  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.
