Resilient Multi-Robot Coverage Path Redistribution Using Boustrophedon Decomposition for Environmental Monitoring.

This study introduces a resilient and adaptive multi-robot coverage path planning approach based on the Boustrophedon Cell Decomposition algorithm, designed to dynamically redistribute coverage tasks in the event of robot failures. The proposed method ensures minimal disruption and maintains a balanced workload across operational robots through a propagation-based redistribution strategy. By iteratively reallocating the failed robot's coverage path to neighboring robots, the method prevents any single robot from becoming overburdened, ensuring efficient task distribution and continuous environmental monitoring.

Hierarchical Area-Based and Path-Based Heuristic Approaches for Multirobot Coverage Path Planning with Performance Analysis in Surveillance Systems.

In this study, we present a systematic exploration of hierarchical designs for multirobot coverage path planning (MCPP) with a special focus on surveillance applications. Unlike conventional studies centered on cleaning tasks, our investigation delves into the realm of surveillance problems, specifically incorporating the sensing range (SR) factor equipped on the robots. Conventional path-based MCPP strategies considering SR, primarily rely on naive approaches, generating nodes (viewpoints) to be visited and a global path based on these nodes.