Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (9): 3020-3031.doi: 10.13229/j.cnki.jdxbgxb.20250381

Previous Articles    

Path planning of workshop mobile robots integrated with improved A* and DWA algorithms

Fu ZHANG1(),Wei-dong HAN1,Ruo-fei BAO1,Ya-kun ZHANG1,Ya-fei WANG2,San-ling FU3   

  1. 1.College of Agricultural Equipment Engineering，Henan University of Science and Technology，Luoyang 471003，China
    2.College of Agricultural Engineering，Jiangsu University，Zhenjiang 212013，China
    3.College of Physics Engineering，Henan University of Science and Technology，Luoyang 471003，China
  • Received:2025-04-29 Online:2025-09-01 Published:2025-11-14

RICH HTML

 0   

PDF (PC)

49

Abstract:

To achieve efficient path planning for mobile robots in complex workshop environments， a hybrid path planning method integrating improved A* algorithm and the DWA algorithm was proposed. Firstly， the global path planning A* algorithm was optimized by improving the heuristic function， taking into account the influences of obstacle density， the number of turns， and motion inertia factors. Secondly， weight factors were introduced to dynamically adjust the heuristic function weights， the Douglas-Peucker algorithm was employed to simplify the path， and the B-Spline algorithm was applied to smooth it. Finally， the local path planning DWA algorithm was improved， the key points of the globally optimal path were used as target points in the DWA alogorithmprocess. A trajectory evaluation function was reconstructed by introducing three evaluation terms： the distance between the current point and the target point， the distance between the current point and dynamic obstacles， and the path tracking cost. Simulation results showed that， compared with the traditional A* algorithm， the improved A* algorithm reduced the path length， planning time， number of expanded nodes， and number of turning points by 15.80%， 81.20%， 2.30%， and 74.10%， respectively. In scenarios involving temporary static obstacles and dynamic obstacles， the runtime of the integrated improved algorithm was reduced by 22.02% and 22.32%， respectively. Experimental results demonstrated that the integrated improved algorithm enabled efficient path planning and safe obstacle avoidance.

Key words: workshop mobile robot, path planning, dynamic obstacle avoidance, A* algorithm, DWA algorithm

CLC Number: 

  • TP242

Fig.1

Grid map"

Fig.2

Obstacle distribution map in circular area"

Fig.3

Schematic diagram of the number of turns on the route"

Fig.4

Schematic diagram of path node motion inertia"

Table 1

Dynamic adjustment effect of weight"

h(n)h(n)hstart1-h(n)hstartw(n)
1001.000.003.00
750.750.251.94
500.500.501.45
250.250.751.21
00.001.001.10

Fig.5

Schematic diagram of eliminating redundant points and smoothing the path"

Fig.6

Schematic diagram of the distance cost from the current point to the global target point"

Fig.7

Flowchart of the fusion-improved A* and DWA algorithms"

Fig.8

Simulation results of global path planning algorithm"

Table 2

Path planning data comparison"

算法长度/m规划时间/s扩展节点数/个拐点数/个
传统A*131.172.614 50027
Dijkstra139.941.985 47713
改进A*110.430.494 3987

Fig.9

Path planning length and time bar chart"

Fig.10

Path planning expanded node number and inflection point number bar chart"

Fig.11

Schematic of path planning in static scenario fused with traditional algorithms"

Fig.12

Schematic of path planning in static scenario fused with improved algorithms"

Fig.13

Schematic of path planning in a scenario with added static obstacles"

Fig.14

Schematic of path planning in a scenario with added dynamic obstacles"

Fig.15

Mobile robot MR600"

Fig.16

Workshop environment and grid map"

Fig.17

Schematic of global path planning experiment in a real-world environment"

Fig.18

Schematic of experiment in a static scenario within a real-world environment"

Fig.19

Schematic of obstacle-added scenario experiment in a real-world environment"

