为四轮独立转向机器人设计多模式路径规划，提升狭窄环境下的机动性与轨迹质量。
From Multi-Modal Paths to Executable Trajectories: A Trajectory Planning Framework for 4WIS Robots
- 将运动模式融入搜索空间，用改进的Hybrid A*实现多模式全局路径探索
- 生成平滑可执行轨迹，实测在安全、到达时间、精度和计算效率上均最优
- 适合高机动性机器人在复杂狭小场景中的实时路径规划应用
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。然而，现有规划方法常无法充分利用其能力，导致轨迹质量不佳。为此，本文提出一种多模式全局路径规划框架，结合模式增强的前端搜索与模式一致的分段轨迹优化。前端采用扩展至四维状态空间的Hybrid A*算法，引入考虑模式切换的代价与启发函数，将模式决策嵌入全局搜索过程；同时设计多模式Reeds-Shepp曲线与智能终端连接策略，提升搜索效率。后端基于改进的迭代安全走廊方案，构建分段轨迹优化框架，将离散多模式路径转化为平滑、满足运动学约束且模式转换静止的可执行轨迹。实验结果表明，该方法在安全性、到达时间、终端精度和计算时间上表现最优。真实4WIS机器人平台上的实测进一步验证了生成轨迹的实际有效性与可执行性，为多模式移动机器人路径规划提供了一种灵活高效的解决方案。
原文摘要 · Abstract (English)
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.
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