Distributed Bayesian Filter Using Measurement Dissemination for Multiple Unmanned Ground Vehicles With Dynamically Changing Interaction Topologies

[+] Author and Article Information
Chang Liu

Department of Mechanical Engineering,
University of California, Berkeley,
Berkeley, CA 94720
e-mail: changliu@berkeley.edu

Shengbo Eben Li

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

Diange Yang

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

J. Karl Hedrick

Department of Mechanical Engineering,
University of California, Berkeley,
Berkeley, CA 94720
e-mail: khedrick@me.berkeley.edu

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received February 14, 2017; final manuscript received June 16, 2017; published online November 8, 2017. Assoc. Editor: Prashant Mehta.

J. Dyn. Sys., Meas., Control 140(3), 030903 (Nov 08, 2017) (11 pages) Paper No: DS-17-1086; doi: 10.1115/1.4037779 History: Received February 14, 2017; Revised June 16, 2017

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

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Fig. 1

Target tracking scenario. The interaction topology is dynamically changing and UGVs can only communicate with neighboring UGVs.

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Fig. 2

Example of FIFO with three UGVs under dynamically changing interaction topologies. The arrows represent a directed communication link between two UGVs. ø denotes the empty set. For the purpose of clarity, we only show measurements, not the states, in the CB.

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Fig. 3

Example of FIFO-DBF for the first UGV at time k. Only the measurement (not the state) is shown in the figure. The UGV first calculates Ptmp1(Xt+1). Since the UGV has received all UGVs' measurements of t+1, the Ptmp1(Xt+1) is assigned as the new stored PDF. The dashed arrow on the right shows the order to fuse measurements in the CB.

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Fig. 4

Example of updating TLs. For the first UGV's TL, the jth(j∈V) entry on the ith(i∈V) row represents this UGV's knowledge about whether the ith UGV has received the jth UGV's state-measurement pair of time k1i, where k1i is the last entry of the ith row. TLs are updated using Algorithm 3.

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Fig. 5

The dynamically changing interaction topologies used in the simulation: (a)–(d) four types of topologies; (e) the union of these topologies is jointly strongly connected. The bottom axis shows a randomly generated sequence of topologies that satisfy the frequently jointly strongly connectedness condition.

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Fig. 6

Evolution of the first UGV's target estimation using FIFO-DBF. The colorful background represents the sum of the individual PDF of three targets. (a) FIFO-DBF at step 3, (b) FIFO-DBF at step 5, (c) FIFO-DBF at step 7, (d) FIFO-DBF at step 10, (e) FIFO-DBF at step 20, and (f) FIFO-DBF at step 40.

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Fig. 7

The first UGV's target estimation using (a) CbDF and (b) CF. The estimation uncertainty remains large for CbDF: (a) CbDF at step 40 and (b) CF at step 40.

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Fig. 8

The average estimation error (a), (c), (e) and the average entropy (b), (d), (f) of ten trials using different filtering approaches. The dotted lines correspond to the results of FIFO-DBF by the six UGVs (“U1”–“U6” in the legends). (a) position error of target 1, (b) entropy of target 1, (c) position error of target 2, (d) entropy of target 2, (e) position error of target 3, and (f) entropy of target 3.



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