[1] 向抒林, 陈炜, 程伟, 等. 石油化工储罐区巡检机器人研究现状与展望[J]. 石油化工腐蚀与防护, 2024, 41(6): 1-8.
Xiang Shu-lin, Chen Wei, Cheng Wei, et al. Research status and prospect of inspection robot for petrochemical storage tank area[J]. Petrochemical Corrosion and Protection,2024, 41(6): 1-8.
[2] Zhang H J, Jiang F, Liu C K, et al. Review of autonomous path planning for planetary rovers[J]. Chinese Journal of Engineering, 2024, 46(11): 2063-2075.
[3] Zhong J K, Kong D Y, Wei Y L, et al. Efficiency-optimized path planning algorithm for car-like mobile robots in bilateral constraint corridor environments[J]. Robotics and Autonomous Systems, 2025, 186: No.104923.
[4] Zhang Z W, Wu L H, Zhang W Q, et al. Energy-efficient path planning for a single-load automated guided vehicle in a manufacturing workshop[J]. Computers & Industrial Engineering, 2021, 158: No.107397.
[5] Katona K, Neamah H A, Korondi P. Obstacle avoidance and path planning methods for autonomous navigation of mobile robot[J]. Sensors, 2024, 24(11): No.2411.
[6] 霍凤财, 迟金, 黄梓健, 等. 移动机器人路径规划算法综述[J]. 吉林大学学报: 信息科学版, 2019, 36(6): 639-647.
Huo Feng-cai, Chi Jin, Huang Zi-jian, et al. A review of path planning algorithms for mobile robots[J]. Journal of Jilin University (Information Science Edition), 2019, 36(6): 639-647.
[7] 姚得鑫, 伞红军, 王雅如, 等. 移动机器人路径规划中A*算法的改进研究[J]. 系统仿真学报, 2024, 36(11): 2684-2698.
Yao De-xin, Hong-jun San, Wang Ya-ru, et al. Research on the improvement of A* algorithm in path planning of mobile robot[J]. Journal of System Simulation, 2024, 36(11): 2684-2698.
[8] 杨姝慧, 郝子鑫, 李彬. 机器人路径规划算法研究分析与综述[J]. 齐鲁工业大学学报, 2024, 38(5): 37-46.
Yang Shu-hui, Hao Zi-xin, Li Bin. Research analysis and summary of robot path planning algorithm[J]. Journal of Qilu University of Technology, 2024, 38(5): 37-46.
[9] Chen H, Lin Z, Chen Z, et al. Adaptive DWA algorithm with decision tree classifier for dynamic planning in USV navigation[J]. Ocean Engineering, 2025, 321: No.120328.
[10] Wang Y C, Li J L, Zhao S Q, et al. Hybrid path planning for USV with kinematic constraints and COLREGS based on improved APF-RRT and DWA[J]. Ocean Engineering, 2025, 318: No.120128.
[11] 王云亮, 张赛, 吴艳娟. 基于改进A*平滑性路径规划算法研究[J]. 计算机应用与软件, 2025, 42(1): 258-263, 276.
Wang Yun-liang, Zhang Sai, Wu Yan-juan. Research on path planning algorithm based on improved A* smoothness[J]. Computer Applications and Software, 2025, 42(1): 258-263, 276.
[12] 张宸威, 黄平, 张国恒, 等. 基于改进A*算法的自适应路径规划研究[J]. 通信与信息技术, 2025(1): 43-45, 122.
Zhang Chen-wei, Huang Ping, Zhang Guo-heng, et al. Research on adaptive path planning based on improved A* algorithm[J]. Communication and Information Technology, 2025(1): 43-45, 122.
[13] Zhang J Q, Ling H J, Tang Z H, et al. Path planning of USV in confined waters based on improved A∗ and DWA fusion algorithm[J]. Ocean Engineering, 2025, 322: No.120475.
[14] Wang Y C, Fu C H, Huang R Y, et al. Path planning for mobile robots in greenhouse orchards based on improved A* and fuzzy DWA algorithms[J]. Computers and Electronics in Agriculture, 2024, 227: No.109598.
[15] Guo H, Li Y, Wang H, et al. Path planning of greenhouse electric crawler tractor based on the improved A* and DWA algorithms[J]. Computers and Electronics in Agriculture, 2024, 227: No.109596.
