|Table of Contents|

[1] SUN Zhe, CHENG Jiajia, BI Yunrui, ZHANG Xu, et al. Robot path planning based on a two-stage DE algorithm and applications [J]. Journal of Southeast University (English Edition), 2025, 41 (2): 244-251. [doi:10.3969/j.issn.1003-7985.2025.02.014]
Copy

Robot path planning based on a two-stage DE algorithm and applications()
基于两阶段DE算法的机器人路径规划及应用
Share：

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
41
Issue:
2025 2
Page:
244-251
Research Field:
Automation
Publishing date:
2025-06-17

Info

Title:
Robot path planning based on a two-stage DE algorithm and applications
基于两阶段DE算法的机器人路径规划及应用
Author(s):
SUN Zhe1, CHENG Jiajia1, BI Yunrui2, ZHANG Xu3, SUN Zhixin1
1.Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2.School of Automation, Nanjing Institute of Technology, Nanjing 211167, China
3.Anhui Yougu Express Intelligent Technology Co., Ltd, Wuhu 241399, China
孙哲1, 程佳佳1, 毕云蕊2, 张旭3, 孙知信1
1.南京邮电大学江苏省邮政大数据技术与应用工程研究中心，南京 210003
2.南京工程学院自动化学院，南京 211167
3.安徽邮谷快递智能科技有限公司，芜湖 241399
Keywords:
path planning;  differential evolution algorithm;  grid method;  parameter adaptive adjustment
路径规划; 差分进化算法; 网格法; 参数自适应调整
PACS:
TP249
DOI:
10.3969/j.issn.1003-7985.2025.02.014
Abstract:
To tackle the path planning problem, this study introduced a novel algorithm called two-stage parameter adjustment-based differential evolution (TPADE). This algorithm draws inspiration from group behavior to implement a two-stage scaling factor variation strategy. In the initial phase, it adapts according to environmental complexity. In the following phase, it combines individual and global experiences to fine-tune the orientation factor, effectively improving its global search capability. Furthermore, this study developed a new population update method, ensuring that well-adapted individuals are retained, which enhances population diversity. In benchmark function tests across different dimensions, the proposed algorithm consistently demonstrates superior convergence accuracy and speed. This study also tested the TPADE algorithm in path planning simulations. The experimental results reveal that the TPADE algorithm outperforms existing algorithms by achieving path lengths of 28.527 138 and 31.963 990 in simple and complex map environments, respectively. These findings indicate that the proposed algorithm is more adaptive and efficient in path planning.
针对路径规划问题，提出了一种基于两阶段参数调整的差分进化算法。受群体行为的启发，该算法提出了一种缩放因子的两阶段调整策略，前期根据环境复杂度适应性调整，后期结合个体的方向因子，考虑自身与全局经验进行自适应调整，有效提升了全局搜索能力。同时，设计了一种新的种群更新策略用于更新和保留适应度良好的个体，从而提高了种群多样性。在不同维度的基准函数测试中，所提算法均表现出更好的收敛精度和速度。进一步地，采用所提算法进行路径规划仿真实验。结果表明，该算法在简单和复杂2种地图环境下规划出的路径长度分别为28.527 138 和 31.963 990，均优于其他对比算法，因此所提算法在路径规划中具有更强的适应性和更高的效率。

References:

