Sensor planning for mobile robot localization using Bayesian network representation and inference

We propose a novel method to solve a kidnapped robot problem. A mobile robot plans its sensor actions to localize itself using Bayesian network inference. The system differs from traditional methods such as the simple Bayesian decision or top-down action selection based on a decision tree. In contrast, we represent the contextual relation between the local sensing results and beliefs about the global localization using Bayesian networks. Inference of the Bayesian network allows us to classify ambiguous positions of the mobile robot when the local sensing evidences are obtained. By taking into account the trade-off between the global localization belief degree and local sensing cost, we define an integrated utility function to decide the local sensing range, and obtain an optimal sensing plan and optimal Bayesian network structure based on this function. We conducted simulation and real robot experiments to validate our planning concept.