Sampling-based algorithms are known for their ability to effectively compute paths for high-dimensional robots in relatively short times. The same algorithms, however, are also notorious for poor-quality solution paths, particularly as the dimensionality of the system grows. This work proposes a new probabilistically complete sampling-based algorithm, XXL, specially designed to plan the motions of high-dimensional mobile manipulators and related platforms. Using a novel sampling and connection strategy that guides a set of points mapped on the robot through the workspace, XXL scales to realistic manipulator platforms with dozens of joints by focusing the search of the robot’s configuration space to specific degrees of freedom that affect motion in particular portions of the workspace. Simulated planning scenarios with the Robonaut2 platform and planar kinematic chains confirm that XXL exhibits competitive solution times relative to many existing works while obtaining execution-quality solution paths. Solutions from XXL are of comparable quality to cost-aware methods even though XXL does not explicitly optimize over any particular criteria, and are computed in an order of magnitude less time. Furthermore, observations about the performance of sampling-based algorithms on high-dimensional manipulator planning problems are presented that reveal a cautionary tale regarding two popular guiding heuristics used in these algorithms, indicating that a nearly random search may outperform the state-of-the-art when defining such heuristics is known to be difficult.

中文翻译：

---

用于高维运动系统的可扩展运动规划器

基于采样的算法以其在相对较短的时间内有效计算高维机器人路径的能力而闻名。然而，相同的算法也因低质量的解决方案路径而臭名昭著，特别是随着系统维度的增长。这项工作提出了一种新的基于概率完整采样的算法 XXL，专门设计用于规划高维移动机械手和相关平台的运动。使用新颖的采样和连接策略，引导映射在机器人上的一组点通过工作区，XXL 通过将机器人配置空间的搜索集中到影响运动的特定自由度，扩展到具有数十个关节的现实操纵器平台工作区的一部分。使用 Robonaut2 平台和平面运动链的模拟规划场景证实，XXL 在获得执行质量的解决方案路径的同时，展现出相对于许多现有工作具有竞争力的解决方案时间。来自 XXL 的解决方案的质量与成本意识方法相当，即使 XXL 没有明确优化任何特定标准，并且计算时间要少一个数量级。此外，还提出了对基于采样的算法在高维机械臂规划问题上的性能的观察，揭示了这些算法中使用的两种流行的指导启发式的警示故事，表明近乎随机的搜索可能优于当前状态众所周知，定义这种启发式方法是困难的。