[1]DURAKLI Z, NABIYEV V. A new approach based on Bezier curves to solve path planning problems for mobile robots[J]. Journal of Computational Science, 2022, 58: 101540.
[2]YAN F, CHEN J H, WU T, et al. UAV trajectory planning algorithm for data collection in wireless sensor networks [J]. Journal of Southeast University (English Edition), 2020, 36 (4): 376-384.
[3]THORESEN M, NIELSEN N H, MATHIASSEN K, et al. Path planning for UGVs based on traversability hybrid A[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 1216-1223.
[4]MIAO C W, CHEN G Z, YAN C L, et al. Path planning optimization of indoor mobile robot based on adaptive ant colony algorithm[J]. Computers & Industrial Engineering, 2021, 156: 107230.
[5]LI C G, HUANG X, DING J, et al. Global path planning based on a bidirectional alternating search A* algorithm for mobile robots[J]. Computers & Industrial Engineering, 2022, 168: 108123.
[6]ZHANG J X, WANG Z W, GUO C, et al. Autonomous valet parking path planning for dynamic obstacle scene[J]. Journal of Southeast University (Natural Science Edition), 2022, 52(2): 369-376. (in Chinese)
[7]DING J, ZHOU Y X, HUANG X, et al. An improved RRT* algorithm for robot path planning based on path expansion heuristic sampling[J]. Journal of Computational Science, 2023, 67: 101937.
[8]WU B, CHI X N, ZHAO C C, et al. Dynamic path planning for forklift AGV based on smoothing A* and improved DWA hybrid algorithm[J]. Sensors, 2022, 22(18): 7079.
[9]ZHANG J X, WANG Z W, GUO C, et al. Autonomous valet parking path planning based on hierarchical architecture[J]. Journal of Southeast University (Natural Science Edition), 2021, 51(5): 883-888.(in Chinese)
[10]LIN S W, LIU A, WANG J G, et al. An intelligence-based hybrid PSO-SA for mobile robot path planning in warehouse[J]. Journal of Computational Science, 2023, 67: 101938.
[11]ZHAO X, CHU W H, REN G, et al. Low-carbon school bus routing optimization model considering vehicle detour[J]. Journal of Southeast University (Natural Science Edition), 2024, 54(1): 192-199. (in Chinese)
[12]FU J C,FU R D,ZHANG Y.Anti-reconnaissance path planning technique of missile based on distributed genetic algorithm and improved artificial potential field method[J].Journal of Southeast University (Natural Science Edition),2023,53(4): 709-717. (in Chinese)
[13]DAI Y N, YU J Y, ZHANG C, et al. A novel whale optimization algorithm of path planning strategy for mobile robots[J]. Applied Intelligence, 2023, 53(9): 10843-10857.
[14]WU D M, HAO F M, YUAN C Z, et al. An improved sparrow search algorithm for mobile robot path planning[C]//2022 41st Chinese Control Conference (CCC). Hefei, China, 2022: 1899-1903.
[15]PARQUE V, MIYASHITA T. Smooth curve fitting of mobile robot trajectories using differential evolution[J]. IEEE Access, 2855, 8: 82855-82866.
[16]CHEN J, LIANG J, TONG Y. Path planning of mobile robot based on improved differential evolution algorithm[C]//2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV). Shenzhen, China, 2020: 811-816.
[17]MARINI F, WALCZAK B. Particle swarm optimization (PSO). A tutorial[J]. Chemometrics and Intelligent Laboratory Systems, 2015, 149: 153-165.
[18]Bilal, PANT M, ZAHEER H, et al. Differential Evolution: A review of more than two decades of research[J]. Engineering Applications of Artificial Intelligence, 2020, 90: 103479.
[19]ABUALIGAH L. Multi-verse optimizer algorithm: A comprehensive survey of its results, variants, and applications[J]. Neural Computing and Applications, 2020, 32(16): 12381-12401.
[20]SUN Z, WANG N, BI Y R, et al. Parameter identification of PEMFC model based on hybrid adaptive differential evolution algorithm[J]. Energy, 2015, 90: 1334-1341.
[21]NADIMI-SHAHRAKI M H, TAGHIAN S, MIRJALILI S, et al. MTDE: An effective multi-trial vector-based differential evolution algorithm and its applications for engineering design problems[J]. Applied Soft Computing, 2020, 97: 106761.

Memo

Memo:
Received 2024-05-20,Revised 2024-10-12.
Biographies:Sun Zhe(1982—), male, doctor, associate professor,zhesunny@njupt.edu.cn; Sun Zhixin(corresponding author), male, doctor, professor, sunzx@njupt.edu.cn.
Foundation items:The National Natural Science Foundation of China (No.62272239, 62303214); Jiangsu Agricultural Science and Technology Independent Innovation Fund(No.SJ222051).
Citation:SUN Zhe,CHENG Jiajia,BI Yunrui,et al.Robot path planning based on a two-stage DE algorithm and applications[J].Journal of Southeast University (English Edition),2025,41(2):244-251.DOI:10.3969/j.issn.1003-7985.2025.02.014.DOI:10.3969/j.issn.1003-7985.2025.02.014
Last Update: 2025-06-20