A Fast Path Planning Method Based on RRT Star Algorithm

Abstract

The path planning algorithm for moving objects has high complexity, and the automatic path planning ability is poor, which cannot deal with complex practical environmental problems. A fast path planning algorithm based on RRT-Star is proposed. First, for the diagonal obstacles in path planning, the deadlock back-off method is used to realize obstacle detection, which effectively improves the safety of the path. Second, as it progresses, the algorithm uses a step size adjustment function to expand the step size, thereby increasing the speed at which the random tree can explore this space. In addition, based on the RRT-Star algorithm, the target deviation strategy is introduced, and the initial pheromone allocation principle is proposed. Finally, the pheromone is classified, and the pheromone on each path is superimposed according to the optimization objective. The results show that the RRT-Star fast path planning efficiency and the number of iterations are significantly better than the RRT algorithm and the ant colony algorithm.