Online Control Barrier Function Construction for Safety-Critical Motion Control of Manipulators

Designing safety-critical control for robotic manipulators is challenging, especially in a cluttered environment. This article proposes an online control barrier function (CBF) construction method, which extracts CBF from distance samples and enforces the safety of the motion control of robotic manipulators. Specifically, the CBF guarantees the controlled invariant property for considering the system dynamics. The proposed method samples the distance function and determines the safe set. Then, the CBF is synthesized based on the safe set by a scenario-based sum-of-square program. Unlike most existing linearization-based approaches, our method preserves the volume of the feasible space for planning without approximating the signed distance function, which helps find a solution in a cluttered environment. The control law is obtained by solving a real-time CBF-based quadratic program. Moreover, our method guarantees safety with the probabilistic result validated on a 7-DOF manipulator in real and virtual environments. The experiments show that the manipulator is able to execute tasks where the potential clearance between obstacles is in millimeters.