Minimum Path Star Topology Algorithms for Weighted Regions and Obstacles

TL;DR

This paper introduces new algorithms for finding minimum path star topologies in weighted regions with obstacles, improving cost-effective electrical system design by accounting for geological features.

Contribution

It proposes novel algorithms that discretize space and combine existing methods to handle weighted regions and obstacles, filling gaps in current shortest path solutions.

Findings

Algorithms effectively manage weighted regions and obstacles.

Bounding with convex hull improves algorithm efficiency.

New methods achieve more accurate minimum path estimations.

Abstract

Shortest path algorithms have played a key role in the past century, paving the way for modern day GPS systems to find optimal routes along static systems in fractions of a second. One application of these algorithms includes optimizing the total distance of power lines (specifically in star topological configurations). Due to the relevancy of discovering well-connected electrical systems in certain areas, finding a minimum path that is able to account for geological features would have far-reaching consequences in lowering the cost of electric power transmission. We initialize our research by proving the convex hull as an effective bounding mechanism for star topological minimum path algorithms. Building off this bounding, we propose novel algorithms to manage certain cases that lack existing methods (weighted regions and obstacles) by discretizing Euclidean space into squares and combining pre-existing algorithms that calculate local minimums that we believe have a possibility of being the absolute minimum. We further designate ways to evaluate iterations necessary to reach some level of accuracy. Both of these novel algorithms fulfill certain niches that past literature does not cover.