Path Planning for Mobile Robots Based on Improved JPS and DWA Algorithms
[1]CHENG Bo,CAI Longshuai,GUO Guifang,et al.Path Planning for Mobile Robots Based on Improved JPS and DWA Algorithms[J].Journal of Zhengzhou University (Engineering Science),2026,47(XX):1-10.[doi:10.13705/j.issn.1671-6833.2025.03.018]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
47
Number of periods:
2026 XX
Page number:
1-10
Column:
Public date:
2026-09-10
- Title:
- Path Planning for Mobile Robots Based on Improved JPS and DWA Algorithms
- Keywords:
- jump point search algorithm; path planning; priority obstacle avoidance; dynamic weight evaluation function; conflict path
- CLC:
- TP242
- Abstract:
- To tackle the problems of heightened search blindness, elevated memory usage, and redundant nodes in the output path resulting from the access of a large number of irrelevant extension nodes in traditional JPS algorithms, an improved JPS algorithm is proposed. This algorithm is based on the priority of the target-point search direction and a dynamic-weight evaluation function. Firstly, according to the positions of the target point and the mobile robot, the priority of the direction where the target point lies during the algorithm’s path-finding process is enhanced. A distance-based dynamic-weight evaluation function is introduced to mitigate the resource waste and efficiency loss caused by searching for irrelevant nodes. Meanwhile, the global path output by the improved JPS algorithm undergoes secondary planning. Redundant nodes in the original path are eliminated, making the global path smoother. Secondly, the DWA algorithm is introduced and improved as a local path-planning algorithm. The improved DWA algorithm adopts a dynamic priority strategy based on collision distance to automatically avoid mobile robots on cross-paths. Finally, the improved JPS algorithm and DWA algorithm are separately simulated and verified. The results indicate that, compared with traditional jump-point algorithms, the improved algorithm in this paper reduces the average number of searched extended nodes by 60.0%, the average number of trajectory nodes by 43.6%, and the average number of path turning points by 23.9%. The improved DWA algorithm can effectively address the drawbacks and limitations of traditional DWA algorithms in dynamic environmental problems such as path conflicts, and improve the collaboration and adaptability of DWA algorithms in multi-robot path planning.
- References:
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Last Update:
2026-01-14
