Scalable Bayesian human-robot cooperation in mobile sensor networks

— 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 plat...

• Type: Conference
• Year: 2008
• Where: IROS
• Authors: Frédéric Bourgault, Aakash Chokshi, John Wang, Danelle Shah, Jonathan R. Schoenberg, Ramnath Iyer, Franco Cedano, Mark Campbell
• Keywords: Autonomous Mobile Sensor, Decentralized Bayesian Sensor, IROS 2008, Mobile Sensor Platforms, Robotics