发表机构
Beihang University; Taiyuan University of Technology（北京航空航天大学; 太原理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于模仿轨迹约束的机器人伺服跟踪方法，通过动态模型、时间缩放变形和轨迹调制生成动态约束，实现移动目标的高精度跟踪与避障。
AI 中文摘要
在机器人视觉伺服中施加显式轨迹约束仍然具有挑战性。现有的跟踪方法通过将视觉残差映射到控制速度来实现快速响应，但对中间运动过程的约束较弱，导致轨迹不连续、振荡或行为保守。为了实现移动目标的受约束跟踪，本文提出了一种基于模仿轨迹约束的伺服跟踪方法。建立并分析了描述机器人接近移动目标的动态模型的收敛性。引入时间可缩放变形机制和包含形状与幅度分量的轨迹调制，以实时生成一系列轨迹，并从中自适应确定跟踪点以形成动态约束。然后，根据目标位姿差分或跟踪的关键特征计算机器人速度，以遵循受约束的轨迹。仿真和真实世界实验表明，与几种最先进的方法相比，所提出的方法在复杂环境中能够实现动态避障和高精度收敛。
英文摘要
Imposing explicit trajectory constraints in robot visual servoing remains challenging. Existing tracking methods achieve fast responses by mapping visual residuals to control velocities, but they have weak constraints on the intermediate motion process, which lead to trajectory discontinuity, oscillation, or conservative behaviors. To enable constrained tracking for moving targets, this paper proposes a servo tracking method based on imitation trajectory constraints. A dynamic model describing the robot approaching a moving target is formulated and analyzed for convergence. A time-scalable deformation mechanism and a trajectory modulation incorporating shape and amplitude components are introduced to generate a series of trajectories in real time, from which tracking points are adaptively determined to form dynamic constraints. The robot velocity is then computed from target pose differentials or tracked key features to follow the constrained trajectory. Simulation and real-world experiments demonstrate that the proposed method can achieve dynamic obstacle avoidance and high-precision convergence compared with several state-of-the-art methods in complex environments.
