用函数约束设计安全动态可行的运动规划，让机器人避障且可执行。
Safe and Dynamically-Feasible Motion Planning using Control Lyapunov and Barrier Functions
- 结合RRT与控制李雅普诺夫/屏障函数生成安全路径
- 对线性系统在多面体和椭球约束下计算高效且概率完备
- 适合需要实时避障与动态可行性保障的机器人系统
本文研究控制仿射系统的运动规划问题，旨在生成从起点到终点的无碰撞路径，并可通过安全且动态可行的控制器执行。提出C-CLF-CBF-RRT算法，融合快速探索随机树（RRT）、控制李雅普诺夫函数（CLFs）和控制屏障函数（CBFs），确保路径的安全性与可执行性。证明该算法在线性系统面对多面体与椭球约束时具有计算效率，并具备概率完备性。通过多种仿真与硬件实验验证了其性能。
原文摘要 · Abstract (English)
This paper considers the problem of designing motion planning algorithms for control-affine systems that generate collision-free paths from an initial to a final destination and can be executed using safe and dynamically-feasible controllers. We introduce the C-CLF-CBF-RRT algorithm, which produces paths with such properties and leverages rapidly exploring random trees (RRTs), control Lyapunov functions (CLFs) and control barrier functions (CBFs). We show that C-CLF-CBF-RRT is computationally efficient for linear systems with polytopic and ellipsoidal constraints, and establish its probabilistic completeness. We showcase the performance of C-CLF-CBF-RRT in different simulation and hardware experiments.
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