In this paper, scalable collaborative human-robot systems for information gathering applications are approached as a decentralized Bayesian sensor network problem. Humancomputer augmented nodes and autonomous mobile sensor platforms are collaborating on a peer-to-peer basis by sharing information via wireless communication network. For each node, a computer (onboard the platform or carried by the human) implements both a decentralized Bayesian data fusion algorithm and a decentralized Bayesian control negotiation algorithm. The individual node controllers iteratively negotiate anonymously with each other in the information space to find cooperative search plans based on both observed and predicted information that explicitly consider the platforms (humans and robots) motion models, their sensors detection functions, as well as the target arbitrary motion model. The results of a collaborative multi-target search experiment conducted with a team of four autonomous mobile sensor platforms and five humans carrying small portable computers with wireless communication are presented to demonstrate the efficiency of the approach. I. INTRODUCTIONThis paper proposes an innovative scalable Bayesian approach for coordinating a network of humans and robots involved in information gathering type missions. The concept of a meta-node, to represent mobile robotic sensors and human-computer augmented systems, is introduced as a fundamental building block of a decentralized Active Sensor Network (ASN) architecture which couples decentralized communication, estimation and control.In this approach the human-computer augmented node constitute a mobile sensor unit where the human provides both the sensors and their carrying "platform", while the portable computer runs both a decentralized Bayesian fusion node and a decentralized Bayesian control negotiation algorithm. The networked controller nodes iteratively negotiate anonymously in the information space to find cooperative search plans based on both observed and predicted information that explicitly consider the humans and robots motion models, their sensors detection functions, as well as the targets arbitrary motion model. This type of decentralized architecture offers increased efficiency, reactivity, robustness and scalability by avoiding the overheads, bottlenecks and single points of failure associated with centralized structures. The proposed methodology enables synergistic human-machine interactions, with applications search and rescue, planetary exploration, mapping,
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