Partition Learning for Multiagent Planning
Joint Authors
Source
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-09-13
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Automated surveillance of large geographic areas and target tracking by a team of autonomous agents is a topic that has received significant research and development effort.
The standard approach is to decompose this problem into two steps.
The first step is target track estimation and the second step is path planning by optimizing directly over target track estimation.
This standard approach works well in many scenarios.
However, an improved approach is needed for the scenario when general, nonparametric estimation is required, and the number of targets is unknown.
The focus of this paper is to present a new approach that inherently handles the task to search for and track an unknown number of targets within a large geographic area.
This approach is designed for the case when the search is performed by a team of autonomous agents and target estimation requires general, nonparametric methods.
There are consequently very few assumptions made.
The only assumption made is that a time-changing target track estimation is available and shared between the agents.
This estimation is allowed to be general and nonparametric.
Results are provided that compare the performance of this new approach with the standard approach.
From these results it is concluded that this new approach improves search and tracking when the number of targets is unknown and target track estimation is general and nonparametric.
American Psychological Association (APA)
Wood, Jared& Hedrick, J. Karl. 2012. Partition Learning for Multiagent Planning. Journal of Robotics،Vol. 2012, no. 2012, pp.1-14.
https://search.emarefa.net/detail/BIM-483239
Modern Language Association (MLA)
Wood, Jared& Hedrick, J. Karl. Partition Learning for Multiagent Planning. Journal of Robotics No. 2012 (2012), pp.1-14.
https://search.emarefa.net/detail/BIM-483239
American Medical Association (AMA)
Wood, Jared& Hedrick, J. Karl. Partition Learning for Multiagent Planning. Journal of Robotics. 2012. Vol. 2012, no. 2012, pp.1-14.
https://search.emarefa.net/detail/BIM-483239
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-483239