Path Planning for Unmanned Vehicles Based on Improved RRT-Connect Algorithm
[1]YAO Lina,LI Jinlong.Path Planning for Unmanned Vehicles Based on Improved RRT-Connect Algorithm[J].Journal of Zhengzhou University (Engineering Science),2026,47(5):1-8.[doi:10.13705/j.issn.1671-6833.2026.02.016]
Copy
Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
47
Number of periods:
2026 Issue 5
Page number:
1-8
Column:
Public date:
2026-09-09
- Title:
- Path Planning for Unmanned Vehicles Based on Improved RRT-Connect Algorithm
- Keywords:
- unmanned vehicle; rapidly-exploring random tree connect algorithm; goal-guided dynamic probability sampling; artificial potential field; trajectory quality evaluation function
- CLC:
- TH112TP242. 6
- Abstract:
- To address the issues of blind searching, redundant nodes, and non‑smooth paths inherent in the traditional rapidly‑exploring random tree connect algorithm for unmanned vehicles, a series of improvements were proposed in goal sampling, node expansion and trajectory optimization. Firstly, a goal‑guided dynamic probability sampling strategy was introduced to filter the randomly selected points, thereby improving sampling efficiency and accelerating convergence. Next, an improved artificial potential field component based on the escape force was incorporated into the node expansion process to help the unmanned vehicle avoid getting trapped in local minima while enhancing its target‑searching capability and node expansion efficiency. Finally, a trajectory quality evaluation function was constructed to assess the safety, deviation, and smoothness of the trajectories generated by the unmanned vehicle at different time steps. The trajectory with the minimum cost value was then selected to guide the vehicle’s motion. The enhanced algorithm was simulated and compared with the traditional RRT‑Connect algorithm in different testing environments. The simulation results showed that, compared to the traditional algorithm, the proposed algorithm could reduce the average path length by 9.83% and the average planning time by 85.40% in simple obstacle environments. In narrow passage environments, the average path length and planning time could be reduced by 10.56% and 64.63%, respectively. In U‑shaped obstacle environments, the average path length and planning time could be reduced by 22.82% and 66.92%, respectively. Furthermore, the proposed algorithm significantly improved the path planning success rate in complex environments, making it more suitable for autonomous vehicle path planning.
- References:
-
[1] Luo Zhongkai, Zhang Libo. Learning path planning methods[J]. Journal of University of Chinese Academy of Sciences, 2024, 41(1): 11‑27.［罗中凯，张立波. 学习路径规划方法［J］. 中国科学院大学学报（中英文），2024，41（1）：11‑27.］
[2] Liu Yaqiu, Zhao Hanchen, Liu Xun, et al. An improved RRT based obstacle avoidance path planning algorithm for industrial robot[J]. Information and Control, 2021, 50(2): 235‑246.［刘亚秋，赵汉琛，刘勋，等. 一种基于改进的快速扩展随机树的工业机器人路径避障规划算法［J］. 信息与控制，2021，50（2）：235‑246.］
[3] Song Jiangyi, Li Dan, Chen Wenbo. Local path planning of indoor mobile robot based on Dijkstra and PID algorithm[J]. Journal of Anhui University of Technology (Natural Science), 2023, 40(1): 59‑64.［宋江一，李丹，陈文博. 融合Dijkstra和PID算法的室内移动机器人局部路径规划［J］. 安徽工业大学学报（自然科学版），2023，40（1）：59‑64.］
[4] Feng Laichun, Liang Huawei, Du Mingbo, et al. Guiding‑area RRT path planning algorithm based on A* for intelligent vehicle[J]. Computer Systems & Applications, 2017, 26(8): 127‑133.［冯来春，梁华为，杜明博，等. 基于A*引导域的RRT智能车辆路径规划算法［J］. 计算机系统应用，2017，26（8）：127‑133.］
[5] Li Yansheng, Wan Yong, Zhang Yi, et al. Path planning for warehouse robot based on the artificial bee colony‑adaptive genetic algorithm[J]. Chinese Journal of Scientific Instrument, 2022, 43(4): 282‑290.［李艳生，万勇，张毅，等. 基于人工蜂群‑自适应遗传算法的仓储机器人路径规划［J］. 仪器仪表学报，2022，43（4）：282‑290.］
[6] Liang Kai, Mao Jianlin. Path planning of indoor mobile robot based on improved ant colony algorithm[J]. Electronic Measurement Technology, 2019, 42(11): 65‑69.［梁凯，毛剑琳. 基于改进蚁群算法的室内移动机器人路径规划［J］. 电子测量技术，2019，42（11）：65‑69.］
[7] Lucas S, Portillo E. Application of the optimised pulse width modulation (PWM) based encoding‑decoding algorithm for forecasting with spiking neural networks (SNNs)[J]. Engineering Proceedings, 2024, 68(1): 41.
