Distributed Consensus Formation Control with Collision and Obstacle Avoidance for Uncertain Networked Omnidirectional Multi-robot Systems Using Fuzzy Wavelet Neural Networks

This paper presents a distributed consensus formation control with collision and obstacle avoidance using fuzzy wavelet neural networks (FWNNs) for a group of networked mobile Mecanum-wheeled omnidirectional robots (MWORs) with uncertainties. The dynamic behavior of each uncertain MWOR is modeled by a reduced three-input–three-output second-order state equation with uncertainties, and the multi-MWOR system is modeled by graph theory. Using the Lyapunov stability theory and online learning the system uncertainties via FWNNs, an adaptive and distributed consensus backstepping control approach is presented to carry out formation control in the presence of uncertainties. Collision and obstacle-avoidance methods are provided to avoid any collisions among MWORs and their working environments. Five simulations are conducted to show the effectiveness and merit of the proposed method .