REAL-TIME PATH TRACKING METHOD USING DIFFERENTIAL FLATNESS FOR CAR-LIKE MOBILE ROBOT

Car-like mobile robot is a typical nonholonomic system, and its path tracking problem is highly nonlinear. The path tracking problem of mobile robot with incomplete information in the dynamic environment is a difficult issue. In this paper, by explicitly considering a kinematic model of the car-like robot, the feasible flat outputs are derived. Then, the controller solving procedure is transformed from the high-order nonlinear space into a low-order flat space. At the same time, path tracking of robot in dynamic environment should take not only tracking problem but also obstacle avoidance problem into consideration. To solve these problems, moving optimization principle of model predictive control (MPC) is used in this paper. Based on the flatness properties and MPC principle, we propose a differential flatness method under the MPC framework to track the path in this paper, by which the reference path can be followed in real time, and the mobile robot can also avoid the unknown obstacles that lie on the reference path. Series of simulation experiments are presented to verify the feasibility, and the simulation results illustrate that the robot can avoid the obstacle successfully as well as the tracking effect.