This paper considers Bayesian data fusion with categorical 'soft sensor' information, such as human input in cooperative multi-agent search applications. Previous work studied variational Bayesian (VB) hybrid data fusion, which produces optimistic posterior covariance estimates and requires simple Gaussian priors with softmax likelihoods. Here, a new hybrid fusion procedure, known as variational Bayesian importance sampling (VBIS), is introduced to combine the strengths of VB and fast Monte Carlo methods to produce more reliable Gaussian posterior approximations for Gaussian priors and softmax likelihoods. VBIS is then generalized to problems involving complex Gaussian mixture priors and multimodal softmax observation models to obtain reliable Gaussian mixture posterior approximations. The utility and accuracy of the VBIS fusion method is demonstrated on a multitarget search problem for a real cooperative human-automaton team.
This paper introduces a novel planning and estimation framework for maximizing information collection in missions involving cooperative teams of multiple autonomous vehicles and human agents, such as those used for multi-target search and tracking. The main contribution of this work is the scalable unification of effective algorithms for distributed high-level task planning, decentralized information-based trajectory planning, and hybrid Bayesian information fusion through a common Gaussian mixture uncertainty representation, which can accommodate multiple mission objectives and constraints as well as heterogeneous human/robot information sources. The proposed framework is validated with promising results on real hardware through a set of experiments involving a human-robot team performing a multi-target search mission.
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