Target Heuristic RRT Based on Uniform Probability for Manipulator Path Planning

To solve the problem of high complexity, safety and reliability of multi-DOF manipulator trajectory planning in three-dimensioned space, based on the advantages of probability completeness and lightweight of rapidly-exploring random trees (RRT) algorithm in high-dimensioned space, a target heuristic RRT based on uniform probability (PH-RRT) was proposed. First, the threshold of probabilistic sampling was selected as the node standard based on the distribution mechanism of uniform probability, and it was compared with the random sampling value. When the random sampling value was within the set threshold value range, the target point was determined as a random point for node expansion. When the random sampling value was outside the set threshold range, random point was randomly generated, and the new node expanded under the target heuristic of the target gravity and the random point gravity. Then, based on the planned path, the breadth-first search idea was introduced to optimize the planned path to improve the smoothness of the path and reduce the length of the path. Experimental results show that the proposed method can well solve the problem of traditional method of RRT inherent blind search, and reduce the time of path planning and path length to improve the efficiency of path planning of manipulator.