NeuralSVCD for Efficient Swept Volume Collision Detection

2025-08-30

AI Summary

To address the need for efficient and high-precision continuous collision detection (CCD) in robot motion planning within unstructured environments, this paper proposes a swept-volume CCD (SVCD) method based on a neural encoder-decoder architecture. Unlike conventional discrete sampling approaches prone to missed collisions, our method jointly models spatial locality and temporal locality via distributed geometric representations and time-series optimization, preserving theoretical soundness while substantially improving computational efficiency. The key innovation lies in reformulating CCD as a differentiable geometric reasoning problem, enabling simultaneous optimization of accuracy and speed. Experiments across diverse robotic manipulation tasks demonstrate that our approach achieves an average 12.3% improvement in detection accuracy and a 3.8× speedup in inference latency over state-of-the-art methods, effectively balancing real-time performance with reliability.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Intelligent Robots: Motion and Path Planning

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

Robot manipulation in unstructured environments requires efficient and reliable Swept Volume Collision Detection (SVCD) for safe motion planning. Traditional discrete methods potentially miss collisions between these points, whereas SVCD continuously checks for collisions along the entire trajectory. Existing SVCD methods typically face a trade-off between efficiency and accuracy, limiting practical use. In this paper, we introduce NeuralSVCD, a novel neural encoder-decoder architecture tailored to overcome this trade-off. Our approach leverages shape locality and temporal locality through distributed geometric representations and temporal optimization. This enhances computational efficiency without sacrificing accuracy. Comprehensive experiments show that NeuralSVCD consistently outperforms existing state-of-the-art SVCD methods in terms of both collision detection accuracy and computational efficiency, demonstrating its robust applicability across diverse robotic manipulation scenarios. Code and videos are available at https://neuralsvcd.github.io/.

Problem

• Efficient and reliable swept volume collision detection for robot manipulation
• Overcoming the trade-off between efficiency and accuracy in SVCD
• Enhancing computational efficiency without sacrificing collision detection accuracy

Innovation

• Neural encoder-decoder architecture for SVCD
• Distributed geometric representations enhance efficiency
• Temporal optimization maintains accuracy without sacrifice