Authors:
Anıl Doğru
1
;
Amin Deldari Alamdari
1
;
Duru Balpınarlı
2
and
Reyhan Aydoğan
3
;
4
;
1
Affiliations:
1
Department of Computer Science, Özyeğin University, İstanbul, Turkey
;
2
Department of Industrial Engineering, Özyeğin University, İstanbul, Turkey
;
3
Department of Artificial Intelligence and Data Engineering, Özyeğin University, İstanbul, Turkey
;
4
Delft University of Technology, Delft, The Netherlands
Keyword(s):
Multiagent Path-Finding, Uncertainty, Ant Colony Optimization, Consensus.
Abstract:
This paper introduces a novel Dynamic and Partially Observable Multiagent Path-Finding (DPO-MAPF) problem and presents a multitier solution approach accordingly. Unlike traditional MAPF problems with static obstacles, DPO-MAPF involves dynamically moving obstacles that are partially observable and exhibit unpredictable behavior. Our multitier solution approach combines centralized planning with decentralized execution. In the first tier, we apply state-of-the-art centralized and offline path planning techniques to navigate around static, known obstacles (e.g., walls, buildings, mountains). In the second tier, we propose a decentralized and online conflict resolution mechanism to handle the uncertainties introduced by partially observable and dynamically moving obstacles (e.g., humans, vehicles, animals, and so on). This resolution employs a metaheuristic-based revision process guided by a consensus protocol to ensure fair and efficient path allocation among agents. Extensive simulati
ons validate the proposed framework, demonstrating its effectiveness in finding valid solutions while ensuring fairness and adaptability in dynamic and uncertain environments.
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