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Paper TuD110.2

Xu, Zhe (The University of Sydney), Fitch, Robert (University of Sydney), Sukkarieh, Salah (University of Sydney)

Learning Utility Models for Decentralised Coordinated Target Tracking

Scheduled for presentation during the Interactive Session "Interactive Session TuD-1" (TuD110), Tuesday, May 15, 2012, 16:30−17:00, Ballroom D

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on October 15, 2018

Keywords Sensor Networks, Distributed Robot Systems, Networked Robots

Abstract

In decentralised target tracking, a set of sensors observes moving targets. When the sensors are static but steerable, each sensor must dynamically choose which target to observe in a decentralised manner. We show that the information exchanged by the sensors to synchronise their beliefs can be exploited to learn a model of the utility function that drives each others' decisions. Instead of communicating utilities to enable negotiation, each sensor regresses on the learnt model to predict the utilities of other team members. This approach bridges the gap between coordinating implicitly, a locally-greedy solution, and negotiating explicitly. We validated our approach in both hardware and simulations, and found that it out-performed implicit coordination by a statistically significant margin with both ideal and limited communications.

 

 

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