复杂环境下的无人车时空联合轨迹规划

doi:10.11896/jsjkx.250300043

计算机科学 ›› 2026, Vol. 53 ›› Issue (5): 328-336.doi: 10.11896/jsjkx.250300043

复杂环境下的无人车时空联合轨迹规划

郑雅羽^1,2, 饶品阳¹, 穆建彬¹, 朱威^1,2

1 浙江工业大学信息工程学院杭州 310023
2 浙江省嵌入式系统联合重点实验室杭州 310023

收稿日期:2025-03-10 修回日期:2025-06-27 发布日期:2026-05-08
通讯作者: 朱威(weizhu@zjut.edu.cn)
作者简介:(yayuzheng@zjut.edu.cn)
基金资助:
国家自然科学基金联合重点项目(U24A20270);浙江省自然科学基金(LQ24F030023)

Spatio-Temporal Trajectory Planning for Unmanned Vehicles in Complex Environments

ZHENG Yayu^1,2, RAO Pinyang¹, MU Jianbin¹, ZHU Wei^1,2

1 College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
2 Zhejiang Provincial Joint Key Laboratory of Embedded Systems, Hangzhou 310023, China

Received:2025-03-10 Revised:2025-06-27 Online:2026-05-08
About author:ZHENG Yayu,born in 1978,Ph.D,associate professor.His main research in-terests include video processing,embedded applications and intelligent robot.
ZHU Wei,born in 1982,Ph.D,associate professor.His main research interests include video processing,machine lear-ning and intelligent robot.
Supported by:
Joint Key Program of the National Natural Science Foundation of China(U24A20270) and Natural Science Foundation of Zhejiang Province(LQ24F030023).

摘要/Abstract

摘要： 针对复杂环境下阿克曼转向无人车轨迹规划处理环境约束和运动学约束低效的问题,提出一种前后端耦合的高效时空联合轨迹规划算法。在前端路径规划中,通过融合拓扑地图引导的滑动窗口剪枝策略和动态启发式函数优化方法来提升搜索效率。在后端轨迹优化中,先以安全通行走廊对偶表示环境约束,根据阿克曼模型无人车微分平坦特性,以无人车微分平坦变量及其高阶导表示运动学约束并作为优化变量;再使用光滑映射和微分同胚变换,解析式地将优化变量转换至无约束空间;最后在无约束空间进行梯度优化,以解决处理约束低效的问题。实验场景为实际停车场环境,结果表明,在路径搜索质量无显著下降的前提下,所提模型所用时间较现有方法减少47%,轨迹优化结果表明,所提算法能够实时地规划出高质量时空轨迹,验证了其有效性。

关键词: 轨迹规划, 时空联合, 微分平坦, 微分同胚变换, 凸优化

Abstract: To address the inefficiency of handling environmental constraints and kinematic constraints in trajectory planning for Ackermann-steering autonomous vehicles in complex environments,this paper proposes an efficient spatiotemporal joint trajectory planning algorithm with front-end and back-end coupling.In the front-end path planning,by integrating a topology map-guided sliding window pruning strategy with a dynamic heuristic function optimization method,the search efficiency is improved.For the back-end trajectory optimization,environmental constraints are first represented by dual safety corridors.Based on the differential flatness characteristics of the Ackermann model vehicle,kinematic constraints are expressed using the vehicle’s differentially flat variables and their higher-order derivatives as optimization variables.These variables are then analytically transformed into an unconstrained space via smooth mapping and diffeomorphic transformations.Finally,gradient optimization is performed in the unconstrained space to resolve constraint-handling inefficiencies.Experiments conducted in real-world parking environments demonstrate that the proposed method achieves 47% faster path search time compared to existing methods while maintaining comparable path quality.The trajectory optimization results show that the proposed algorithm can plan high-quality spatiotemporal trajectories in real time,verifying its effectiveness.

Key words: Trajectory planning, Spatio-temporal coupling, Differential flatness, Diffeomorphic transformation, Convex optimization

中图分类号:

TP391

郑雅羽, 饶品阳, 穆建彬, 朱威. 复杂环境下的无人车时空联合轨迹规划[J]. 计算机科学, 2026, 53(5): 328-336. https://doi.org/10.11896/jsjkx.250300043

ZHENG Yayu, RAO Pinyang, MU Jianbin, ZHU Wei. Spatio-Temporal Trajectory Planning for Unmanned Vehicles in Complex Environments[J]. Computer Science, 2026, 53(5): 328-336. https://doi.org/10.11896/jsjkx.250300043

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复杂环境下的无人车时空联合轨迹规划

Spatio-Temporal Trajectory Planning for Unmanned Vehicles in Complex Environments

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