Collision Avoidance for Convex Primitives via Differentiable Optimization Based High-Order Control Barrier Functions

TL;DR

This paper introduces a high-order control barrier function framework using differentiable optimization to improve collision avoidance among convex primitives, addressing challenges like torque control and spurious equilibria, validated on a robotic manipulator.

Contribution

It presents a novel high-order CBF approach with differentiable optimization for convex primitives, enabling torque control and preventing undesired equilibria.

Findings

Successful collision avoidance demonstrated on a robotic manipulator

Effective prevention of spurious equilibria with circulation mechanism

Framework accommodates high-dynamics and force-based tasks

Abstract

Ensuring the safety of dynamical systems is crucial, where collision avoidance is a primary concern. Recently, control barrier functions (CBFs) have emerged as an effective method to integrate safety constraints into control synthesis through optimization techniques. However, challenges persist when dealing with convex primitives and tasks requiring torque control, as well as the occurrence of unintended equilibria. This work addresses these challenges by introducing a high-order CBF (HOCBF) framework for collision avoidance among convex primitives. We transform nonconvex safety constraints into linear constraints by differentiable optimization and prove the high-order continuous differentiability. Then, we employ HOCBFs to accommodate torque control, enabling tasks involving forces or high dynamics. Additionally, we analyze the issue of spurious equilibria in high-order cases and propose a circulation mechanism to prevent the undesired equilibria on the boundary of the safe set. Finally, we validate our framework with three experiments on the Franka Research 3 robotic manipulator, demonstrating successful collision avoidance and the efficacy of the circulation mechanism.