Self-Organized Multi-Robot Path Planning and Chain Distribution Based on Improved DWA and A* Fusion in Unknown Space

Abstract

With the rise of autonomous driving in recent years, path planning has gained widespread attention. Traditional path planning needs to be based on a large amount of known information, which is not available for confined environments. Taking the complex indoor space where GPS cannot be used as the research background, the article designs a self-organised motion scheme for multi-intelligent body trolleys that includes exploration and path planning. By improving the DWA and A* algorithms, the multi-robot self-organisation achieves reasonable path planning, and the fusion of the two algorithms solves the contradictory problems of global planning being unable to avoid dynamic obstacles and local planning possibly falling into local optimum. After that, the pilot-following algorithm is added to guide the multi-intelligence body to operate in formation. By studying the constraints of hardware such as LiDAR and machine trolleys, the chain distribution of multiple intelligences is proposed to solve the problem of information loss caused by the discontinuous monitoring field of view. Eventually, when the carts are all in position, the whole area is covered and monitored using sensor fusion with multiple viewpoints. The feasibility of the explored scheme is verified by simulation experiments, and the feasibility and robustness of multi-sensor fusion is verified by specific hardware.