejection of sensor attacks. First, a typical shortcoming of distributed control frameworks (e.g., potential fields) is that the overall system behavior is highly sensitive to the gain assigned to relative interactions. Second, MRSs with limited FOV sensors can be more susceptible to sensor attacks aimed at their FOVs, and therefore must be resilient to such attacks. Based on these shortcomings, we propose a comprehensive solution that combines efforts in adaptive gain tuning and attack resilience to the problem of topology control for MRSs with limited FOVs. Specifically, we first derive an adaptive gain tuning scheme based on satisfying nominal pairwise interactions, which yields a dynamic balancing of interaction strengths in a robot's neighborhood. We then model additive sensor and actuator attacks (or faults) and derive H infinity control protocols by employing a static output-feedback technique, guaranteeing bounded L2 gains of the error induced by the attack (fault) signals. Finally, simulation results using ROS Gazebo are provided to support our theoretical findings.