让机器人以有体积的方式更高效地覆盖空间，提升任务完成率。
Volumetric Ergodic Control
- 用体积化状态表示机器人，模拟真实物理交互。
- 覆盖效率提升两倍以上，任务完成率保持100%。
- 适用于多种机器人结构与传感器，适合实时控制场景。
经典遍历控制针对非线性系统设计最优空间覆盖行为，但将机器人视为无体积的点，忽略了其实际的物理体积和传感范围。本文提出一种新的体积化遍历控制方法，采用体积化状态表示以优化空间覆盖。该方法保持了原遍历控制的渐近覆盖保证，计算开销极小，支持任意基于样本的体积模型。我们在搜索与操作任务中测试了该方法，涵盖多种机器人动力学、末端执行器几何形状及传感器模型，结果表明其覆盖效率提升超过两倍，所有实验任务完成率均为100%，显著优于标准遍历控制。最后，我们在机械擦除任务中验证了该方法在实际机器人臂上的有效性。
原文摘要 · Abstract (English)
Ergodic control synthesizes optimal coverage behaviors over spatial distributions for nonlinear systems. However, existing formulations model the robot as a non-volumetric point, whereas in practice a robot interacts with the environment through its body and sensors with physical volume. In this work, we introduce a new ergodic control formulation that optimizes spatial coverage using a volumetric state representation. Our method preserves the asymptotic coverage guarantees of ergodic control, adds minimal computational overhead for real-time control, and supports arbitrary sample-based volumetric models. We evaluate our method across search and manipulation tasks -- with multiple robot dynamics and end-effector geometries or sensor models -- and show that it improves coverage efficiency by more than a factor of two while maintaining a 100% task completion rate across all experiments, outperforming the standard ergodic control method. Finally, we demonstrate the effectiveness of our method on a robot arm performing mechanical erasing tasks. Project website: https://murpheylab.github.io/vec/
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。