🤖 AI Summary
为解决机器人视觉伺服中对移动目标的轨迹约束问题，提出了一种基于模仿轨迹约束的伺服跟踪方法，通过动态模型和实时轨迹生成机制实现高精度跟踪。
📝 Abstract
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.