Coverage and Transition Planning in Obstacle-Rich Workspaces

This study proposes a comprehensive and efficient algorithm for coverage path planning, specifically designed for autonomous mobile robots operating on two-dimensional grid-based maps with fixed obstacles. The suggested approach merges several established techniques into a cohesive framework, encompassing row-based cellular decomposition, A*-based safe transition planning, and the dynamic optimization of entry and exit points for each segment of coverage. This synthesis allows for the creation of thorough and secure coverage paths while minimizing redundancy. The algorithm undergoes evaluation on maps of varying intricacy (5×5, 15×15, and 50×50), and its performance is measured using various metrics, including coverage density, average cell length, A* transition rate, and overall path efficiency. Results indicate that the proposed integration-based method consistently achieves high levels of coverage and path efficiency, while ensuring safe transitions between different areas. This framework proves to be not only practically efficient but also flexible across different scales, showcasing significant potential for real-world uses such as autonomous cleaning, agricultural monitoring, and emergency rescue operations.