Dynamic path planning strategy based on improved RRT^* algorithm

In order to solve the problem of path planning of mobile robots in a dynamic environment, an improved rapidly-exploring random tree^* (RRT^*) algorithm is proposed in this paper. First, the target bias sampling is introduced to reduce the randomness of the RRT^* algorithm, and then the initial path planning is carried out in a static environment. Secondly, apply the path in a dynamic environment, and use the initially planned path as the path cache. When a new obstacle appears in the path, the invalid path is clipped and the path is replanned. At this time, there is a certain probability to select the point in the path cache as the new node, so that the new path maintains the trend of the original path to a greater extent. Finally, MATLABis used to carry out simulation experiments for the initial planning and replanning algorithms, respectively. More specifically, compared with the original RRT^* algorithm, the simulation results show that the number of nodes used by the new improved algorithm is reduced by 43.19% on average.