Ultra-wide-band-based adaptive Monte Carlo localization for kidnap recovery of mobile robot

Abstract

In the article, a global localization algorithm based on improved ultra-wide-band-based adaptive Monte Carlo localization is proposed for quick and robust kidnap recovery of mobile robot. First, two ultra-wide-band modules, the tag installed inside the mobile robot and the anchor installed inside charging station, are used to obtain the relative distance between the mobile robot and the charging station. Second, the global grid map is converted into a map with obstacle noise given the ranging accuracy of the ultra-wide-band modules with different obstacles. Third, while the robot is kidnapped, matching grids are screened based on the range information of ultra-wide-band modules and the obstacle noise of the grids. Finally, global localization algorithm is performed based on ultra-wide-band-based adaptive Monte Carlo localization to convert randomly generated particles from the whole map into randomly generated particles in the local map. Experimental results based on gazebo simulation and a real scenario showed that our global localization algorithm based on improved ultra-wide-band-based adaptive Monte Carlo localization not only significantly helped to improve the chances of the robot global pose recovery from lost or kidnapped state but also enabled the robot kidnap recovery with a smaller number of randomly generated particles, thus reducing the time to recover its accurate global localization. The algorithm was also more effective especially for kidnap recovery in a similar and large scenario.