Applications of industrial robotic manipulators such as cobots can require efficient online motion planning in environments that have a combination of static and non-static obstacles. Existing general purpose planning methods often produce poor quality solutions when available computation time is restricted, or fail to produce a solution entirely. We propose a new motion planning framework designed to operate in a user-defined task space, as opposed to the robot's workspace, that intentionally trades off workspace generality for planning and execution time efficiency. Our framework automatically constructs trajectory libraries that are queried online, similar to previous methods that exploit offline computation. Importantly, our method also offers bounded suboptimality guarantees on trajectory length. The key idea is to establish approximate isometries known as $\epsilon$-Gromov-Hausdorff approximations such that points that are close by in task space are also close in configuration space. These bounding relations further imply that trajectories can be smoothly concatenated, which enables our framework to address batch-query scenarios where the objective is to find a minimum length sequence of trajectories that visit an unordered set of goals. We evaluate our framework in simulation with several kinematic configurations, including a manipulator mounted to a mobile base. Results demonstrate that our method achieves feasible real-time performance for practical applications and suggest interesting opportunities for extending its capabilities.
翻译:在固定和非静态障碍交织的环境中,工业机器人操控器(如cobots)的应用需要高效的在线运动规划。现有的通用目的规划方法往往在可用的计算时间有限时产生质量差的解决方案,或者无法完全产生解决方案。我们提议一个新的运动规划框架,目的是在用户定义的任务空间运行,而不是机器人的工作空间,有意将工作空间的一般性与规划和执行时间效率进行交换。我们的框架自动建立在线查询的轨道图书馆,类似于以往利用离线计算的方法。重要的是,我们的方法也为轨迹长度提供受约束的亚优化保障。关键的想法是建立近似缩略数,称为$\epslon$-Gromov-Hausdorf-Hausdovf 近于任务空间的点也在配置空间中运行。这些连接关系还意味着轨迹可以顺利配置,使我们的框架能够解决在线查询的轨迹图。我们的方法还提供最短的轨迹序列,用于轨迹长度的轨道长度保证。关键是要建立最短的轨迹定的轨迹,以访问不固定的轨迹定的模型,以显示机动的机动式模型,以显示机动式的机能功能,以展示。我们的目标包括机动的机动的机动式模型。