Rapid behaviour adaptation for resilient robot swarms

摘要：机器人正日益成为我们日常生活的重要组成部分，它们将一些任务自动化，比如保持家里的清洁，在大型仓库里挑选/包装包裹。人口老龄化以及在危险和重复性任务中取代人类工人的需求现在导致了新的任务（例如，农业自动化和环境监测），要求我们的机器人做更多的事情，作为群体（大型机器人团队）的一部分大量工作，在广阔的区域协调感知和行动，并有效地执行任务。然而，到目前为止，我们的机器人群还没有做好部署的准备；由于无法处理在运行过程中不可避免的损坏和故障，它们仍然是脆弱的系统，在困难的条件下停止工作。这个项目的目标是通过开发算法来纠正这种情况，使机器人群能够在不超过几分钟的时间内迅速从故障和损坏中恢复过来。现有的机器人群容错系统存在一定的局限性。它们只能诊断设计者先验预测的故障，这很难涵盖机器人群在复杂环境中长时间运行时可能遇到的所有可能情况。蜂群中的众多机器人以及它们之间相互作用的大量复杂方式使得预测潜在故障和预先定义相应的恢复策略变得困难；这也许可以解释为什么现有的故障检测和故障诊断系统都没有被扩展到为机器人群提供故障恢复机制。因此，为了设计机器人群体的容错算法，我们需要超越传统的依赖故障诊断信息进行故障恢复的方法。为有效的故障恢复而进行的行为适应要求机器人群体创造性地、快速地在线学习新的补偿群体行为，这些行为在持续故障的情况下仍然有效地从故障中恢复群体。该项目将通过研究数据高效的机器学习技术来满足这些需求，以创造性和自动生成的直觉（离线进化的）工作群体行为为指导，快速适应在线行为。由此产生的系统将对海洋机器人群的长期运行产生重大影响，并为它们的部署开辟新的和有趣的应用，例如使用一群自主水面车辆监测大片水体的污染物。

Summary: Robots are increasingly becoming an important part of our day-to-day lives, automating tasks such as keeping our homes clean, and picking/packing our parcels at large warehouses. An aging population and the need to substitute human workers in dangerous and repetitive tasks have now resulted in new tasks on the horizon (e.g., in agriculture automation and environmental monitoring), requiring our robots to do more, to work in large-numbers as part of a swarm (a large team of robots), to coordinately sense and act over vast areas, and efficiently perform their mission. However, our robot swarms to date are unprepared for deployment; unable to deal with the inevitable damages and faults sustained during operation, they remain frail systems that cease functioning in difficult conditions. The goal of this project is to remedy this situation by developing algorithms for robot swarms to rapidly -- in no more than a few minutes -- recover from faults and damages sustained by robots of the swarm.The existing fault-tolerant systems for robot swarms are limited. They are constrained to only diagnose faults anticipated a priori by the designer, which can hardly encompass all the possible scenarios a robot swarm may encounter while operating in complex environments for extended periods of time. The multitude of robots in a swarm and the large number of intricate ways they can interact with each other makes it difficult to predict potential faults and predefine corresponding recovery strategies; which may explain why none of the existing fault-detection and fault-diagnosis systems have been extended to provide fault-recovery mechanisms for robot swarms. Therefore, in order to design fault-tolerant algorithms for robot swarms, we need to move beyond the traditional approaches relying on fault-diagnosis information for fault recovery.Behavior adaptation for effective fault recovery requires the robot swarm to creatively and rapidly learn new compensatory swarm behaviors online, that work despite the sustained faults, effectively recovering the swarm from the faults. The project will address these requirements by investigating data-efficient machine learning techniques for rapid online behavior adaptation, guided by creatively and automatically generated intuitions -- evolved offline -- of working swarm behaviors. The resulting system would have a significant impact on long-term operations of marine robot swarms, and open up new and interesting applications for their deployment, such as the monitoring of large bodies of water for pollutants using a swarm of autonomous surface vehicles.