Research on the Dynamic Path Planning of Manipulators Based on a Grid-Local Probability Road Map Method

A Grid-Local Probability Road Map (PRM) method was proposed for the path planning of manipulators in dynamic environments. Based on the idea of boundary discretization, a double-grid model was built to obtain a mapping from dynamic obstacles to configuration space. The collision detection was simplified as a data indexing process to improve its efficiency. Times of collision detections were reduced by employing local programming strategies and the stratified sampling method. Moreover, the validity of sampling was increased. Taking the PUMA560 manipulator as a research object, the simulation experiments show that the time consumption of the proposed simplified collision-detection algorithm is about 14% of that of the standard one, and the stratified sampling is beneficial to the generation of probability maps compared with simple random sampling method. The simulation experiment of the static path planning shows that the proposed algorithm consumes an average of 10ms, which is superior to the comparison algorithm and has high efficiency and real-time performance. The simulation experiment of the dynamic path planning shows that the proposed algorithm consumes an average of 7ms per step, which is better than the comparison algorithm. The proposed algorithm can adjust the global path in real time to avoid obstacles as the environment changes. The algorithm mentioned has been proved to be efficient.