skip to main content


This content will become publicly available on February 19, 2019

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

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.
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:
Report Number(s):
Journal ID: ISSN 0929-5593
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Autonomous Robots
Additional Journal Information:
Journal Name: Autonomous Robots; Journal ID: ISSN 0929-5593
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
Country of Publication:
United States
42 ENGINEERING; Motion Planning; Coverage; Load Balancing; Robotics; Spatial load balancing; Limited range; Multi-agent coverage
OSTI Identifier: