Paper Info
Title
Swarm EKF localization for a multiple robot system with range-only measurements
Abstract
Swarm localization, cooperative robot localization in swarm robotics, has a significant role in a swarm robot system and requires much deliberation for its estimation scheme. As such, designing stochastic hidden Markov model, in a way a variety of conditionally dependent, observed random variables such as measurements are effectively chosen and properly integrated into the probability distribution of a belief, is very important. In this paper, we propose swarm EKF localization, a hybrid of two inference algorithms, extended Kalman filter (EKF) and belief propagation (BP), with a capability of choosing how many dependencies of random variables are exploited in inference using the concept of neighborhood. Also, this paper presents a numerical experiment result of swarm EKF localizations. In conclusion, we could confirm that 2nd order neighborhood EKF has an overall better estimation performance compared to conventional 1st order neighborhood EKFs.
Year
DOI
Venue
2013
10.1109/SII.2013.6776751
System Integration
Keywords
Field
DocType
Kalman filters,belief networks,control engineering computing,estimation theory,hidden Markov models,inference mechanisms,mobile robots,multi-robot systems,nonlinear filters,random processes,statistical distributions,2nd order neighborhood EKF,BP,belief propagation,cooperative robot localization,estimation performance,estimation scheme,extended Kalman filter,inference algorithms,multiple robot system,probability distribution,random variables,range-only measurements,stochastic hidden Markov model,swarm EKF localization,swarm robot system
Extended Kalman filter,Swarm behaviour,Stochastic process,Kalman filter,Artificial intelligence,Hidden Markov model,Mathematics,Mobile robot,Swarm robotics,Belief propagation
Conference
Volume
ISSN
Citations 
269
2194-5357
0
PageRank 
References 
Authors
0.34
3
2
Name
Order
Citations
PageRank
Shigekazu Fukui100.34
Keitaro Naruse24719.98