Dynamic Object Tracking for Quadruped Manipulator with Spherical Image-Based Approach

TL;DR

This paper introduces a novel image-based visual servoing approach for quadruped manipulators that enables accurate tracking of dynamic objects using only an onboard RGB camera, combining spherical projection, a robust observer, and MPC.

Contribution

It proposes a new IBVS framework with a spherical projection model, a super-twisting observer, and MPC to track moving objects without depth estimation.

Findings

Effective in hardware experiments

Improves autonomy of quadruped manipulators

Accurately estimates dynamic object velocities

Abstract

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.