Adapted Conflict Detection for Conflict Based Search

Avgi Kollakidou, Leon Bodenhagen

2024

Abstract

Mobile robots are increasingly deployed in various applications, including autonomous vehicles and logistics. Conflict-Based Search (CBS) is a promising approach for Multi-Agent Path Finding (MAPF), but has limitations when applied to real-world scenarios. This paper explores the challenges of adapting CBS to real-world mobile robotics, focusing on additional conflicts caused by imperfect navigation. We propose an Adaptive Conflict Detection (ACD) approach that proactively identifies conflicts within a rolling time window, making CBS more suitable for real-world applications. Both virtual and real robots are used to evaluate the importance of an adaptation to CBS if adapted to real scenarios. Experimental results show that ACD outperforms traditional CBS when penalties for conflict resolution are applied, demonstrating its potential for improved performance and reliability in practical multi-agent path planning applications.

Paper Citation

in Harvard Style

Kollakidou A. and Bodenhagen L. (2024). Adapted Conflict Detection for Conflict Based Search. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 367-373. DOI: 10.5220/0012438000003636

in Bibtex Style

@conference{icaart24,
author={Avgi Kollakidou and Leon Bodenhagen},
title={Adapted Conflict Detection for Conflict Based Search},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2024},
pages={367-373},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012438000003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Adapted Conflict Detection for Conflict Based Search
SN - 978-989-758-680-4
AU - Kollakidou A.
AU - Bodenhagen L.
PY - 2024
SP - 367
EP - 373
DO - 10.5220/0012438000003636
PB - SciTePress