Design and Implementation of Trajectory Tracking Motion in Mobile Robot Skid Steering Using Model Predictive Control

This research aims to design and implement the motion planning and trajectory tracking on a mobile robot four-wheel skid steering system using Model Predictive Control (MPC). Motion planning is one of the fundamental problems in the navigation of autonomous robots. The main concern of this research is to find the paths that can guide the robot running from the initial position to the destination. The first thing to do was create a map of the environment that would be used, then the map result was used to find the path that was free from obstacles. The searching process used A* (A-star) algorithm, then the path was used as the robot reference path. The Design of Predictive Model Control aims to track the reference path by the robot, in designing this MPC used the dynamics of the Pioneer 3-AT robot model based on the current robot position against the reference robot position. Model Predictive Control could accommodate the limitation on control signals, while also predicting the subsequent system behavior along the specified horizon. Error tracking errors are a combination of MPC which was derived through the quadratic function of the error tracking system and advanced feed control. Designed control would be simulated first to see the system behavior. This simulation process used MobileSim software that was integrated with MATLAB to obtain appropriate system behavior. The results of the implementation showed that the Model Predictive Control could track to reach the destination on the reference path in the form of the number “8”, the path to the end of the LSKK Lane, and the path to the JCC LSKK ITB Room with an average tracking error of less than 10 cm (x, y) and 0,2 degree.