Flexible Active Safety Motion Control for Robotic Obstacle Avoidance: A CBF-Guided MPC Approach

TL;DR

This paper introduces a flexible active safety control method for robotic manipulators that uses control barrier functions and model predictive control to dynamically optimize safety margins during obstacle avoidance in real-time.

Contribution

It presents a novel CBF-guided MPC framework with dynamically optimized decay rates and safety margins, enhancing flexibility and safety in dynamic environments for robot manipulators.

Findings

Effective obstacle avoidance demonstrated on UR5 robot

Dynamic safety margins improve flexibility in safety control

Method outperforms traditional fixed-margin approaches

Abstract

A flexible active safety motion (FASM) control approach is proposed for the avoidance of dynamic obstacles and the reference tracking in robot manipulators. The distinctive feature of the proposed method lies in its utilization of control barrier functions (CBF) to design flexible CBF-guided safety criteria (CBFSC) with dynamically optimized decay rates, thereby offering flexibility and active safety for robot manipulators in dynamic environments. First, discrete-time CBFs are employed to formulate the novel flexible CBFSC with dynamic decay rates for robot manipulators. Following that, the model predictive control (MPC) philosophy is applied, integrating flexible CBFSC as safety constraints into the receding-horizon optimization problem. Significantly, the decay rates of the designed CBFSC are incorporated as decision variables in the optimization problem, facilitating the dynamic enhancement of flexibility during the obstacle avoidance process. In particular, a novel cost function that integrates a penalty term is designed to dynamically adjust the safety margins of the CBFSC. Finally, experiments are conducted in various scenarios using a Universal Robots 5 (UR5) manipulator to validate the effectiveness of the proposed approach.