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

Beth Boardman, Troy Harden, and Sonia Martínez
Autonomous Robots, 42 (2018) 1731-1748

Abstract:

This paper analyzes a limited range, spatial load balancing problem for agents deployed in non-convex environments and subject to differential constraints. First, the (unlimited range) spatial load balancing problem is introduced and the minimization problem subject to 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, with results proving the existence of a partition that satisfies the area constraints. The non-convex environment make the problem difficult to solve in continuous space, therefore, a probabilistic roadmap is used to approximate agents' cells via sets of vertices and edges. A distributed algorithm is proven to converge to an approximate solution. Simulations confirm the convergence of the algorithms.

File: (local copy)

Bib-tex entry:

@article{BB-TH-SM:18-ar,
author = {B. Boardman and T. Harden and S. Mart{\'\i}nez},
title = {Limited range spatial load balancing in non-convex environments using sampling based motion planners},
journal= {Autonomous Robots},
year = {2018},
volume = {42},
pages = {1731--1748}
}