Concurrent-learning-based visual servo tracking and scene identification of mobile robots

Abstract

Purpose The purpose of this paper is to present a visual servo tracking strategy for the wheeled mobile robot, where the unknown feature depth information can be identified simultaneously in the visual servoing process. Design/methodology/approach By using reference, desired and current images, system errors are constructed by measurable signals that are obtained by decomposing Euclidean homographies. Subsequently, by taking the advantage of the concurrent learning framework, both historical and current system data are used to construct an adaptive updating mechanism for recovering the unknown feature depth. Then, the kinematic controller is designed for the mobile robot to achieve the visual servo trajectory tracking task. Lyapunov techniques and LaSalle’s invariance principle are used to prove that system errors and the depth estimation error converge to zero synchronously. Findings The concurrent learning-based visual servo tracking and identification technology is found to be reliable, accurate and efficient with both simulation and comparative experimental results. Both trajectory tracking and depth estimation errors converge to zero successfully. Originality/value On the basis of the concurrent learning framework, an adaptive control strategy is developed for the mobile robot to successfully identify the unknown scene depth while accomplishing the visual servo trajectory tracking task.

Adaptive Estimation and Control With Online Data Memory: A Historical Perspective

“…In [64], concurrent learning was combined with robust composite MRAC to overcome mismatched LIP uncertainties. In [65], concurrent learning was applied to estimate unknown camera parameters in robot visual servoing. In [66], concurrent learning was employed in a prescribed performance control framework to improve the transient performance.…”

“…Concurrent learning has been applied to robot visual servoing in [65], where a concurrent learning homography-based visual servoing (HBVS) method was developed for a wheeled mobile robot to achieve trajectory tracking and estimate scene depth information. Composite learning has been applied to enhance visual tracking accuracy while achieving online fast calibration of camera parameters in robot visual servoing [122]- [126].…”

Boosting visual servoing performance through RGB-based methods

“…With the advancements in computer vision and machine learning techniques, robots can now recognize, interpret and make decisions based on the perception information gathered (Qiao et al , 2022). This has resulted in the widespread use of robot perception in various applications, including the navigation of mobile robots, providing context-awareness for service robots (Miao et al , 2023), robot arm manipulation (Lin and Wang, 2021), manufacturing (Wan et al , 2022; Zeng et al , 2018), mobile robots (Qiu et al , 2019) and transportation guidance for logistic (Bloss, 2011).…”

Saturated Visual-Servoing Control Strategy for Nonholonomic Mobile Robots With Experimental Evaluations

“…The authors of [14] provided a model-free visual servoing strategy to drive a wheeled mobile robot to the desired pose without the desired image. Qiu et al [15] developed a concurrent learning-based visual servo tracking scheme with scene depth identification for wheeled mobile robots. A visual servo tracking scheme for wheeled mobile robots under the presence of uncalibrated translational camera-to-robot parameters was proposed in [16].…”