Robots are ideal for open-pit mining on the Moon as its a dull, dirty, and dangerous task. The challenge is to scale up productivity with an ever-increasing number of robots. This paper presents a novel method for developing scalable controllers for use in multi-robot excavation and site-preparation scenarios. The controller starts with a blank slate and does not require human-authored operations scripts nor detailed modeling of the kinematics and dynamics of the excavator. The 'Artificial Neural Tissue' (ANT) architecture is used as a control system for autonomous robot teams to perform resource gathering. This control architecture combines a variable-topology neural-network structure with a coarse-coding strategy that permits specialized areas to develop in the tissue. Our work in this field shows that fleets of autonomous decentralized robots have an optimal operating density. Too few robots result in insufficient labor, while too many robots cause antagonism, where the robots undo each other's work and are stuck in gridlock. In this paper, we explore the use of templates and task cues to improve group performance further and minimize antagonism. Our results show light beacons and task cues are effective in sparking new and innovative solutions at improving robot performance when placed under stressful situations such as severe time-constraint.

中文翻译：

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多机器人控制器和任务线索的协同进化，用于离世界露天采矿

机器人非常适合在月球上进行露天采矿，因为这是一项枯燥、肮脏且危险的任务。面临的挑战是通过越来越多的机器人来提高生产力。本文提出了一种用于开发用于多机器人挖掘和现场准备场景的可扩展控制器的新方法。控制器从一张白纸开始，不需要人工编写的操作脚本，也不需要对挖掘机的运动学和动力学进行详细建模。“人工神经组织”(ANT) 架构用作自主机器人团队执行资源收集的控制系统。这种控制架构将可变拓扑神经网络结构与粗编码策略相结合，允许在组织中形成专门的区域。我们在该领域的工作表明，自主分散的机器人车队具有最佳的操作密度。机器人太少导致劳动力不足，机器人太多导致对抗，机器人相互取消工作，陷入僵局。在本文中，我们探索使用模板和任务线索来进一步提高团队绩效并最大限度地减少对抗。我们的结果表明，当置于压力情况（例如严重的时间限制）下时，灯标和任务提示可有效激发新的创新解决方案，以提高机器人的性能。我们探索使用模板和任务线索来进一步提高团队绩效并最大限度地减少对抗。我们的结果表明，当置于压力情况（例如严重的时间限制）下时，灯标和任务提示可有效激发新的创新解决方案，以提高机器人的性能。我们探索使用模板和任务线索来进一步提高团队绩效并最大限度地减少对抗。我们的结果表明，当置于压力情况（例如严重的时间限制）下时，灯标和任务提示可有效激发新的创新解决方案，以提高机器人的性能。