Improved Model Predictive Control System Design and Implementation for Unmanned Ground Vehicles

Abstract

: Autonomous ground robots autonomously are being used in the places where it is very hazardous for human beings to reach and operate, such as nuclear power plants and chemical industries. The aim of the research presented here is to develop a control system that enables such ground robots navigate autonomously with various sensors as the depth camera, 2D scanning laser, 3D Lidar, GPS, and IMU. The controller uses the current position measured using the sensors on the Husky A200, given the waypoints of the destination. Then it calculates the best possible route based on the recent events provided using IMU data and GPS. The Model Predictive Control (MPC) improves the robot’s motion, by using a path planner for the robot’s trajectory generation. The use of global reference frame waypoints is planned to create the appropriate path and the actions required to follow the motion planner’s direction. The path planner depends on the active sensor data such as locations and size of obstacles. Then, a feasible path is generated based on the sensor data. The desired trajectory consists of a set of waypoints fit in a 3 rd -order polynomial. They determine the path’s feasibility for the ground robot’s dynamics and a series of points generated with a certain velocity and acceleration profile. The MPC adjusts the robot’s lateral, longitudinal, yaw motions and approximates a continuous trajectory with discrete paths to command behaviors. The kinematic model of a robot, Husky is used as the dynamic model for transient and steady-state characteristics. The camera captures the images and other types of data processed through the computational framework used to build machine learning models. TensorFlow is used for deep learning and to identify and classify various objects around the Husky. This research has limitations such as using the linear dynamic model as the LQR method. Also on vehicle models, the vehicle model considered in this research considers a constant value to describe the slope in the most linear region. Detailed discussion on MPC development with a major system design factor has been emphasized with logical steps in MPC.