Abstract
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.
Highlights
Robot path planning [1] is to plan a path from the beginning to the ending, and can safely avoid the obstacles in the environment
The results show that the dynamic performance of the proposed algorithm is better than that of the contrast algorithm, and the Local Probability Road Map (PRM) algorithm can be used in dynamic environments
The sampling experiment in two-dimensional space shows that the stratified sampling is beneficial to the generation of probability map
Summary
Robot path planning [1] is to plan a path from the beginning to the ending, and can safely avoid the obstacles in the environment. Bohlin proposed a planning algorithm suitable for dynamic environments, which constructed a probability map in the environment containing only static obstacles, and quickly updated the probability map combined with the lazy evaluation mechanism [8] to obtain a new path after the introduction of dynamic obstacles. These two methods still describe the dynamic environments in a narrow sense and do not involve the movement of obstacles or the transient occlusion of the starting and ending point.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
