Abstract:
This work proposes a novel method for collaborative global localization of a team of soccer playing autonomous robots. It is also applicable to other indoor real-time robot applications in noisy, unpredictable environments, with insufficient perception. A novel solution, Reverse Monte Carlo Localization (R-MCL) is designed to solve single self-localization problem using local perception and action about the surrounding environment for each robot. R-MCL is a hybrid method based on Markov Localization (ML) and Monte Carlo Localization (MCL) where the ML based part finds the region where the robot should be and the MCL based part predicts the geometrical location with high precision by selecting samples in this region. In the multi-robot localization problem, robots use their own local position estimations, and the shared information from other team mates, to localize themselves. To integrate the local information and beliefs optimally, avoid conflicts and support collaboration among team members, a novel collaborative multi-robot localization method called Collaborative Reverse Monte Carlo Localization (CR-MCL), based on R-MCL, is presented. When robots detect each other, they share the grid cells representing this observation. The power of the method comes from its hybrid nature. It uses a grid based approach to handle detections which can not be accurate in real-time applications, and sample based approach in self-localization to improve its success, although it uses lower amount of samples compared to similar methods. Both methods are tested using simulated robots and real robots and results show that they are fast, robust, accurate and cheap in terms of communication, memory and computational costs.
