Informed Sampling-Based Motion Planning for Manipulating Multiple Micro Agents using Global External Fields
Informed Sampling-Based Motion Planning for Manipulating Multiple Micro Agents using Global External Fields
复制标题
使用全局外部场操纵多个微代理的基于知情采样的运动规划
DOI：
10.1109/case48305.2020.9217046
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Kaiyan Yu
中科院分区：
文献类型：
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作者：
Xilin Li;Kaiyan Yu
在本文中，我们提出了新的运动规划算法，Bi-iSST和Ref-iSST，快速生成时间最优的轨迹共享全球外部领域的多个代理。这两种算法的扩展，稳定稀疏快速探索随机树kinodynamic运动规划算法。Bi-iSST使用双向方法来加速搜索过程。提出了一种新的连接过程，通过应用优化过程有效地连接两棵树。Ref-iSST使用工作空间信息快速生成全局布线轨迹作为参考，然后根据全局布线参考轨迹获得更精确的路径，从而更有效地指导搜索过程。与现有的算法相比，所提出的算法能够快速更新可行解，并收敛到一个接近最优的最短时间解，从而提高了全局外场同时控制多个微代理的效率。
In this paper, we propose novel motion planning algorithms, Bi-iSST and Ref-iSST, to quickly generate time-optimal trajectories for multiple agents sharing global external fields. Both algorithms are extended by the stable sparse rapidly-exploring random tree kinodynamic motion-planning algorithm. The Bi-iSST uses the bidirectional approach to speed up the searching process. A novel connection process is proposed to connect two trees efficiently by applying an optimization procedure. The Ref-iSST uses the workspace information to quickly generate global-routing trajectories as references, then guides the search process more effectively by getting more accurate heuristics according to the global-routing reference trajectory. Compared to the state-of-the-art algorithms, the proposed algorithms quickly update feasible solutions and converge to a near-optimal, minimum-time solution to increase the efficiency of the simultaneous manipulation of multiple micro agents using global external fields.