[16] Ayalew W, Menebo M, Merga C, et al. Optimal path planning using bidirectional rapidly-exploring random tree star-dynamic window approach (BRRT*-DWA) with adaptive Monte Carlo localization (AMCL) for mobile robot[J]. Engineering Research Express, 2024, 6(3): No.035212.
[17] Jathunga T, Rajapaksha S. Improved path planning for multi-robot systems using a hybrid probabilistic roadmap and genetic algorithm approach[J]. Journal of Robotics and Control, 2025, 6(2): 715-733.
[1] Jian LI,Xiao-hai SUN,Chang-yi LIAO,Jian-ping YANG. Robot path planning method based on double-origin ant colony algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(1): 325-332.
[2] Jin ZHU,Qi HUANG. Automated terminal horizontal transportation scheduling and route planning under network resource allocation [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(8): 2245-2255.
[3] Shuai-shuai SUN,Chun-xiao FENG,Liang ZHANG. Path planning for multimodal quadruped robots based on discrete sampling [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(4): 1120-1128.
[4] Hao ZHENG,Li-jun YU,Peng-peng ZHI,Zhong-lai WANG. Dynamic obstacle avoidance strategy for flapping⁃wing micro air vehicles [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(9): 2732-2740.
[5] Zhen-yu WU,Xiao-fei LIU,Yi-pu WANG. Trajectory planning of unmanned system based on DKRRT*⁃APF algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 781-791.
[6] Fan YANG,Zhi-qiang ZHAI,Yuan-yuan WANG,Zhong-xiang ZHU,Yue-feng DU,En-rong MAO. Design of assembly system for tractor transmission based on virtual reality [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(10): 3038-3044.
[7] Jia-xu ZHANG,Chen WANG,Jian ZHAO,Chun-yan BU. Path planning and tracking control for narrow parallel parking space [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1879-1886.
[8] Jia-xu ZHANG,Xin-zhi WANG,Jian ZHAO,Zheng-tang SHI. Path planning and discrete sliding mode tracking control for high⁃speed lane changing collision avoidance of vehicle [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 1081-1090.
[9] Xiao-jian HAN,Wei-qiang ZHAO,Li-jun CHEN,Hong-yu ZHENG,Yang LIU,Chang-fu ZONG. Local path planning of bus based on RS-RRT algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(5): 1428-1440.
[10] QIAN Li-jun, HU Wei-long, LIU Qing, WU Bing. Multiple segment method for automatic parking path planning and its key technology [J]. 吉林大学学报(工学版), 2016, 46(3): 785-791.
[11] ZHOU Bing-hai, ZHOU Qi. Impending deadlock-free scheduling method for unified AMHS in semiconductor FABs [J]. 吉林大学学报(工学版), 2016, 46(2): 595-601.
[12] KANG Bing, WANG Xi-hui, LIU Fu. Path planning of searching robot based on improved ant colony algorithm [J]. 吉林大学学报(工学版), 2014, 44(4): 1062-1068.
[13] LIU Hou-de, LIANG Bin, XU Wen-fu, MU Qing-tao, YU Jiang-hua. Motion prediction and autonomous path planning for spinning target capturing [J]. 吉林大学学报(工学版), 2014, 44(3): 757-764.
[14] LIU Gang, LIU Yu-bin, ZHAO Jie, ZHU Lei. Path planning for a new mine rescue robot base on visual tangent graphs [J]. 吉林大学学报(工学版), 2011, 41(4): 1107-1112.
[15] ZHAO Yang, ZHAO Ji, ZHANG Lei, JI Li-Zhe. Robotic blade grinding based on reverse engineering [J]. 吉林大学学报(工学版), 2009, 39(05): 1176-1180.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd