从多模态路径到可执行轨迹：面向四轮独立转向（4WIS）机器人的轨迹规划框架

中文摘要 AI 辅助

四轮独立转向（4WIS）移动机器人支持多种运动模式，在狭窄复杂环境中具备高机动性，但现有规划方法往往无法充分利用这些能力，导致轨迹质量欠佳。为解决该问题，本文提出一种多模态全局轨迹规划框架，将模式增强的前端搜索与模式一致的分段轨迹优化相结合。在前端阶段，将Hybrid A*扩展至包含运动模式的四维状态空间，同时设计模式切换感知的代价函数与启发式函数，将模式决策嵌入全局搜索过程；进一步设计多模态Reeds-Shepp曲线与智能终端连接策略，提升搜索效率。在后端阶段，开发基于改进迭代安全走廊方案的分段轨迹优化框架，将离散多模态路径转换为平滑、运动学可行且模式过渡平稳的轨迹。实验结果表明，所提方法在安全性、到达时间、终端精度与计算时间上均实现最优综合性能；在实体4WIS机器人上开展的实际实验，进一步验证了生成轨迹的实际有效性与可执行性，为多模态移动机器人轨迹规划提供了灵活且高性能的解决方案。

英文摘要

Four-wheel independent steering (4WIS) mobile robots support multiple motion modes, offering high maneuverability in narrow and complex environments. However, existing planning methods often fail to fully exploit these capabilities, leading to suboptimal trajectory quality. To address this limitation, this paper proposes a multi-modal global trajectory planning framework that couples mode-augmented front-end search with mode-consistent segment-wise trajectory optimization. In the front-end stage, Hybrid A* is extended to a four-dimensional state space incorporating motion modes, while mode-switching-aware cost and heuristic functions embed mode decisions into the global search process. Multi-modal Reeds-Shepp curves and an intelligent terminal connection strategy are further designed to improve search efficiency. In the back-end stage, a segment-wise trajectory optimization framework based on an improved iterative safe corridor scheme is developed to convert discrete multi-modal paths into smooth, kinematically feasible trajectories with stationary mode transitions. Experimental results show that the proposed method achieves the best overall performance in safety, arrival time, terminal accuracy and computation time. Real-world experiments on a physical 4WIS robot further validate the practical effectiveness and executability of the generated trajectories, providing a flexible and high-performance solution for multi-modal mobile robot trajectory planning.

发表机构

• The Chinese University of Hong Kong（香港中文大学）
• Beijing Institute of Technology（北京理工大学）
• Shenzhen Research Institute of Nankai University（南开大学深圳研究院）

机构由 AI 辅助整理，请以论文原文为准。