cHyRRT and cHySST: Two Motion Planning Tools for Hybrid Dynamical Systems

TL;DR

This paper introduces two motion planning tools, cHyRRT and cHySST, for hybrid dynamical systems, demonstrating their effectiveness through simulations and ensuring compatibility with ROS for practical applications.

Contribution

The paper provides the first practical implementations of HyRRT and HySST algorithms, integrating them with OMPL and ROS for efficient hybrid system motion planning.

Findings

cHyRRT guarantees probabilistic completeness in hybrid systems.

cHySST finds near-optimal trajectories based on user-defined costs.

Tools successfully applied to a pinball game and tensegrity multicopter simulations.

Abstract

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.