Decentralized Information-Rich Planning and Hybrid Sensor Fusion for Uncertainty Reduction in Human-Robot Missions

摘要

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. I.