Multiple vehicle Bayesian-based domain search with intermittent information sharing

This paper focuses on the development of Bayesian-based domain search strategies for distributed multiple autonomous vehicles with intermittent information sharing. Multi-sensor fusion based on observations from neighboring vehicles is implemented via binary Bayesian filtering. We will prove that, under appropriate sensor models, the belief of whether objects exist or not will converge to the true state. An uncertainty map based on these probabilities is constructed to guide the vehicles´ motion. It will be shown that all objects in the search domain will be detected. Different motion control schemes are numerically tested to illustrate the effectiveness of the proposed strategy.