Papers › Failure-Resilient Coverage Maximization with Multiple Robots
Failure-Resilient Coverage Maximization with Multiple Robots
Ishat E Rabban, Pratap Tokekar
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections