Decentralized and Asymmetric Multi-Agent Learning in Construction Sites
14 October 2024 Multi-agent collaboration involves multiple participants working together in a shared environment to achieve a common goal. These agents share information, divide tasks, and synchronize their actions. Key aspects of multi agent collaboration include coordination, communication, task...
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Zusammenfassung: | 14 October 2024 Multi-agent collaboration involves multiple participants working together in
a shared environment to achieve a common goal. These agents share information,
divide tasks, and synchronize their actions. Key aspects of multi agent
collaboration include coordination, communication, task allocation,
cooperation, adaptation, and decentralization. On construction sites, surface
grading is the process of leveling sand piles to increase a specific area's
height. In this scenario, a bulldozer grades while a dumper allocates sand
piles. Our work aims to utilize a multi-agent approach to enable these vehicles
to collaborate effectively. To this end, we propose a decentralized and
asymmetric multi-agent learning approach for construction sites (DAMALCS). We
formulate DAMALCS to reduce expected collisions for operating vehicles.
Therefore, we develop two heuristic experts capable of achieving their joint
goal optimally by applying an innovative prioritization method. In this
approach, the bulldozer's movements take precedence over the dumper's
operations, enabling the bulldozer to clear the path for the dumper and ensure
continuous operation of both vehicles. Since heuristics alone are insufficient
in real-world scenarios, we utilize them to train AI agents, which proves to be
highly effective. We simultaneously train the bulldozer and dumper agents to
operate within the same environment, aiming to avoid collisions and optimize
performance in terms of time efficiency and sand volume handling. Our trained
agents and heuristics are evaluated in both simulation and real-world lab
experiments, testing them under various conditions, such as visual noise and
localization errors. The results demonstrate that our approach significantly
reduces collision rates for these vehicles. |
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DOI: | 10.48550/arxiv.2409.10375 |