Path Planning for Mobile Robots Based on Improved JPS and DWA Algorithms

NAVIGATE

Table of Contents

STATISTICS

Viewed10

Downloads37

Path Planning for Mobile Robots Based on Improved JPS and DWA Algorithms

[HTML] PDF下载 (37)

[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(3):47-56.[doi:10.13705/j.issn.1671-6833.2025.03.018]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 47 Number of periods: 2026 Issue 3 Page number: 47-56 Column: Public date: 2026-05-27

Title:: Path Planning for Mobile Robots Based on Improved JPS and DWA Algorithms

Author(s):: CHENG Bo¹, CAI Longshuai¹, GUO Guifang², ZHANG Xuan¹; 1.School of Construction Machinery, Chang’an University, Xi’an 710064, China;2.School of Information Engineering, Xizang Minzu University, Xianyang 712082, China

Keywords:: jump point search algorithm; path planning; priority obstacle avoidance; dynamic weight evaluation function; conflict path

CLC:: TP242

DOI:: 10.13705/j.issn.1671-6833.2025.03.018

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 was proposed. This algorithm 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 of target point in the algorithm’s path-finding process was enhanced. A distance-based dynamic-weight evaluation function was 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 underwent secondary planning. Redundant nodes in the original path were eliminated, making the global path smoother. Secondly, the DWA algorithm was introduced and improved as a local path-planning algorithm. The improved DWA algorithm adopted a dynamic priority strategy based on collision distance to automatically avoid mobile robots on cross-paths. Finally, the improved JPS algorithm and DWA algorithm were separately simulated and verified. The results indicated that, compared with traditional jump-point algorithms, the improved algorithm in this study reduced 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 could 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:: [1]卢凌霄, 董乾鹏, 张天乐, 等. 机器人运动学与运动规划算法综述[J]. 印刷与数字媒体技术研究, 2023(5): 1-16.
LU L X, DONG Q P, ZHANG T L, et al. A review of robot kinematics and motion planning algorithms[J]. Printing and Digital Media Technology Study, 2023(5): 1-16.
[2]白晓兰, 袁铮, 周文全, 等. 移动机器人路径规划算法研究综述[J]. 机械工程师, 2024(8): 24-28, 33.
BAI X L, YUAN Z, ZHOU W Q, et al. Survey of path planning algorithms for mobile robots[J]. Mechanical Engineer, 2024(8): 24-28, 33.
[3]HART P E, NILSSON N J, RAPHAEL B. A formal basis for the heuristic determination of minimum cost paths[J]. IEEE Transactions on Systems Science and Cybernetics, 1968, 4(2): 100-107.
[4]WANG H, LI G Q, HOU J, et al. A path planning method for underground intelligent vehicles based on an improved RRT∗ algorithm[J]. Electronics, 2022, 11(3): 294.
[5]高岳林,武少华.基于自适应粒子群算法的机器人路径规划[J].郑州大学学报(工学版),2020,41(4):46-51.
GAO Y L, WU S H. Robot path planning based on adaptive particle swarm optimization[J]. Journal of Zhengzhou University (Engineering Science), 2020, 41(4): 46-51.
[6]HAO K, ZHAO J L, LI Z S, et al. Dynamic path planning of a three-dimensional underwater AUV based on an adaptive genetic algorithm[J]. Ocean Engineering, 2022, 263: 112421.
[7]HARABOR D, GRASTIEN A, HARABOR D, et al. Online graph pruning for pathfinding on grid maps[C]∥Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence. New York:ACM, 2011: 1114-1119.
[8]丁承君, 阎欣怡, 冯玉伯, 等. 基于APF的AGV局部路径规划改进算法研究[J]. 计算机工程与应用, 2022, 58(22): 305-312.
DING C J, YAN X Y, FENG Y B, et al. Improved algorithm of AGV local path planning based on APF[J]. Computer Engineering and Applications, 2022, 58(22): 305-312.
