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Title: Limited range spatial load balancing in non-convex environments using sampling-based motion planners

Abstract

This paper analyzes the limited range, spatial load balancing problem for agents deployed in non-convex environments and subject to differential constraints which restricts how the agents can move. First, the (unlimited range) spatial load balancing problem is introduced and the minimization problem with area constraints is defined. Then, to extend the problem for limited ranges, two cost functions and a sub-partition are defined. The problems are then analyzed and the results prove the existence of a partition that satisfies the area constraints. The non-convex environment makes the problem difficult to solve in continuous-space. Therefore, a probabilistic roadmap is used to approximate agents’ cells via a graph. A distributed algorithm is proven to converge to an approximate solution. Lastly, the convergence of the algorithm is shown in simulation.

Authors:
ORCiD logo [1]; ORCiD logo [1];  [2]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Univ. of California, San Diego, La Jolla, CA (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1459630
Report Number(s):
LA-UR-16-27817
Journal ID: ISSN 0929-5593
Grant/Contract Number:  
AC52-06NA25396
Resource Type:
Accepted Manuscript
Journal Name:
Autonomous Robots
Additional Journal Information:
Journal Volume: 42; Journal Issue: 8; Journal ID: ISSN 0929-5593
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; Motion Planning; Coverage; Load Balancing; Robotics

Citation Formats

Boardman, Beth Leigh, Harden, Troy Anthony, and Martinez, Sonia. Limited range spatial load balancing in non-convex environments using sampling-based motion planners. United States: N. p., 2018. Web. doi:10.1007/s10514-018-9713-x.
Boardman, Beth Leigh, Harden, Troy Anthony, & Martinez, Sonia. Limited range spatial load balancing in non-convex environments using sampling-based motion planners. United States. doi:10.1007/s10514-018-9713-x.
Boardman, Beth Leigh, Harden, Troy Anthony, and Martinez, Sonia. Mon . "Limited range spatial load balancing in non-convex environments using sampling-based motion planners". United States. doi:10.1007/s10514-018-9713-x. https://www.osti.gov/servlets/purl/1459630.
@article{osti_1459630,
title = {Limited range spatial load balancing in non-convex environments using sampling-based motion planners},
author = {Boardman, Beth Leigh and Harden, Troy Anthony and Martinez, Sonia},
abstractNote = {This paper analyzes the limited range, spatial load balancing problem for agents deployed in non-convex environments and subject to differential constraints which restricts how the agents can move. First, the (unlimited range) spatial load balancing problem is introduced and the minimization problem with area constraints is defined. Then, to extend the problem for limited ranges, two cost functions and a sub-partition are defined. The problems are then analyzed and the results prove the existence of a partition that satisfies the area constraints. The non-convex environment makes the problem difficult to solve in continuous-space. Therefore, a probabilistic roadmap is used to approximate agents’ cells via a graph. A distributed algorithm is proven to converge to an approximate solution. Lastly, the convergence of the algorithm is shown in simulation.},
doi = {10.1007/s10514-018-9713-x},
journal = {Autonomous Robots},
number = 8,
volume = 42,
place = {United States},
year = {2018},
month = {2}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Figures / Tables:

Fig. 1 Fig. 1 : An example of a limited range sub-partition for four agents, each with a different ω$i$.

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