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

Journal Article · · Autonomous Robots
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)
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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.

Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC52-06NA25396
OSTI ID:
1459630
Report Number(s):
LA-UR-16-27817
Journal Information:
Autonomous Robots, Vol. 42, Issue 8; ISSN 0929-5593
Publisher:
SpringerCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 2 works
Citation information provided by
Web of Science

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Cited By (1)

Planning Under Non-Rational Perception of Uncertain Spatial Costs journal April 2021

Figures / Tables (17)

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