Collision Avoidance for Convex Primitives via Differentiable Optimization Based High-Order Control Barrier Functions
This work introduces a novel framework for collision avoidance among convex shapes by transforming difficult nonconvex safety constraints into linear constraints using high-order control barrier functions (HOCBFs). By representing obstacles as scaling functions and proving high-order continuous differentiability, the method enables efficient collision avoidance for both velocity and torque-controlled systems.