Robust Whole-Body Safety-Critical Control for Sampled-Data Robotic Manipulators via Control Barrier Functions

In this paper, a novel robust safety-critical control method is proposed to ensure whole-body safety for the robotic manipulator, which is implemented as a sampled-data system with measurement errors. The manipulator and obstacles are approximated as several spherical enclosures, and a whole-body safety constraint with relative degree two is formulated based on the distance function. Robust control barrier function (CBF) constraints are first designed to handle the predefined joint velocity constraints. Building upon this, a robust high-order CBF constraint is derived to enforce the whole-body safety constraint. Each stage of the derivation incorporates the sample-and-hold error and measurement error. These robust CBF constraints are then unified with a nominal controller to form an optimization problem, ensuring that the velocity constraints and the safety constraint are satisfied. The effectiveness of the proposed algorithm is demonstrated through simulations and experiments on a 7-degree-of-freedom (DOF) Franka Emika Panda robot.