This paper presents an intelligent sliding mode formation control using recurrent fuzzy wavelet neural networks (RFWNN) for a group of uncertain, networked heterogeneous Mecanum-wheeled omnidirectional platforms (MWOPs). The dynamic behavior of each uncertain MWOP is modelled by a reduced three-input-three-output second-order state equation and the multi-MWOP system is modeled by a directed graph. By using the Lyapunov stability theory and online learning the system uncertainties via RFWNN, an intelligent adaptive, sliding mode control approach is presented to carry out formation control in presence of uncertainties. Two simulations are conducted to show the effectiveness and merit of the proposed method with existing collision-free and obstacle-avoidance approaches.