Failure-Resilient Coverage Maximization with Multiple Robots

The task of maximizing coverage using multiple robots has several applications such as surveillance, exploration, and environmental monitoring. A major challenge of deploying such multi-robot systems in a practical scenario is to ensure resilience against robot failures. A recent work introduced the Resilient Coverage Maximization (RCM) problem where the goal is to maximize a submodular coverage utility when the robots are subject to adversarial attacks or failures. The RCM problem is known to be NP-hard. In this paper, we propose two approximation algorithms for the RCM problem, namely, the Ordered Greedy (OrG) and the Local Search (LS) algorithm. Both algorithms empirically outperform the state-of-the-art solution in terms of accuracy and running time. To demonstrate the effectiveness of our proposed solution, we empirically compare our proposed algorithms with the existing solution and a brute force optimal algorithm. We also perform a case study on the persistent monitoring problem to show the applicability of our proposed algorithms in a practical setting.