An unscented model predictive control approach to the formation control of nonholonomic mobile robots

Abstract

Formation control of nonholonomic robots in dynamic unstructured environments is a challenging task yet to be met. This paper presents the unscented model predictive control (UMPC) approach to tackle the formation control of multiple nonholonomic robots in unstructured environments. In unscented predictive control, the uncertainty propagation in the nonholonomic nonlinear motion model is approximated using the unscented transform. The collision avoidance constraints have been introduced as the chance constraints to model predictive control. The UMPC approach enables us to find a closed form of the collision avoidance probabilistic constraints. The desired pose of each robot in the formation is introduced through the local objective function of UMPC of each robot. The simulation results indicate the effective and robust performance of UMPC in unstructured environment in the presence of action disturbance and communication signal noise.