Exactly estimating and tracking the motion of surrounding dynamic objects is one of important tasks for the autonomy of a quadruped manipulator. However, with only an onboard RGB camera, it is still a challenging work for a quadruped manipulator to track the motion of a dynamic object moving with unknown and changing velocities. To address this problem, this manuscript proposes a novel image-based visual servoing (IBVS) approach consisting of three elements: a spherical projection model, a robust super-twisting observer, and a model predictive controller (MPC). The spherical projection model decouples the visual error of the dynamic target into linear and angular ones. Then, with the presence of the visual error, the robustness of the observer is exploited to estimate the unknown and changing velocities of the dynamic target without depth estimation. Finally, the estimated velocity is fed into the model predictive controller (MPC) to generate joint torques for the quadruped manipulator to track the motion of the dynamical target. The proposed approach is validated through hardware experiments and the experimental results illustrate the approach's effectiveness in improving the autonomy of the quadruped manipulator.

翻译：精确估计与跟踪周围动态目标的运动是实现四足机械臂自主性的关键任务之一。然而，仅依靠机载RGB相机，四足机械臂在跟踪具有未知且时变速度的动态目标运动时仍面临挑战。针对此问题，本文提出了一种基于图像的视觉伺服（IBVS）新方法，该方法包含三个要素：球面投影模型、稳健超螺旋观测器以及模型预测控制器（MPC）。球面投影模型将动态目标的视觉误差解耦为线性误差与角误差。进而，在存在视觉误差的情况下，利用观测器的稳健性，无需深度估计即可估计动态目标未知且时变的运动速度。最后，将估计速度输入模型预测控制器（MPC）以生成四足机械臂的关节扭矩，从而实现对其动态目标的运动跟踪。通过硬件实验对所提方法进行了验证，实验结果表明该方法在提升四足机械臂自主性方面具有有效性。