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

TL;DR

This work addresses dynamic obstacle avoidance for robot manipulators by coupling a flexible CBF-guided safety criterion with an MPC framework. By introducing dynamic decay rates $\gamma_k$ in the discrete-time CBF constraints and embedding them as decision variables, the approach enables proactive avoidance when obstacles are distant and relaxes safety as proximity increases. A GPIO-based observer estimates obstacle dynamics and yields bounded estimation errors, which are folded into a refined safety distance $r_{\text{safe}}$, ensuring forward invariance of the safe set. Experimental validation on a UR5 demonstrates that the method achieves online planning, robust tracking, and constraint satisfaction with smaller horizons than conventional MPC, while enabling adjustable safety margins through $P_{\gamma}$ and dynamic $\gamma_k$. The results highlight the practical potential of FASM for real-time, safe operation in dynamic environments.

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.

Figures (11)

• Figure 1: The advantages of the CBFSC. (The next-step reachable set $\mathbb{R}_s := \{x_{k+1} \in \mathbb{Q} \ | \ x_{k+1} = f(x_{k},u_k), u_k \in U \}$.)
• Figure 2: Framework of the proposed FASM control.
• Figure 3: The experimental setup.
• Figure 4: Experiment 1: Frame-by-frame plots in the scenarios of fast-moving small and large obstacles (top: small obstacle; bottom: large obstacle). (Only the trajectory from point A to point B is shown.)
• Figure 5: Experiment 1: Position errors, quaternion errors, joint velocities and end-effector trajectories of the proposed FASM control in the scenarios of fast-moving small and large obstacles (top: small obstacle; bottom: large obstacle).