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

Aya Kherrour, Marco Robol, Marco Roveri, Paolo Giorgini
10/3/2023
cs.MAcs.RO

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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.

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Cite this paper

@article{kherrour2023evaluating,
  title  = {Evaluating Heuristic Search Algorithms in Pathfinding: A Comprehensive Study on Performance Metrics and Domain Parameters},
  author = {Aya Kherrour and Marco Robol and Marco Roveri and Paolo Giorgini},
  year   = {2023},
  eprint = {2310.02346},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.02346v1}
}

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