Distributed Resilient Submodular Action Selection in Adversarial Environments

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