An Improved RRT Algorithm Based on Heuristic Function Strategy for Manipulator Path Planning

Manipulator path planning refers to planning the movement trajectory of a manipulator through algorithms and techniques to realize accurate and efficient operation. It is widely used in industrial production lines, medical surgeries, and other fields. The sampling-based rapid exploration random tree (RRT) algorithm has been widely used in high-dimensional manipulators due to its probabilistic completeness and outstanding expandability. However, the algorithm is purely random in the sampling session, which leads to the existing RRT planning algorithms still having low planning efficiency and poor path quality. To solve these problems, this paper proposes a new and improved method, which assigns a cost value to the sampling points and bases the selection of sampling points on the cost value, so that the growth of the path tree can be oriented to the goal point more quickly. A new evaluation metric, i.e., the average angle of each node in the path, is also designed to evaluate the quality of the path, and the smoother the path is, the more the metric converges to 180°. By comparing our improved RRT algorithm with the original algorithm in several different scenarios, the results show that our improvement possesses shorter running time, shorter path length, and larger average angle of path.