Evaluating Heuristic Search Algorithms in Pathfinding: A Comprehensive Study on Performance Metrics and Domain Parameters

TL;DR

This study evaluates various heuristic search algorithms in pathfinding for autonomous systems, analyzing how domain parameters affect their performance, and proposes a selection method for optimal algorithm choice based on problem features.

Contribution

It provides a comprehensive comparison of heuristic search algorithms across different pathfinding scenarios and introduces a selection algorithm for choosing the best search method based on problem parameters.

Findings

Performance varies with domain size and obstacle density.

Certain algorithms outperform others depending on problem characteristics.

The proposed selection algorithm improves search efficiency by choosing suitable algorithms.

Abstract

The paper presents a comprehensive performance evaluation of some heuristic search algorithms in the context of autonomous systems and robotics. The objective of the study is to evaluate and compare the performance of different search algorithms in different problem settings on the pathfinding domain. Experiments give us insight into the behavior of the evaluated heuristic search algorithms, over the variation of different parameters: domain size, obstacle density, and distance between the start and the goal states. Results are then used to design a selection algorithm that, on the basis of problem characteristics, suggests the best search algorithm to use.