In robotics, the tough problem about the dynamic target capturing consists of tracking the target by the robot manipulator and grasping the target by the robot finger. For the sake of space, this article deals with only the first problem, tracking the dynamic target by the robot manipulator. The traditional approaches of capturing the dynamic target may work well when they are employed in low-dimensional space by reinforcement learning or physical modeling. However, they fail to work well in high-dimensional space. The traditional approaches have four limitations with respect to Cartesian space, configuration space, reinforcement learning, and physical modeling. To overcome these limitations, this article implements improved dynamic A* algorithm in high-dimensional configuration space map to capture the target. First, a space injection model injects the collision detection and target position from the Cartesian space into the configuration space to construct a high-dimensional map. Then, the target capturing method including the improved dynamic A* algorithm is applied on the map to track and capture the target. Finally, the experiment performed in time-varying environment and the dynamic target achieves a reliable result. This article has proposed an approach that makes the robot manipulator motion planning more accurate in high-dimensional dynamic configuration space. This approach enables the multi-joint manipulator to avoid the obstacle while tracking the target in high-dimensional configuration space. It takes the advantages of heuristic algorithms in the process of target capturing method designing. It adds precision and speed to target tracking. The success of the approach may apply to any industrial robot tracking target, surgical operation, and space probes. And, it may lay a solid foundation for dynamics control with a scope for future investigations.

中文翻译：

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机械手在高维构型空间图中捕捉动态目标

在机器人学中，动态目标捕获的难题包括机器人机械手跟踪目标和机器人手指抓取目标。为了篇幅，本文只处理第一个问题，机器人机械手跟踪动态目标。当通过强化学习或物理建模在低维空间中使用时，捕获动态目标的传统方法可能会很好地工作。然而，它们在高维空间中不能很好地工作。传统方法在笛卡尔空间、配置空间、强化学习和物理建模方面有四个限制。为了克服这些限制，本文在高维配置空间图中实现了改进的动态A*算法来捕获目标。第一的，空间注入模型将碰撞检测和目标位置从笛卡尔空间注入配置空间，构建高维地图。然后，在地图上应用包括改进的动态A*算法在内的目标捕获方法来跟踪和捕获目标。最后，在时变环境和动态目标下进行的实验取得了可靠的结果。本文提出了一种在高维动态配置空间中使机器人机械手运动规划更加准确的方法。这种方法使多关节机械手能够在高维配置空间跟踪目标的同时避开障碍物。在目标捕获方法的设计过程中，充分利用了启发式算法的优点。它增加了目标跟踪的精度和速度。该方法的成功可能适用于任何工业机器人跟踪目标、外科手术和空间探测器。而且，它可以为动力学控制奠定坚实的基础，并为未来的研究提供空间。