Abstract
Autonomous mobile robots (AMRs) face challenges in navigating complex environments efficiently. To manoeuvre through both narrow and wide spaces, AMRs require two essential design features: a compact form for tight areas as well as a large configuration with omnidirectional movement for wide spaces. This study utilizes inhouse designed Expand and Collapse Variable Width Robot (ECVWR) to demonstrate effective area coverage. These ECVWR can contract to navigate constrained spaces and expand to optimize coverage in open areas. Existing methods for achieving complete area coverage do not account for the reconfiguration or the change in footprint. To address this issue, we propose the Depth-First Search (DFS) for Complete Coverage Path Planning Strategy (CCPPS) for ECVWRs. This method allows adjustments to the generation of waypoints in CCPPS, minimizing the path length. The simulation study shows that our proposed CCPPS outperforms contemporary state-of-the-art CCPPS, namely, GBNN, exhibiting superior expanded area coverage, reduced travel distance, and enhanced computational efficiency. Moreover real-world experiment to further benchmark the efficacy of our proposed algorithm. The proposed CCPPS is generic and can be extended to other variable footprint robots.