Guarding Force: Safety-Critical Compliant Control for Robot-Environment Interaction

TL;DR

The paper addresses enforcing explicit, strict interaction force constraints for robot manipulators during contact with unknown environments. It introduces Safety-Critical Compliant Control (SC3) by embedding a Force-Constrained Control Barrier Function (FC-CBF) into the kinematic (and later dynamic) control loop, aided by a tracking differentiator to bound environment-model mismatch with Lyapunov analysis. The SC3 controller is realized as a quadratic program that minimally modifies a nominal compliant controller while guaranteeing the force constraint, with stability guarantees and forward invariance of the safe set; the approach is extended from kinematics to dynamics. Experimental validation on a UR3e robot across elastic, viscoelastic, and hybrid environments demonstrates reliable enforcement of strict force constraints where conventional admittance control may fail, highlighting strong practical impact for safe, adaptive human-robot collaboration in unknown environments.

Abstract

In this study, we propose a safety-critical compliant control strategy designed to strictly enforce interaction force constraints during the physical interaction of robots with unknown environments. The interaction force constraint is interpreted as a new force-constrained control barrier function (FC-CBF) by exploiting the generalized contact model and the prior information of the environment, i.e., the prior stiffness and rest position, for robot kinematics. The difference between the real environment and the generalized contact model is approximated by constructing a tracking differentiator, and its estimation error is quantified based on Lyapunov theory. By interpreting strict interaction safety specification as a dynamic constraint, restricting the desired joint angular rates in kinematics, the proposed approach modifies nominal compliant controllers using quadratic programming, ensuring adherence to interaction force constraints in unknown environments. The strict force constraint and the stability of the closed-loop system are rigorously analyzed. Experimental tests using a UR3e industrial robot with different environments verify the effectiveness of the proposed method in achieving the force constraints in unknown environments.

Figures (9)

• Figure 1: Illustrations of the notion of CBF and a typical physical interaction task: (a) Under the control action generated by (\ref{['cbf_based']}), the trajectory $\boldsymbol{x}(t)\in C,\;\forall t\geq 0$. Moreover, any $\boldsymbol{x}\in\partial C$, the controller (\ref{['cbf_based']}) will make $\boldsymbol{x}(t)$ move forward the ${\rm{Int}} C$; (b) The illustrative robot-environment interaction task with spring and damper environment.
• Figure 2: Schematic block of the proposed safety-critical compliant control strategy.
• Figure 3: The setup of the experimental test on a UR3e robot: (a) the elastic environment (spring); (b) the viscoelastic environment (sponge); (c) the hybrid characteristic environment (a combination of sponge and spring in series).
• Figure 4: Experiment results for interaction task with the elastic environment: (a) The snap of UR3e pressing the spring under the SC$^3$ control. The yellow line indicates the initial position of the end-effector base, the red and white lines are the deepest position reached with SC$^3$ control and admittance control, respectively; (b) Curves of interaction forces; (c) Curves of desired joint velocities under SC$^3$.
• Figure 5: Experiment results for interaction task with the viscoelastic environment: (a) The snap of UR3e pressing the sponge under the SC$^3$ control. The yellow line indicates the initial position of the end-effector base, the red and white lines are the deepest position reached with SC$^3$ control and admittance control, respectively; (b) Curves of interaction forces; (c) Curves of desired joint velocities under SC$^3$.

Theorems & Definitions (4)

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