两款工具为混合系统规划路径，一个保证概率完整，一个逼近最优。
cHyRRT and cHySST: Two Motion Planning Tools for Hybrid Dynamical Systems
- 基于OMPL与ROS，实现两类混合系统运动规划算法
- cHyRRT有概率完整性保障，cHySST可优化用户定义代价函数
- 适用于机器人复杂动态环境下的路径规划，如弹球游戏和柔韧无人机
本文介绍了近期提出的运动规划算法HyRRT（arXiv:2210.15082）和HySST（arXiv:2305.18649）的两个实现：cHyRRT与cHySST。cHyRRT是HyRRT算法的实现，为具有混合动力系统的运动规划问题生成解，并具备概率完备性；cHySST是渐近近似最优的HySST算法实现，可根据用户定义的代价函数寻找近似最优轨迹。两个工具均基于混合系统理论构建，依托OMPL框架，兼容ROS系统，兼顾计算效率。文中详细说明了其结构、组件与使用方式，并通过修改后的弹球游戏和抗碰撞张拉整体多旋翼机实例，展示了其核心能力。
原文摘要 · Abstract (English)
This paper presents two implementations of the recently developed motion planning algorithms HyRRT arXiv:2210.1508(2) and HySST arXiv:2305.1864(9). Specifically, cHyRRT, an implementation of the HyRRT algorithm, generates solutions to motion planning problems for hybrid systems with a probabilistic completeness guarantee, while cHySST, an implementation of the asymptotically near-optimal HySST algorithm, finds near-optimal trajectories based on a user-defined cost function. The implementations align with the theoretical foundations of hybrid system theory and are designed based on OMPL, ensuring compatibility with ROS while prioritizing computational efficiency. The structure, components, and usage of both tools are detailed. A modified pinball game and collision-resilient tensegrity multicopter example are provided to illustrate the tools' key capabilities.
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