Reactive Whole-Body Control of Mobile Manipulators for Dynamic Target Tracking Via Adaptive-Predictive Visual Servoing

Abstract

This paper addresses the challenging problem of enabling a mobile manipulator with an eye-in-hand camera to track dynamic targets with time-varying positions and orientations in an unbounded workspace. Specifically, we propose an optimization-based whole-body control framework for dynamic target tracking. The framework enables the mobile manipulator to maintain the target within the camera’s field of view while reaching the desired pose, by dynamically regulating the priorities of the optimization constraints and objectives according to the task execution state. Moreover, we present an adaptive-predictive position-based visual servoing strategy to generate the Cartesian references sent to the controller. To enhance the tracking performance, we introduce (1) adaptive gains to avoid abrupt motions and the resulting vibrations while preserving final precision; (2) dynamic addition of a feedforward term incorporating a velocity estimate of the target using a Kalman Filter. The proposed approach is validated on a real robotic setup, as compared to a state-of-the-art approach, demonstrating superior performance in dynamic target tracking.