Cache-Aware Asymptotically-Optimal Sampling-Based Motion Planning.
Cache-Aware Asymptotically-Optimal Sampling-Based Motion Planning.
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基于缓存感知的渐近最优采样运动规划。
DOI：
10.1109/icra.2014.6907712
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Alterovitz,Ron
中科院分区：
文献类型：
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作者：
Ichnowski,Jeffrey;Prins,JanF;Alterovitz,Ron
我们提出了CARRT*（缓存感知快速探索随机树*），这是一种基于渐进最优采样的运动规划器，通过有效利用现代中央处理器（cpu）的缓存存储器层次结构，显著减少了运动规划计算时间。CARRT*可以以一种将其工作数据集保存在缓存中的方式来解释CPU的缓存大小。运动规划器随着位形样本数量的增加，逐步将机器人的位形空间细分为更小的区域。通过将配置探索集中在一个区域的一段时间内，最近邻搜索可以加速，因为工作数据集足够小，可以放入缓存中。CARRT*还以一种补充缓存感知细分策略的方式重新连接运动规划图，以更快地优化运动规划图。我们展示了我们的缓存感知运动规划方法在涉及点机器人以及Rethink Robotics Baxter机器人的场景中的性能优势。
We present CARRT* (Cache-Aware Rapidly Exploring Random Tree*), an asymptotically optimal sampling-based motion planner that significantly reduces motion planning computation time by effectively utilizing the cache memory hierarchy of modern central processing units (CPUs). CARRT* can account for the CPU's cache size in a manner that keeps its working dataset in the cache. The motion planner progressively subdivides the robot's configuration space into smaller regions as the number of configuration samples rises. By focusing configuration exploration in a region for periods of time, nearest neighbor searching is accelerated since the working dataset is small enough to fit in the cache. CARRT* also rewires the motion planning graph in a manner that complements the cache-aware subdivision strategy to more quickly refine the motion planning graph toward optimality. We demonstrate the performance benefit of our cache-aware motion planning approach for scenarios involving a point robot as well as the Rethink Robotics Baxter robot.