Bayesian-Based Decision-Making for Object Search and Classification

This paper focuses on the development of real-time decision-making criteria for an autonomous vehicle whose tasks to be performed are competing under limited sensory resources. More specifically, we are interested in the search and classification of multiple static objects of unknown number and positions given a single autonomous sensor vehicle. In this case, search and classification are two competing demands since an autonomous vehicle can perform either task but not both at the same time. During a search task, once the vehicle finds an object of interest, it has to decide on whether to neglect the found object and continue searching, or stop and observe that object. If the vehicle decides to stop and classify the object, it has to decide on how much time it can afford to do so. This is a very critical decision as choosing one option over the other may mean missing other, more important objects not yet found, or missing the opportunity to satisfactorily classify a found critical object. Building on previous deterministic work by the authors, in this paper we develop Bayesian-based probabilistic and information-theoretic search versus classification decision-making criteria that result in guaranteed detection and classification of all the unknown objects in the domain. Simulation-based results are provided to study the performance of the proposed decision-making strategy.