RRT-CBF Based Motion Planning

TL;DR

This paper introduces a novel motion planning method combining RRT, MPC, and control barrier functions to ensure safe, collision-free trajectories for robots with dynamic obstacles and model uncertainties, including application to robot arms.

Contribution

It presents a new integrated approach using RRT, MPC, and CBF with dynamic constraints, applied to nonlinear systems and robot arms, reducing computational complexity and improving safety.

Findings

Successfully avoids static and dynamic obstacles

Enforces safety constraints with dynamic updates

Applicable to robot arm nonlinear systems

Abstract

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.