Immersion-and-Invariance Fuzzy Adaptive Image-Based Visual Servoing of Omnidirectional Mobile Robots Considering Uncertain Feature Point Dynamics

This paper proposes an immersion and invariance (I&I) fuzzy adaptive image-based visual servoing (IBVS) method for an omnidirectional mobile robot (OMR), ensuring robustness against the uncertainties in feature point dynamics. Most existing IBVS studies for OMR systems have only addressed the kinematics of OMRs, and studies that consider the dynamics of OMRs have only been conducted recently. In particular, the practical uncertainties caused by dynamic uncertainty, interaction matrix uncertainty, and target motion have not been considered in previous studies. In this paper, feature point dynamics of OMRs are modeled by including all of the dynamic uncertainty, interaction matrix uncertainty, and target motion. To approximate and compensate for these uncertainties, fuzzy logic systems (FLS) with two update laws—the general adaptive law and I&I law—are proposed. Notably, the proposed I&I law eliminates the limitations of general adaptive methods, improving transient response owing to its noncertainty-equivalent adaptive structure. The proposed methods are designed by based on the FLS, I&I law, and integral sliding mode control all together to compensate for the practical uncertainties in feature point dynamics, guaranteeing global asymptotic stability unlike the existing studies. The stability and feasibility of the proposed methods are verified via the Lyapunov stability analysis, simulations, and experiments in an environment with uncertain feature point dynamics.