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Robot Path Planning Based on Multi-strategy Improved RRT* Algorithm

In order to solve the problems of the commonly used RRT* algorithm, such as the large amount of calculation in the uniform random sampling process and the inconsistent results of multiple planned paths in the same environment, a navigation path planning method for underwater robots based on the multi-strategy improved RRT* algorithm was proposed. Firstly, a bidirectional expanding random tree search strategy is used to speed up the path search process, and then the original planned path is used as a path cache to avoid random sampling. At the same time, aiming at the problem of slow convergence of the basic ant colony algorithm in the search process, an improved ant colony algorithm is proposed. By using a new heuristic function and pheromone update rule, the algorithm convergence speed is accelerated. Finally, the improved RTT* algorithm and the improved ant colony algorithm are combined to form an underwater robot path exploration algorithm. Compared with the basic RRT* algorithm, the ant colony algorithm and the particle swarm algorithm, the experimental results show that the improved algorithm has fewer nodes, faster convergence speed and higher accuracy in the path planning of underwater robots.