Full coverage path planning strategy for cleaning robots in semi-structured outdoor environments

With the increasing demand for autonomous cleaning solutions in urban environments, Coverage Path Planning (CPP) technology has seen widespread development in the field of cleaning robots. Cleaning robots must navigate complex semi-structured environments, which are often characterized by intricate area layouts and diverse obstacles. This study proposes a CPP framework for semi-structured environments, integrating a map preprocessor and a coverage path planner. The map preprocessor accurately processes environmental boundaries and obstacles, optimizing the environmental information to enable the coverage path planner to generate more efficient paths. This improves coverage efficiency, allowing cleaning robots to adapt to semi-structured community environments. Both simulation and real-world experiments demonstrate that the proposed framework offers significant performance advantages over state-of-the-art methods. By maximizing the coverage rate and minimizing the number of turns in the coverage path, our approach significantly enhances autonomous cleaning robots' cleaning efficiency and quality. This research improves the operational capabilities of cleaning robots in semi-structured environments and provides valuable insights and practical guidance for the broader field of autonomous path planning.