LiDAR-Based Obstacle Detection and Avoidance for Navigation and Control of an Unmanned Ground Robot Using Model Predictive Control

Unmanned Ground Vehicles (UGVs) have, as of late, been utilized in a wide assortment of utilizations because of their flexibility, diminished expense, and quick response, among other benefits. Search and Rescue (SAR) is quite possibly the most conspicuous zones for the work of UGVs instead of a monitored mission, mainly due to its impediments on the expenses, human resources, and view of the human administrators. An ongoing way of arranging to utilize numerous helpful UGVs for the SAR mission is proposed in this study. This study aims to introduce the initial moves towards a Model Predictive Control (MPC) based peril evasion calculation for UGVs representing the vehicle elements through high constancy models and uses just surrounding data about the environment as given by the available onboard sensors. In particular, the paper presents the MPC definition for peril evasion utilizing a Light Detection and Ranging (LiDAR) sensor and applies it to a contextual of the effect of model constancy on the calculation's presentation, where execution is estimated principally when to arrive at the objective point. The Robot Operating System (ROS) is used to drive the sensors and visualize the data in RVIZ. This study presents MPC development for navigating Husky A200 by adjusting the longitudinal, lateral, and yaw motion command behaviors. The proposed algorithm for Husky A200 is tested indoors and compared the results with the simulation results plotted using MATLAB and GAZEBO. A novel simulator package is developed for the Husky using RVIZ and GAZEBO. The efficiency of the proposed MPC design is tested through simulation and compared with real world experiments, the real-time longitudinal movement follows the simulation results closely. For MPC's short-term optimization, an optimized control signal from a linear framework is utilized for a linear quadratic controller. According to the Husky position and orientation, applying a transformation to convert the map coordinate system to the Husky coordinate system. Transforming the map coordinate system helped in computing the errors because the initial vector considers position and orientation as zero.