Action selection for active and cooperative global localization based on localizability estimation

In this paper we investigate the action selection problem for multiple mobile robots active and cooperative global localization in large environments. Firstly, an off-line localizability estimation approach using known probabilistic grid map (PGM) is briefly proposed and a localizability matrix is presented to describe properties of expected localization probability distribution (LPD). Based on the proposed localizability matrix, a novel on-line action selection mechanism is further presented which enables mobile robots to actively collaborate with each other and select complementary actions with redundant information exclusion. This action selection mechanism brings the lowest expected localization covariance in each localization cycle and can accelerate the converging speed of global localization. Efficiency in time complexity also ensures that the proposed method could be used in real applications. The appropriateness of our approach is demonstrated through simulations and real experiments.