Heuristic-RRT*: An Optimal Motion Planning Algorithm Inspired by Plant Tropism and Guided by Environmental Index

The Rapidly-exploring Random Tree (RRT) algorithm is one of the most popular algorithms for motion planning problems. However, the slow convergence of RRT* in searching the initial solution caused the low efficiency. Inspired by the mechanism of plant tropism, an environmental index-guided heuristic algorithm, the Heuristic-RRT*(H-RRT*) is proposed in this paper to improve the search efficiency: the growth direction of the search tree consists of a random term and a directional term; the directional term is determined by the location of the current node, the goal point the local microenvironment; the weights of the random and directional terms are determined by the environmental index; the search pattern is determined by the environmental index. Three search patterns are defined for various environments. The optimal solution can be obtained by combining the informed sampling strategy. The directional guidance of the H-RRT* algorithm can provide a better initial searching tree and a higher convergence rate. Compared to the RRT and GB-RRT algorithms, the H-RRT* algorithm demonstrated an initial solution speed improvement of 24.16% and 14.98%, respectively. And the quality of the optimal path of H-RRT* is typically 46.12% and 18.83% better than that of RRT* and GB-RRT*. Especially in sparse high-dimensional space like robotic arm joint space obstacle avoidance path planning, H-RRT* showed excellent performance.