Output-feedback formation tracking control of networked nonholonomic multi-robots with connectivity preservation and collision avoidance

Abstract This paper studies the problem of adaptive output-feedback formation tracking control for networked uncertain nonholonomic mobile robots (NMRs) with different limited communication distances, while achieving connectivity preservation and collision avoidance. In robot control, equipping all the speed sensors not only increases the cost of the system but also risks losing accuracy and reliability. In this paper, it is assumed that the velocities of the nonholonomic mobile robots in this paper are unknown, they are estimated according to the output of the systems by an adaptive observer via a neural network. The designed dynamic surface based on nonlinear transformation errors can achieve the above three control objectives at the same time, avoiding the situation of using multiple potential functions to solve such problems, which will lead to conflicts in the selection of the design parameters. Compared with the existing references about formation tracking to maintain connectivity and avoid collisions, the main contribution of this paper is to design an observer of robot speed estimation and use only a neural network to estimate the unknown nonlinear term of the system itself. The unknown nonlinearity produced in the process of controller design does not need an additional neural network. Furthermore, the effectiveness of the proposed strategy is verified by simulation examples.