Model Predictive Collision-Free Path Following Control for Nonholonomic Mobile Robots

In this research, a model predictive collision-free path following controller is developed and applied for an omnidirectional mobile robot (OMR). The mobile robot is controlled to track a reference path while avoiding collision with obstacles. The path-following problem is reformulated into the regulation problem of an extended plant by introducing a virtual degree of freedom, the path parameter of a geometric reference curve. Then a Model Predictive Controller (MPC) is then applied to steer the mobile robot. The optimization cost function is established from the difference between the state of the robot and the parameter path. The solution of MPC can be obtained by repeatedly solving an optimal control problem (OCP) to reduce the optimization cost function to a minimum value, making the robot state as close to the state of the path as possible. Obstacle avoidance is considered by adding terms as a function of the gap between the mobile robot and the objects in front of the robot. Constraints on the states and inputs of the system are also easily considered in the optimal control problem of MPC. This makes the control inputs not exceed the allowable limits of the robot. Simulations are carried out to reveal the controller's efficiency and show how to choose the right parameters to synchronize path tracking and obstacle avoidance tasks.