Differentiable Optimization Based Time-Varying Control Barrier Functions for Dynamic Obstacle Avoidance

TL;DR

This paper extends differentiable optimization-based control barrier functions to dynamic obstacle avoidance, incorporating time-varying CBFs with noise and actuation considerations, validated through simulations and robotic experiments.

Contribution

It introduces a novel TVCBF framework for dynamic obstacles, extending previous static obstacle methods with noise and actuation limits, and demonstrates its effectiveness in simulation and real robot tests.

Findings

Outperforms model predictive control in dynamic obstacle avoidance tasks.

Successfully applies TVCBF to a 7-DOF robotic arm in experiments.

Handles measurement noise and actuation limits effectively.

Abstract

Control barrier functions (CBFs) provide a simple yet effective way for safe control synthesis. Recently, work has been done using differentiable optimization (diffOpt) based methods to systematically construct CBFs for static obstacle avoidance tasks between geometric shapes. In this work, we extend the application of diffOpt CBFs to perform dynamic obstacle avoidance tasks. We show that by using the time-varying CBF (TVCBF) formulation, we can perform obstacle avoidance for dynamic geometric obstacles. Additionally, we show how to extend the TVCBF constraint to consider measurement noise and actuation limits. To demonstrate the efficacy of our proposed approach, we first compare its performance with a model predictive control based method and a circular CBF based method on a simulated dynamic obstacle avoidance task. Then, we demonstrate the performance of our proposed approach in experimental studies using a 7-degree-of-freedom Franka Research 3 robotic manipulator.