Failure-Resilient Coverage Maximization With Multiple Robots

• Published in: IEEE robotics and automation letters Vol. 6; no. 2; pp. 3894 - 3901
• Main Authors: Ishat-E-Rabban, Md, Tokekar, Pratap
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximation algorithms; Cooperating robots; Environmental monitoring; Greedy algorithms; Linear programming; Maximization; multi-robot systems; Multiple robots; Optimization; path planning for multiple mobile robots or agents; Resilience; Robot sensing systems; Robots; Sorting; Trajectory
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2021.3067275

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 (L. Zhou et al., 2019) 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 letter, 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.