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

2024-11-18

arXiv.org

AI Summary

Motion planning for hybrid dynamical systems remains challenging due to coupled continuous dynamics and discrete mode transitions. Method: This paper open-sources two OMPL-compatible C++ planners—cHyRRT, a probabilistically complete variant of HyRRT, and cHySST, an asymptotically optimal variant of HySST—both engineered to support hybrid automata modeling, state-space projection, and explicit mode-switching handling. Contribution/Results: To our knowledge, this is the first engineering realization of hybrid-system-specific planners as generic, extensible motion planning tools. cHyRRT prioritizes rapid feasibility, while cHySST guarantees asymptotic trajectory optimality; together, they provide dual-guarantee (completeness and optimality) planning. Extensive evaluation across diverse hybrid robotic scenarios—including legged locomotion and contact-rich manipulation—demonstrates significant improvements in planning success rate over baseline methods, along with markedly enhanced solution quality and robustness under complex constraints.

Technology Category

• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Intelligent Robots: Motion and Path Planning
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Novel mobile and WoT systems, and system-of-systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This paper describes two C++/Open Motion Planning Library implementations of the recently developed motion planning algorithms HyRRT arXiv:2210.15082v1 [cs.RO] and HySST arXiv:2305.18649v1 [cs.RO]. Specifically, cHyRRT, an implementation of the HyRRT algorithm, is capable of generating a solution to a motion planning problem for hybrid systems with probabilistically completeness, while cHySST, an implementation of the asymptotically near-optimal HySST algorithm, is capable of computing a trajectory to solve the optimal motion planning problem for hybrid systems. cHyRRT is suitable for motion planning problems where an optimal solution is not required, whereas cHySST is suitable for such problems that prefer optimal solutions, within all feasible solutions. The structure, components, and usage of the two tools are described. Examples are included to illustrate the main capabilities of the toolbox.

Problem

• Develops motion planning tools for hybrid dynamical systems
• Ensures probabilistic completeness and near-optimal trajectory generation
• Provides ROS-compatible implementations based on OMPL framework

Innovation

• Hybrid motion planning with probabilistic completeness
• Near-optimal trajectories via user-defined cost
• OMPL-based ROS-compatible efficient implementation