Traditional robot trajectory planning and programming methods often struggle to adapt to changing working requirements, leading to repeated programming in manufacturing processes. To address these challenges, a trajectory learning and modification method based on improved Dynamic Movement Primitives (DMPs), called FDC-DMP, is proposed. The method introduces an improved force-controlled dynamic coupling term (FDCT) that uses virtual force as coupling force. This enhancement enables precise and flexible shape modifications within the target trajectory range. The paper also dissects the core dynamic systems of DMP to achieve the reproduction and generalization of both robot position and pose trajectories. The practical feasibility of the proposed method in manufacturing is demonstrated through two case studies on trajectory planning for bus body polishing.

中文翻译：

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基于改进动态运动基元的轨迹学习与修正方法研究

传统的机器人轨迹规划和编程方法往往难以适应不断变化的工作要求，导致制造过程中的重复编程。为了解决这些挑战，提出了一种基于改进的动态运动基元（DMP）的轨迹学习和修改方法，称为FDC-DMP。该方法引入了一种改进的力控制动态耦合项（FDCT），它使用虚拟力作为耦合力。这一增强功能可以在目标轨迹范围内实现精确、灵活的形状修改。本文还剖析了 DMP 的核心动态系统，以实现机器人位置和位姿轨迹的再现和泛化。通过公交车车身抛光轨迹规划的两个案例研究证明了所提出的方法在制造中的实际可行性。