建筑工地上的分散和非对称多代理学习

IF 5.3 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Yakov Miron;Dan Navon;Yuval Goldfracht;Dotan Di Castro;Itzik Klein
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引用次数: 0

摘要

多代理协作涉及多个参与者在共享环境中共同工作,以实现共同目标。这些代理共享信息、分工合作并同步行动。多代理协作的主要方面包括协调、沟通、任务分配、合作、适应和分散。在建筑工地上,地表平整是平整沙堆以增加特定区域高度的过程。在这里,推土机负责平整,而翻斗车则负责分配沙堆。我们的工作旨在利用多代理方法使这些车辆有效协作。为此,我们提出了一种用于建筑工地的分散式非对称多代理学习方法(DAMALCS)。我们制定 DAMALCS 的目的是减少作业车辆的预期碰撞。因此,我们开发了两个启发式专家,通过应用创新的优先级排序方法,能够以最佳方式实现它们的共同目标。在这种方法中,推土机的行动优先于翻斗车的行动。这样,推土机就能为翻斗车开辟道路,并确保两辆车的连续运行。在实际应用中,仅靠启发式方法是不够的,因此我们利用启发式方法来训练人工智能代理,事实证明这种方法非常有效。我们同时训练推土机和翻斗车代理在同一环境中运行,目的是避免碰撞,并在时间效率和沙量处理方面优化性能。我们在模拟和实际实验室实验中对训练有素的代理和启发式方法进行了评估,在视觉噪音和定位误差等各种条件下对其进行了测试。结果表明,我们的方法大大降低了这些车辆的碰撞率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Decentralized and Asymmetric Multi-Agent Learning in Construction Sites
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. There, 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. This enables the dozer to clear the path for the dumper and ensure continuous operation of both vehicles. As heuristics alone are insufficient in real-world scenarios, we utilize them to train AI agents, which proves to be highly effective. We simultaneously train dozer and dumper agents to operate within the same environment, aiming to avoid collisions and optimizing 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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来源期刊
CiteScore
9.60
自引率
0.00%
发文量
25
审稿时长
10 weeks
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