Description

An a priori map is often unavailable for a mobile robot in an unknown environment. In large spaces, relying on a tele-operated guidance to create a map of the environment will result in a huge and inefficient workload. Thus, an autonomous exploration algorithm is required for a mobile robot to complete the exploration. The work proposes an autonomous exploration and mapping method for a tracked-robot based on the information gained from a LiDAR sensor, the motion cost, and a grid map that represents the built map for navigation and localization. The project is created from scratch, that means that some previous work has to be done before implementing and testing the algorithm on the robot. The previous work carries out a simultaneous localization and mapping while searching for interesting objects that may modify the navigation of the robot, the navigation planner that generates trajectories for the robot and the kinematic model of the tracked-robot. Once all those steps are implemented, the experiments can be done on the real robot, and the exploration strategy is tested both in the real robot and in the simulator. All the project has been implemented using ROS.