Alterovitz Ron, Patil Sachin, Derbakova Anna
Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA {ron,sachin,anya}@cs.unc.edu.
IEEE Int Conf Robot Autom. 2011:3706-3712. doi: 10.1109/ICRA.2011.5980286.
Computing globally optimal motion plans requires exploring the configuration space to identify reachable free space regions as well as refining understanding of already explored regions to find better paths. We present the rapidly-exploring roadmap (RRM), a new method for single-query optimal motion planning that allows the user to explicitly consider the trade-off between exploration and refinement. RRM initially explores the configuration space like a rapidly exploring random tree (RRT). Once a path is found, RRM uses a user-specified parameter to weigh whether to explore further or to refine the explored space by adding edges to the current roadmap to find higher quality paths in the explored space. Unlike prior methods, RRM does not focus solely on exploration or refine prematurely. We demonstrate the performance of RRM and the trade-off between exploration and refinement using two examples, a point robot moving in a plane and a concentric tube robot capable of following curved trajectories inside patient anatomy for minimally invasive medical procedures.
计算全局最优运动规划需要探索配置空间，以识别可达的自由空间区域，并完善对已探索区域的理解，从而找到更好的路径。我们提出了快速探索路线图（RRM），这是一种用于单查询最优运动规划的新方法，它允许用户明确考虑探索与完善之间的权衡。RRM最初像快速探索随机树（RRT）一样探索配置空间。一旦找到一条路径，RRM会使用用户指定的参数来权衡是进一步探索还是通过向当前路线图添加边来完善已探索的空间，以便在已探索的空间中找到更高质量的路径。与先前的方法不同，RRM并非仅专注于探索或过早地进行完善。我们使用两个示例展示了RRM的性能以及探索与完善之间的权衡，一个是在平面上移动的点机器人，另一个是能够在患者解剖结构内沿着弯曲轨迹移动以进行微创医疗手术的同心管机器人。
