用RRT与CBF结合实现机器人动态避障，兼顾安全与效率。
RRT-CBF Based Motion Planning
- 将RRT与模型预测控制结合，动态更新安全约束
- 在存在模型不确定性的条件下实现静态与动态障碍物避碰
- 首次应用于非线性机械臂系统，提升复杂场景规划能力
控制屏障函数（CBF）近年来被广泛用于非线性系统的安全约束保障。尽管已有研究将CBF融入路径规划算法以生成安全路径，但这些方法常伴随巨大计算开销或单向随机性，导致运行时间增加，且在满足安全约束时牺牲了搜索效率与空间覆盖。本文提出一种新型运动规划方法，结合快速探索随机树（RRT）与模型预测控制（MPC），通过动态更新约束实现轨迹的安全性保障，使机器人在考虑模型不确定性的情况下，可有效避开静态和动态圆形障碍物及其它移动机器人。此外，本文首次将CBF-RRT应用于机械臂这一非线性系统模型，实现了对复杂任务场景下高安全性轨迹的实时求解。
原文摘要 · Abstract (English)
Control barrier functions (CBF) are widely explored to enforce the safety-critical constraints on nonlinear systems recently. There are many researchers incorporating the control barrier functions into path planning algorithms to find a safe path, but these methods involve huge computational complexity or unidirectional randomness, resulting in arising of run-time. When safety constraints are satisfied, searching efficiency, and searching space are sacrificed. This paper combines the novel motion planning approach using rapid exploring random trees (RRT) algorithm with model predictive control (MPC) to enforce the CBF with dynamically updating constraints to get the safety-critical resolution of trajectory which will enable the robots not to collide with both static and dynamic circle obstacles as well as other moving robots while considering the model uncertainty in process. Besides, this paper first realizes application of CBF-RRT in robot arm model for nonlinear system.
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