Self-Balancing Robot Navigation


Human activity has been increasing, to support the activity, people in the modern era create robots to replace some human activities. The interest in two-wheeled balance robots has continued to increase, this is because it highly maneuverable, making it efficient for use in various areas. In this study, a two-wheeled balance robot was used, with a navigation system using a robot operating system (ROS). In the navigation, the adaptive Monte Carlo localization is used. The robot can move to all points on the map with selected points on the map and then the robot will immediately move, or by providing coordinate points via command terminal. The results of the tracking simulation show that the robot can move from the starting point to the destination point in either a straight or a curved path. At straight path, the average error is -0.094 and 0 for X and Y axis, respectively. Whereas in curve path the average error is -0.052 and -0.05 for X and Y axis, respectively. The results show that tracking simulations can also be used to track real robots. This is possible by sending the speed data of the simulated robot to the real robot.