[8] Yin Feng, Xie Qingsong. Research on mobile robot path planning based on improved RRT* algorithm[J]. Journal of Xiangtan University (Natural Science Edition), 2022, 44(4): 22‑31.［印峰，谢青松. 基于改进RRT*算法的移动机器人路径规划研究［J］. 湘潭大学学报（自然科学版），2022，44（4）：22‑31.］
[9] Becerra I, Suomalainen M, Lozano E, et al. Human perception‑optimized planning for comfortable VR‑based telepresence[J]. IEEE Robotics and Automation Letters, 2020, 5(4): 6489‑6496.
[10] Kuffner J J, Lavalle S M. RRT‑Connect: an efficient approach to single‑query path planning[C]//IEEE International Conference on Robotics and Automation. Piscataway: IEEE, 2000: 995‑1001.
[11] Wang Kun, Huang Bo, Zeng Guohui, et al. Faster path planning based on improved RRT‑Connect algorithm[J]. Journal of Wuhan University (Natural Science Edition), 2019, 65(3): 283‑289.［王坤，黄勃，曾国辉，等. 基于改进RRT‑Connect的快速路径规划算法［J］. 武汉大学学报（理学版），2019，65（3）：283‑289.］
[12] Ge Chao, Zhang Xinyuan, Wang Hong, et al. Path planning based on improved RRT‑Connect algorithm[J]. Electronics Optics & Control, 2025, 32(3): 21‑26.［葛超，张鑫源，王红，等. 一种改进RRT‑Connect算法的路径规划研究［J］. 电光与控制，2025，32（3）：21‑26.］
[13] Chen Zhilan, Tang Haoyang. Research on robot path planning based on improved RRT‑Connect algorithm[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(2): 396‑405.［陈志澜，唐昊阳. 改进RRT‑Connect算法的机器人路径规划研究［J］. 计算机科学与探索，2025，19（2）：396‑405.］
[14] Jordan M, Perez A. Optimal bidirectional rapidly‑exploring random trees: MIT‑CSAIL‑TR‑2013‑021[R]. Cambridge: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 2013.
[15] Liu Jianyu, Fan Pingqing. Path planning of manipulator based on improved RRT*‑Connect algorithm[J]. Computer Engineering and Applications, 2021, 57(6): 274‑278.［刘建宇，范平清. 基于改进的RRT*‑Connect算法机械臂路径规划［J］. 计算机工程与应用，2021，57（6）：274‑278.］
[16] Han Shaolong, Liu Wenqi, Liu Yuanshang, et al. Research on path planning of manipulator based on the improved RRT‑Connect algorithm[J]. IEEE Access, 2025, 13: 135165‑135178.
[17] Zhou Hengxu, Cheng Yong, Liu Weicai. Manipulator motion planning based on Dubins‑Informed RRT* Algorithm[J]. Techniques of Automation and Applications, 2020, 39(10): 67‑74.［周恒旭，程勇，刘伟才. Dubins‑Informed RRT*算法规划的机械臂运动［J］. 自动化技术与应用，2020，39（10）：67‑74.］
[18] Suwoyo H, Hastomi Y, Andika J. A Bidirectional‑RRT*‑Connect‑Assisted RRT*‑Smart for a path planning algorithm[J]. Sinergi, 2025, 29(2): 473.
[19] Zheng Zhuan, Xie Shuangjian, Ye Zimo, et al. Research on path smoothing optimisation based on improved RRT‑Connect algorithm and third‑order Bezier curve[J]. Journal of Mechanical Engineering Science, 2025, 239(7): 2544‑2561.
[20] Chen Jiangyi, Yin Xiaoyong, Wang Tingting, et al. Local path planning of artificial potential field based on improved repulsive model[J]. Journal of Zhengzhou University (Engineering Science), 2023, 44(3): 83‑87.［陈江义，殷笑勇，王婷婷，等. 基于改进斥力模型的人工势场局部路径规划［J］. 郑州大学学报（工学版），2023，44（3）：83‑87.］
- Similar References:
Memo
-
Last Update:
2026-09-03
