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. Note to Practitioners—This paper presents a robust control approach aimed at addressing whole-body safety for robotic manipulators, ensuring that all parts of the robot operate safely without violating safety constraints. The manipulator and obstacles are modeled as spherical enclosures, which simplifies the formulation of the whole-body safety constraint and enhances computational efficiency. Furthermore, by introducing robust CBFs, this work effectively addresses errors caused by sampling and measurement to maintain safe operation even under real-world conditions. The robust design reduces the reliance on specific parameters, thereby offering greater flexibility in handling diverse safety constraints and improving adaptability to various safety-critical situations. This approach is expected to significantly improve whole-body safety in practical deployment, especially in dynamic or unstructured environments.