A Fast Path Planing Solution For Multiple Obstacle Spaces

Abstract

Among the many path planning algorithms, the RRT* algorithm and the Informed RRT* algorithm are the most popular. Both of them can complete the single path planning effectively. However, because the planning effect of these algorithms is limited by the number of iterations, the optimal solution cannot be planned in a limited time. Therefore, a new algorithm-Self-Optimizing Growing Tree (SOGT) is proposed in this paper. On the premise of ensuring complete probability, heuristic bias sampling is used to improve the efficiency of the algorithm to explore free space. Then, the initial path is optimized by the maximum distance and the inflection point optimization using prior information to obtain an optimal path. In the simulation experiments, we give the comparison results of numerical simulations of SOGT algorithm, RRT* algorithm and Informed RRT* algorithm. The experiment proves that the SOGT algorithm can get an optimal path in less time, which fully reflects the characteristics of the algorithm with good real-time performance and strong adaptability.