Path Planning of McNum Wheel Robot based on the Fusion of RRT* and Artificial Potential Field Method

The traditional artificial potential Held method is easy to fall into the local minimum point of the potential field, that is, the “dead point” in the process of path planning in a complex and unknown environment, so that it is impossible to get out and find a suitable path, so that it is impossible to carry out complete planning in the unknown environment. The improved RRT* can carry out good global path planning, so the combination of RRT* and artificial potential field method can be adopted, the RRT* algorithm is adopted globally, and the artificial potential field method is used locally, when the robot falls into the “dead spot”, give feedback, let the RRT* algorithm take the robot out of the “dead spot”, and then continue to implement the artificial potential field method. The results show that the fused algorithm can effectively solve the situation that traditional algorithms tend to fall into local minimums. At the same time, compared with the traditional algorithm, the planning path selection in the random obstacle environment is better and the consumption is lower, which effectively improves the path planning efficiency.