论文标题
多机器人控制器和off World开放坑挖掘的任务提示的共同发展
Co-Evolution of Multi-Robot Controllers and Task Cues for Off-World Open Pit Mining
论文作者
论文摘要
机器人是在月球上开放式矿井开采的理想选择，因为它是一项沉闷，肮脏和危险的任务。面临的挑战是通过数量不断增加的机器人来扩展生产率。本文提出了一种用于开发可扩展控制器的新方法，用于用于多机器人发掘和场地准备情况。控制器以空白的板岩开头，不需要人为撰写的操作脚本，也不需要对挖掘机的运动学和动力学的详细建模。 “人造神经组织”（ANT）结构用作自主机器人团队进行资源收集的控制系统。该控制结构将可变的神经网络结构与粗编码策略相结合，该策略允许在组织中发展专业区域。我们在该领域的工作表明，自动分散机器人的车队具有最佳的工作密度。机器人太少会导致人工不足，而太多的机器人会引起对抗，在这种机器人中，机器人互相撤消了彼此的工作，并且陷入了僵局。在本文中，我们探讨了模板和任务提示的使用，以进一步提高群体绩效并最大程度地减少拮抗作用。我们的结果表明，轻型信标和任务提示在引发新的和创新的解决方案方面有效，以改善机器人性能，例如在诸如严重的时间限制之类的压力下。
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.
