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Safe Dynamic Motion Generation in Configuration Space Using Differentiable Distance Fields

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arxiv 2412.16456 v1 pith:SDZUCYSX submitted 2024-12-21 cs.RO

Safe Dynamic Motion Generation in Configuration Space Using Differentiable Distance Fields

classification cs.RO
keywords constraintscontrolgenerationmotionrobotapproachcbfsdifferentiable
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Neural Configuration-Space Barriers for Manipulation Planning and Control

    cs.RO 2025-03 unverdicted novelty 6.0

    Neural CDF barriers enable efficient planning and robust safe control for manipulators in cluttered dynamic environments from point-cloud observations.

  2. Neural Configuration-Space Barriers for Manipulation Planning and Control

    cs.RO 2025-03 unverdicted novelty 5.0

    Neural CDF barriers enable efficient planning and distributionally robust safe control for manipulators in cluttered dynamic environments using only point-cloud observations.