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Robot task planning and trajectory learning based on programming by demonstration

机译:基于演示编程的机器人任务计划和轨迹学习

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This paper presents a method to model and reproduce cyclic trajectories captured from human demonstrations. Heuristic algorithms are used to determine the general type of pattern, its parameters, and its kinematic profile. The pattern is described independently of the shape of the surface on which it is demonstrated. Key pattern points are identified based on changes in direction and velocity, and are then reduced based on their proximity. The results of the analysis are provided are used inside a task planning algorithm, to produce robot trajectories based on the workpiece geometries. The trajectory is output in the form of robot native language code so that it can be readily downloaded on the robot.
机译:本文提出了一种建模和复制从人类演示中捕获的循环轨迹的方法。启发式算法用于确定模式的一般类型,其参数及其运动学轮廓。图案的描述与显示图案的表面形状无关。根据方向和速度的变化识别关键图案点,然后根据其接近程度将其减少。提供的分析结果在任务计划算法中使用,以根据工件的几何形状生成机器人轨迹。轨迹以机器人本地语言代码的形式输出,因此可以很容易地将其下载到机器人上。

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