ABSTRACT Skillful motions in the actual assembly process are challenging for the robot to generate with conventional motion planning approaches because some states during the human assembly can be too skillful to realize automatically due to the narrow passage. To deal with this problem, this paper develops a motion planning method using the human demonstration, which can be applied to complete skillful motions in the robotic assembly process. To demonstrate conveniently without redundant third-party devices, we attach augmented reality (AR) markers to the manipulated object to track and capture its poses during the human demonstration. To overcome the problem brought by the coarse resolution of the vision system, we extract the most important key poses from the demonstration data and employ them as clues to execute motion planning to suit the target precise task. As for the selection of key poses, two policies are compared, where the first and the second derivative of the main changing parameter of every key pose serve as criteria to determine the priority of utilizing key poses. Besides, a solution to deal with colliding key poses is also proposed. The effectiveness of the presented method is verified through some simulation examples and actual robot experiments. GRAPHICAL ABSTRACT

中文翻译：

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通过人工演示对装配过程中熟练动作的运动规划

摘要 实际装配过程中的熟练运动对于机器人使用传统的运动规划方法生成具有挑战性，因为由于通道狭窄，人类装配过程中的某些状态可能过于熟练而无法自动实现。针对这一问题，本文开发了一种基于人体演示的运动规划方法，可用于完成机器人装配过程中的熟练动作。为了在没有多余第三方设备的情况下方便地演示，我们将增强现实 (AR) 标记附加到操纵对象上，以在人类演示期间跟踪和捕获其姿势。为了克服视觉系统分辨率粗糙带来的问题，我们从演示数据中提取最重要的关键姿势，并将它们用作执行运动规划以适应目标精确任务的线索。对于关键姿态的选择，比较了两种策略，其中每个关键姿态的主要变化参数的一阶和二阶导数作为确定利用关键姿态的优先级的标准。此外，还提出了一种处理碰撞关键姿势的解决方案。通过一些仿真实例和实际机器人实验验证了所提出方法的有效性。图形概要 还提出了一种处理碰撞关键姿势的解决方案。通过一些仿真实例和实际机器人实验验证了所提出方法的有效性。图形概要 还提出了一种处理碰撞关键姿势的解决方案。通过一些仿真实例和实际机器人实验验证了所提出方法的有效性。图形概要