| This thesis describes an approach for cooperative multi-robot localization based on probabilistic method (Monte Carlo Localization) used in assistant robots which are capable of sensing and communicating one with another. In our approach, each of the robots maintains its own clustering based MCL algorithm, and communicates with each other whenever it detects another robot. We develop a new information exchange mechanism, which makes use of the information extracted from the clustering component, to synchronize the beliefs of detected robots. By avoiding unnecessary information exchange whenever detection occurs through a belief comparison, our approach can solve the delayed integration problem to improve the effectiveness and efficiency of multi-robot localization. This approach has been tested in both real and simulated environments. Compared with single robot localization, the experimental results demonstrate that our approach can notably improve the performance, especially when the environments are highly symmetric. |
