基于检查排序的学习加速窄相位碰撞检测用于基于采样的运动规划

中文摘要 AI 辅助

碰撞检测对于确保规划路径的安全性至关重要。然而，它给运动规划器带来了不可忽视的计算负担，促使了关于碰撞检测加速的广泛研究。在常用的基于相位的碰撞检测方法中，宽相位采用层次结构快速丢弃明显无碰撞的对象对，而随后的窄相位对宽相位无法确定碰撞状态的剩余对象对进行详细的碰撞检查。尽管这些方法通过宽相位剪枝有效减少了详细检查的数量，但窄相位通常按照宽相位返回的默认顺序执行，对检查顺序几乎没有显式优化。这为进一步加速留下了空间，尤其是在杂乱环境中，许多对象对可能在宽相位后保留，窄相位可能占总检测时间的很大一部分。在这项工作中，我们提出了一种基于学习的方法，通过优化窄相位中的检查顺序来加速基于相位的碰撞检测。我们首先制定了窄相位的预期时间成本，并推导出最小化该预期的最优检查排序标准。由于该标准所需的先验难以提前获得，我们设计了一个基于超网络的模型来预测碰撞概率，然后用这些概率来近似最优检查顺序。由此产生的顺序指导窄相位中精确网格检查的执行，从而在不替换底层几何碰撞检查器的情况下减少检测时间。仿真结果表明，我们的方法有效加速了基于相位的碰撞检测，并提高了基于采样的运动规划的效率和成功率，尤其是在杂乱环境中。

英文摘要

Collision detection is critical for ensuring the safety of planned paths. However, it imposes a non-negligible computational burden on motion planners, motivating extensive studies on collision-detection acceleration. In commonly used phase-based collision-detection methods, the broad phase employs hierarchical structures to rapidly discard object pairs that are clearly collision-free, while the subsequent narrow phase performs detailed collision checks on the remaining object pairs whose collision status cannot be determined by the broad phase. Although these methods effectively reduce the number of detailed checks through broad-phase pruning, the narrow phase is usually executed in the default order returned by the broad phase, with little explicit optimization of the check order. This leaves room for further acceleration, especially in cluttered environments where many object pairs may remain after the broad phase and the narrow phase can account for a significant portion of the total detection time. In this work, we propose a learning-based method to accelerate phase-based collision detection by optimizing the check order in the narrow phase. We first formulate the expected time cost of the narrow phase and derive an optimal check-ordering criterion that minimizes this expectation. Since the priors required by this criterion are difficult to obtain in advance, we design a hypernetwork-based model to predict collision probabilities, which are then used to approximate the optimal check order. The resulting order guides the execution of exact mesh checks in the narrow phase, thereby reducing detection time without replacing the underlying geometric collision checker. Simulation results show that our method effectively accelerates phase-based collision detection and improves the efficiency and success rate of sampling-based motion planning, especially in cluttered environments.

发表机构

• Shanghai Jiao Tong University（上海交通大学）

机构由 AI 辅助整理，请以论文原文为准。