HDRM: A Resolution Complete Dynamic Roadmap for Real-Time Motion Planning in Complex Scenes
HDRM: A Resolution Complete Dynamic Roadmap for Real-Time Motion Planning in Complex Scenes
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HDRM：复杂场景中实时运动规划的分辨率完整动态路线图
DOI：
10.1109/lra.2017.2773669
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发表时间：
2018
影响因子：
5.2
通讯作者：
S. Vijayakumar
中科院分区：
文献类型：
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作者：
Yiming Yang;W. Merkt;V. Ivan;Zhibin Li;S. Vijayakumar
在这封信中，我们首先从理论上证明了具有离散工作空间的确定性路线图方法的分辨率完全性的条件和边界。这种方法的一种新的变体，层次动态路线图（HDRM），然后提出了解决复杂的规划问题。一个独特的层次结构，以有效地编码的配置工作空间占用信息的介绍，并允许机器人检查碰撞状态的数以千万计的样本上飞的数量是以前严格限制的可用内存。分层结构还显著减少了路径搜索的时间，因此，机器人能够在极其受限的环境中实时找到可行的运动计划。一个严格的基准测试表明，HDRM是强大的，计算速度快，与经典的动态路线图方法和其他国家的最先进的规划算法相比。在集成实时感知的七自由度KUKA LWR机械臂上进行的实验进一步验证了HDRM在复杂环境中的有效性。
In this letter, we first theoretically prove the conditions and boundaries of resolution completeness for deterministic roadmap methods with a discretized workspace. A novel variant of such methods, the hierarchical dynamic roadmap (HDRM), is then proposed for solving complex planning problems. A unique hierarchical structure to efficiently encode the configuration-to-workspace occupation information is introduced and allows the robot to check the collision state of tens of millions of samples on-the-fly—the number of which was previously strictly limited by available memory. The hierarchical structure also significantly reduces the time for path searching, hence, the robot is able to find feasible motion plans in real-time in extremely constrained environments. A rigorous benchmarking shows that HDRM is robust and computationally fast compared with classical dynamic roadmap methods and other state-of-the-art planning algorithms. Experiments on the seven degree-of-freedom KUKA LWR robotic arm integrated with live perception further validate the effectiveness of HDRM in complex environments.
DOI：
10.1109/coase.2017.8256092
发表时间：
2017-08
期刊：
2017 13th IEEE Conference on Automation Science and Engineering (CASE)
影响因子：
--
作者：
W. Merkt;Yiming Yang;Theodoros Stouraitis;Christopher E. Mower;M. Fallon;S. Vijayakumar
通讯作者：
W. Merkt;Yiming Yang;Theodoros Stouraitis;Christopher E. Mower;M. Fallon;S. Vijayakumar