[1]姚利娜,李金龙.基于改进RRT-Connect算法的无人车路径规划[J].郑州大学学报(工学版),2027,48(XX):1-8.[doi:10. 13705 / j. issn. 1671-6833. 2026. 02. 016]
　YAO Lina,LI Jinlong.Path Planning for Unmanned Vehicles Based on Improved RRT-Connect Algorithm[J].Journal of Zhengzhou University (Engineering Science),2027,48(XX):1-8.[doi:10. 13705 / j. issn. 1671-6833. 2026. 02. 016]
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基于改进RRT-Connect算法的无人车路径规划()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
48
期数:
2027年XX
页码:
1-8
栏目:
出版日期:
2027-12-10

文章信息/Info

Title:
Path Planning for Unmanned Vehicles Based on Improved RRT-Connect Algorithm
作者:
姚利娜, 李金龙
郑州大学 电气与信息工程学院,河南 郑州 450001
Author(s):
YAO Lina, LI Jinlong
School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
关键词:
无人车;  双向快速扩展随机树算法;  目标动态概率采样;  人工势场;  轨迹质量评估函数
Keywords:
unmanned vehicle;  rapidly-exploring random tree connect algorithm;  goal-guided dynamic probability sampling;  artificial potential field;  trajectory quality evaluation function
分类号:
TH112TP242. 6
DOI:
10. 13705 / j. issn. 1671-6833. 2026. 02. 016
文献标志码:
A
摘要:
针对传统双向快速扩展随机树算法搜索盲目、节点冗余和路径不平滑等问题,对其在目标采样、节点扩展以及轨迹优化等方面进行了改进。 首先,引入目标动态概率采样策略,根据当前随机树的扩展状态与目标点的位置,动态调整目标采样点的采样概率,对生成的随机点进行筛选,从而提高采样效率,加快算法收敛速度;其次,在节点扩展过程加入基于出逃力的改进人工势场分量,在避免陷入局部最优的同时提高无人车的目标搜索能力和节点扩展效率;最后,构建轨迹质量评估函数,分别对无人车在不同时刻下生成轨迹的安全程度、偏移程度以及平滑性进行代价评估并选取代价函数值最小的轨迹来引导无人车行驶。 将所提改进算法与传统双向快速扩展随机树算法在不同测试环境下进行仿真,仿真结果表明,相比传统算法,所提算法在简单障碍物环境下规划出来的平均路径长度缩短了 9. 83%,平均规划时间缩短了 85. 40%,在狭窄通道环境下,所提算法规划出来的平均路径长度和平均规划时间缩短了 10. 56%和 64. 63%,在 U 形障碍物环境下本文算法规划出来的平均路径长度和平均规划时间缩短了 22. 82% 和 66. 92%。 此外,所提算法在复杂环境下的规划成功率得到了显著提升,更适用于无人车的路径规划。
Abstract:
To address the issues of blind searching, redundant nodes, and non-smooth paths inherent in the traditional Rapidly-exploring Random Tree Connect (RRT-Connect) algorithm, a series of improvements have been proposed in goal sampling, node expansion and trajectory optimization. Firstly, a goal-guided dynamic probability sampling strategy is 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 is incorporated into the node expansion process. This helps 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 is 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 is then selected to guide the vehicle’s motion. The enhanced algorithm is simulated and compared with the traditional RRT-Connect algorithm under different testing environments. The simulation results show that, compared to the traditional algorithm, the proposed algorithm reduces 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 are reduced by 10.56% and 64.63%, respectively. In U-shaped obstacle environments, the average path length and planning time are reduced by 22.82% and 66.92%, respectively. Furthermore, the proposed algorithm significantly improves the path planning success rate in complex environments, making it more suitable for autonomous vehicle path planning.

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备注/Memo

备注/Memo:
收稿日期:2026-02-03;修订日期:2026-04-14
基金项目:国家自然科学基金资助项目(61973278) ;河南省杰出青年基金资助项目(222300420019)
作者简介:姚利娜(1977— ) ,女,河南开封人,郑州大学教授,博士,博士生导师,主要从事故障诊断与容错控制研究,Email:michelle_lnxq@ 126. com。
更新日期/Last Update: 2026-05-26