Decentralized cooperative transportation with obstacle avoidance using fuzzy wavelet neural networks for uncertain networked omnidirectional multi-robots

Abstract

This paper presents a decentralized cooperative transportation control method with obstacle avoidance using fuzzy wavelet neural networks (FWNN) and consensus algorithm for a group of mobile Mecanum-wheeled omnidirectional robots (MWORs) with uncertainties, in order to get together to move a large payload. The dynamic behavior of each uncertain MWOR is modelled by a reduced three-input-three-output second-order system model and the uncertain multi-MWOR system is modeled by graph theory. By online learning the system uncertainties using FWNN and using the Lyapunov stability theory, an intelligent adaptive, cooperative consensus-based control approach is presented to carry out transportation control with uncertainties. An obstacle avoidance method is proposed to modify the generating trajectory of the virtual leader, in order to avoid any collisions between the robots and the environment. Two simulations are conducted to show the effectiveness of the proposed method.