[9]FOX D, BURGARD W, THRUN S. The dynamic window approach to collision avoidance[J]. IEEE Robotics & Automation Magazine, 1997, 4(1): 23-33.
[10] MAGYAR B, TSIOGKAS N, DERAY J, et al. Timedelastic bands for manipulation motion planning[J]. IEEE Robotics and Automation Letters, 2019, 4 (4): 3513-3520.
[11] SHEN D, CHEN Y B, LI L X, et al. Trajectory tracking for autonomous vehicles using robust model predictive control[J]. IFAC-PapersOnLine, 2024, 58 (10): 94-101.
[12] ZHENG X, TU X W, YANG Q H. Improved JPS algorithm using new jump point for path planning of mobile robot[C]∥2019 IEEE International Conference on Mechatronics and Automation (ICMA). Piscataway: IEEE, 2019: 2463-2468.
[13] MA L, GAO X, FU Y X, et al. An improved jump point search algorithm for home service robot path planning[C]∥2019 Chinese Control and Decision Conference (CCDC). Piscataway:IEEE, 2019: 2477-2482.
[14]黄健萌, 吴宇雄, 林谢昭. 移动机器人平滑JPS路径规划与轨迹优化方法[J]. 农业机械学报, 2021, 52(2): 21-29, 121.
HUANG J M, WU Y X, LIN X Z. Smooth JPS path planning and trajectory optimization method of mobile robot[J]. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(2): 21-29, 121.
[15]秦齐, 万熠, 侯嘉瑞, 等. 基于双向动态跳点搜索算法的AGV路径规划研究[J]. 单片机与嵌入式系统应用, 2021, 21(8): 55-58, 63.
QIN Q, WAN Y, HOU J R, et al. AGV path planning based on bidirectional dynamic jumping search algorithm[J]. Microcontrollers & Embedded Systems, 2021, 21(8): 55-58, 63.
[16] SU Q H, MA S B, WANG L Y, et al. Artificial potential field guided JPS algorithm for fast optimal path planning in cluttered environments[J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2022, 44(12): 602.
[17] CHEN T, CHEN S F, ZHANG K R, et al. A jump point search improved ant colony hybrid optimization algorithm for path planning of mobile robot[J]. International Journal of Advanced Robotic Systems, 2022, 19(5): 17298806221127953.
[18]张庆, 刘旭, 彭力, 等. 融合JPS和改进A∗算法的移动机器人路径规划[J]. 计算机科学与探索, 2021, 15(11): 2233-2240.
ZHANG Q, LIU X, PENG L, et al. Path planning for mobile robots based on JPS and improved A∗ algorithm[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(11): 2233-2240.
[19] KOBAYASHI M, MOTOI N. Path planning method considering blind spots based on ROS navigation stack and dynamic window approach for wheeled mobile robot[C]∥2022 International Power Electronics Conference. Piscataway:IEEE, 2022: 274-279.
[20]刘宙浩, 万超一, 尹明锋, 等. 改进A∗算法与DWA融合的移动机器人的路径规划算法研究[J]. 制造业自动化, 2023, 45(12): 55-60.
LIU Z H, WAN C Y, YIN M F, et al. Research on path planning algorithm of mobile robot based on improved A∗ algorithm and dynamic window approach[J]. Manufacturing Automation, 2023, 45(12): 55-60.
[21] BERTI H, SAPPA A D, AGAMENNONI O E. Improved dynamic window approach by using Lyapunov stability criteria[J]. Latin American Applied Research, 2008, 38(4): 289-298.
[22]王超, 梅瑛, 张溢, 等. 基于改进DWA算法的移动机器人避障研究[J]. 机械设计与研究, 2024, 40(1): 92-96.
WANG C, MEI Y, ZHANG Y, et al. Research on obstacle avoidance of mobile robot based on improved DWA algorithm[J]. Machine Design & Research, 2024, 40(1): 92-96.
[23]蔡佳成, 白克强, 李旭春, 等. 基于JPS改进的移动机器人路径规划算法[J]. 计算机应用研究, 2022, 39(7): 1985-1991.
CAI J C, BAI K Q, LI X C, et al. Improved path planning algorithm for mobile robot based on JPS[J]. Application Research of Computers, 2022, 39(7): 1985-1991.
[24]石英托, 陈华, 张连新, 等. 基于改进A∗算法的AGV转运机器人路径规划研究[J]. 制造技术与机床, 2022(5): 19-22.
SHI Y T, CHEN H, ZHANG L X, et al. Research on path planning of AGV transport robot based on improved A∗ algorithm[J]. Manufacturing Technology & Machine Tool, 2022(5): 19-22.

Similar References:

Memo

Last Update: 2026-05-27