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A Tracking Algorithm for Autonomous Navigation of AGVs: Federated Information Filter
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 Title & Authors
A Tracking Algorithm for Autonomous Navigation of AGVs: Federated Information Filter
Kim, Yong-Shik; Hong, Keum-Shik;
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 Abstract
In this paper, a tracking algorithm for autonomous navigation of automated guided vehicles (AGVs) operating in container terminals is presented. The developed navigation algorithm takes the form of a federated information filter used to detect other AGVs and avoid obstacles using fused information from multiple sensors. Being equivalent to the Kalman filter (KF) algebraically, the information filter is extended to N-sensor distributed dynamic systems. In multi-sensor environments, the information-based filter is easier to decentralize, initialize, and fuse than a KF-based filter. It is proved that the information state and the information matrix of the suggested filter, which are weighted in terms of an information sharing factor, are equal to those of a centralized information filter under the regular conditions. Numerical examples using Monte Carlo simulation are provided to compare the centralized information filter and the proposed one.
 Keywords
State estimation;Kalman filter;information filter;sensor fusion;federated filter;automated guided vehicle;
 Language
English
 Cited by
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