NMPC Motion Planning Method For Two Wheel Differential Motion Model

Abstract

At present, many motion planning algorithms generally have problems such as linearization and failure to consider the robot kinematics model, which may lead to unsmooth path planning, or vehicles cannot fully fit the path. Aiming at the problem of motion planning, a nonlinear model predictive control (NMPC) algorithm for two wheel differential model is designed. The algorithm uses NMPC as the algorithm core and takes the two wheel differential motion model as the kinematic constraint. In order to achieve navigation and obstacle avoidance in complex environments, the control obstacle function (CBF) and control Lyapunov function (CLF) are added to NMPC as constraints. The planning ability of NMPC in simple environments with static obstacles and NMPC-DCBF-DCLF in complex environments with multiple moving obstacles are tested. The experimental results show that the algorithm can well complete path fitting in simple environments and obstacle avoidance planning in complex environments.