Distributed Resilient Submodular Action Selection in Adversarial Environments

TL;DR

This paper introduces a distributed algorithm for multi-robot systems to select actions resiliently under adversarial attacks, ensuring performance and convergence similar to centralized methods in hostile environments.

Contribution

It proposes a fully distributed, resilient submodular maximization algorithm for multi-robot systems under attack, with proven worst-case performance guarantees and convergence to centralized solutions.

Findings

Algorithm guarantees performance under partial robot failures.

Converges to centralized solution in worst-case scenarios.

Validated through a distributed multi-robot exploration case study.

Abstract

In this letter, we consider a distributed submodular maximization problem for multi-robot systems when attacked by adversaries. One of the major challenges for multi-robot systems is to increase resilience against failures or attacks. This is particularly important for distributed systems under attack as there is no central point of command that can detect, mitigate, and recover from attacks. Instead, a distributed multi-robot system must coordinate effectively to overcome adversarial attacks. In this work, our distributed submodular action selection problem models a broad set of scenarios where each robot in a multi-robot system has multiple action selections that may fulfill a global objective, such as exploration or target tracking. To increase resilience in this context, we propose a fully distributed algorithm to guide each robot's action selection when the system is attacked. The proposed algorithm guarantees performance in a worst-case scenario where up to a portion of the robots malfunction due to attacks. Importantly, the proposed algorithm is also consistent, as it is shown to converge to the same solution as a centralized method. Finally, a distributed resilient multi-robot exploration problem is presented to confirm the performance of the proposed algorithm.