Safe Dynamic Motion Generation in Configuration Space Using Differentiable Distance Fields

TL;DR

This paper presents a novel method for safe, real-time motion generation in high-dimensional robots by incorporating velocity-aware distance fields into control barrier functions, improving safety and performance.

Contribution

It introduces time-varying control barrier functions based on differentiable distance fields that include velocity information, enhancing safety and success rates in dynamic environments.

Findings

Effective in simulation and real-world tests on a 7-axis robot.

Outperforms existing CBF-based methods in safety and efficiency.

Enables real-time, whole-body contact motion planning.

Abstract

Generating collision-free motions in dynamic environments is a challenging problem for high-dimensional robotics, particularly under real-time constraints. Control Barrier Functions (CBFs), widely utilized in safety-critical control, have shown significant potential for motion generation. However, for high-dimensional robot manipulators, existing QP formulations and CBF-based methods rely on positional information, overlooking higher-order derivatives such as velocities. This limitation may lead to reduced success rates, decreased performance, and inadequate safety constraints. To address this, we construct time-varying CBFs (TVCBFs) that consider velocity conditions for obstacles. Our approach leverages recent developments on distance fields for articulated manipulators, a differentiable representation that enables the mapping of objects' position and velocity into the robot's joint space, offering a comprehensive understanding of the system's interactions. This allows the manipulator to be treated as a point-mass system thus simplifying motion generation tasks. Additionally, we introduce a time-varying control Lyapunov function (TVCLF) to enable whole-body contact motions. Our approach integrates the TVCBF, TVCLF, and manipulator physical constraints within a unified QP framework. We validate our method through simulations and comparisons with state-of-the-art approaches, demonstrating its effectiveness on a 7-axis Franka robot in real-world experiments.