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research-article

Distributed Bayesian Filter using Measurement Dissemination for Multiple UGVs with Dynamically Changing Interaction Topologies

[+] Author and Article Information
Chang Liu

Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA 94720 U.S.
changliu@berkeley.edu

Shengbo Li

Department of Automotive Engineering, State Key Lab of Automotive Safety and Energy, Tsinghua University, Beijing 100084 China
lisb04@gmail.com

Diange Yang

Department of Automotive Engineering, State Key Lab of Automotive Safety and Energy, Tsinghua University, Beijing 100084 China
ydg@tsinghua.edu.cn

J. Karl Hedrick

Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA 94720 U.S.
khedrick@me.berkeley.edu

1Corresponding author.

ASME doi:10.1115/1.4037779 History: Received February 14, 2017; Revised June 16, 2017

Abstract

This paper presents a novel distributed Bayesian filtering (DFB) method using measurement dissemination for multiple unmanned ground vehicles (UGVs) with dynamically changing interaction topologies. Different from statistics dissemination-based algorithms that transmit posterior distributions or likelihood functions, this method relies on a full-in-full-out (FIFO) transmission protocol, which significantly reduces the transmission burden between each pair of UGVs. Each UGV only sends a communication buffer and a track list to its neighbors, in which the former contains a history of measurements from all UGVs, and the latter trims the used measurements in the buffer to reduce communication overhead. It is proved that each UGV can disseminate its measurements over the whole network within the finite time and is able to achieve the consistency of environmental state estimation. The effectiveness of this method is validated by comparing with consensus-based distributed filters and centralized filter in a multi-target tracking problem.

Copyright (c) 2017 by ASME
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