Intelligent motion planning of three-wheeled mobile robots via nonlinear model predictive control

Abstract

 This study proposes an intelligent, safe, and efficient motion planning strategy for a Three-Wheeled Mobile Robots, based on the integration of Nonlinear Model Predictive Control (NMPC) and Control Barrier Functions (CBF), collectively referred to as NMPC-CBF. The approach is implemented in MATLAB R2023b using the CasADi toolkit and the IPOPT solver, and evaluated through two challenging simulation scenarios involving both static and dynamic obstacles. The results demonstrate that NMPC-CBF not only enables effective collision avoidance but also generates shorter trajectories, smaller position and orientation errors, and smoother, more stable control signals compared to conventional NMPC. Thanks to its flexible response and consistent control performance in highly dynamic environments, the proposed method shows promising potential for practical applications in robotic systems operating under complex conditions.