Heuristic sparse sampling path planning algorithm for complex scenes

In this paper, a heuristic sparse sampling method is proposed to solve the path planning problems in complex scenarios such as narrow passages and zigzags. The proposed approach first obtains sampling points around the obstacles that obstruct the linear link between the start and end points, and expands the sampling points to ensure that there is a path connection between any two sampling points. Then use an improved ant colony algorithm to plan a path between these sampling points. Finally path compression is performed to shorten the path length. Experimental results show that this method is better than the Informed-RRT* algorithm in terms of planning efficiency and path length when performing path planning in complex scenes such as narrow passages and zigzags.