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Collision detection for 3D rigid body motion planning with narrow passages

机译:具有狭窄通道的3D刚体运动计划的碰撞检测

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In sampling-based 3D rigid body motion planning one of the major subroutines is collision detection. Especially for problems with narrow passages many samples have to be checked by a collision detection algorithm. In this application, the runtime of the motion planning algorithm is dominated by collision detection and the samples have the very specific characteristic that many of them are in collision and have small penetration volumes. In our work, we introduce a data structure and an algorithm that makes use of this characteristic by combining well-known data structures like a distance field and an octree with the swap algorithm by Llanas et al. For 3D rigid body motion planning with narrow passages, our approach achieves a speedup of up to 5.0 compared to well-established collision detection libraries like the Proximity Query Package (PQP) and the Flexible Collision Library (FCL).
机译:在基于采样的3D刚体运动计划中,主要子例程之一是碰撞检测。特别是对于通道狭窄的问题,必须通过碰撞检测算法检查许多样本。在此应用中,运动计划算法的运行时间主要由碰撞检测决定,并且样本具有非常特殊的特性,即许多样本处于碰撞状态且穿透量较小。在我们的工作中,我们通过结合著名的数据结构(例如距离场和八叉树)和Llanas等人的交换算法,介绍了一种利用此特性的数据结构和算法。对于通道狭窄的3D刚体运动计划,与完善的碰撞检测库(例如,邻近查询包(PQP)和柔性碰撞库(FCL))相比,我们的方法可将速度提高多达5.0。

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