自主多机器人月球挖掘应用
Autonomous Multirobot Excavation for Lunar Applications
摘要：
在本文中，一种控制方法称为人工神经组织（ANT）被应用到多机器人挖掘月球基地的准备任务，包括清除着陆垫和掩埋的栖息地模块。我们首次展示了一组自主机器人挖掘地形以匹配给定的3D蓝图。在着陆台周围建造土墩将在发射/着陆期间提供物理屏蔽，使其免受碎片的影响。将人类居住舱埋在0.5米深的月壤之下，预计既能提供辐射屏蔽，又能保持-25 $^{o}$ C的温度。这使基本生命支持的复杂性最小化，并减少了发射质量。ANT对于月球任务来说是引人注目的，因为它不需要一队宇航员进行挖掘，而且需要最少的监督。机器人团队可以自主解释蓝图，挖掘和准备月球基地的场地。由于几乎没有提供预先编程的知识，控制器发现创造性的技术。蚂蚁进化技术，如槽打瞌睡，否则将需要挖掘专家。当派遣宇航员的费用高得令人望而却步时，这对于使挖掘任务可行至关重要。控制者进化出复杂的谈判行为，以便在近距离内工作。这些和其他技术，如控制器和团队规模的并发进化，以解决对抗的问题，当太多的机器人干扰降低整体效率或更糟，导致僵局。虽然这项技术仍然存在许多挑战，但我们的工作为现场测试这种方法提供了一条引人注目的途径。
英文摘要：
In this paper, a control approach called Artificial Neural Tissue (ANT) is applied to multirobot excavation for lunar base preparation tasks including clearing landing pads and burying of habitat modules. We show for the first time, a team of autonomous robots excavating a terrain to match a given 3D blueprint. Constructing mounds around landing pads will provide physical shielding from debris during launch/landing. Burying a human habitat modules under 0.5 m of lunar regolith is expected to provide both radiation shielding and maintain temperatures of -25 $^{o}$C. This minimizes base life-support complexity and reduces launch mass. ANT is compelling for a lunar mission because it doesn't require a team of astronauts for excavation and it requires minimal supervision. The robot teams are shown to autonomously interpret blueprints, excavate and prepare sites for a lunar base. Because little pre-programmed knowledge is provided, the controllers discover creative techniques. ANT evolves techniques such as slot-dozing that would otherwise require excavation experts. This is critical in making an excavation mission feasible when it is prohibitively expensive to send astronauts. The controllers evolve elaborate negotiation behaviors to work in close quarters. These and other techniques such as concurrent evolution of the controller and team size are shown to tackle problem of antagonism, when too many robots interfere reducing the overall efficiency or worse, resulting in gridlock. While many challenges remain with this technology our work shows a compelling pathway for field testing this approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
