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 deploying multi-robot systems in a practical scenario is to ensure resilience against robot failures. recent work (L. Zhou et al., 2019) introduced the Resilient Coverage Maximization (RCM) problem where goal maximize submodular utility when are subject adversarial attacks or RCM known be NP-hard. In this letter, we propose two approximation algorithms for problem, namely, Ordered Greedy (OrG) Local Search (LS) algorithm. Both empirically outperform state-of-the-art solution terms accuracy running time. To demonstrate effectiveness our proposed solution, compare with existing brute force optimal We also perform case study on persistent monitoring show applicability setting.