An Empirical Approach for Tuning an Autonomous Mobile Robot in Gazebo

This paper mainly focuses on simulation of an autonomous mobile robot (AMR) based on the robot operating system (ROS) simulated in Gazebo in a Laboratory scene finding the shortest path and hence traversing the coordinate-based checkpoints in the quickest time possible. ROS is an open-source software framework mainly used for software development for robots. Sensing and perception are done by using sensors like light detection and ranging (LIDAR), depth camera, inertial measurement unit (IMU), and encoders. Global planner and Local planner are responsible for determining the path that the AMR will take and traveling in the scenario without any collisions. Simultaneous localization and mapping (SLAM) are launched in a node and for localization adaptive Monte Carlo localization (AMCL) is used. The robot in the simulated environment can be used in real life applications like autonomous cleaner, final stage delivery robots. The ultimate goal is to travel from point to point in the quickest time possible without any crashes.