用神经网络提升机器人运动碰撞检测的效率与精度
NeuralSVCD for Efficient Swept Volume Collision Detection
- 设计神经编码解码架构，利用几何与时间局部性优化计算
- 在多个场景中实现更高准确率和更快检测速度
- 适合需要实时安全规划的复杂机器人操作任务
非结构化环境中机器人操作需高效可靠的扫掠体积碰撞检测（SVCD）以保障运动规划安全。传统离散方法可能漏检点间碰撞，而SVCD能连续检测整个轨迹上的碰撞。现有方法常在效率与精度间权衡，限制实际应用。本文提出NeuralSVCD，一种针对该问题的新型神经编码解码架构，通过分布式几何表示和时间优化，利用形状与时间局部性，在不损失精度的前提下显著提升计算效率。大量实验表明，NeuralSVCD在碰撞检测准确率与计算效率上均持续优于现有最先进方法，展现出在多样化机器人操作场景中的鲁棒适用性。代码与视频见 https://neuralsvcd.github.io/。
原文摘要 · Abstract (English)
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/.
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