Rapid fault-recovery strategies for resilient robot swarms

机器人正越来越成为我们日常生活的重要组成部分，它可以自动执行任务，例如保持房屋清洁以及在大型仓库拾取/包装包裹。人口老龄化以及在危险和重复性任务中替代人类工人的需求现在已经导致了新的任务即将出现（例如，农业自动化和环境监测），要求我们的机器人做更多的工作，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，并有效地执行其使命。然而，迄今为止，我们的机器人群还没有做好部署的准备;无法处理操作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。本项目的目标是通过开发算法来补救这种情况，使机器人群能够在几分钟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是有限的。它们被限制为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。众多的机器人在一个群和大量的复杂的方式，他们可以相互作用，很难预测潜在的故障和故障恢复策略，这可以解释为什么没有现有的故障检测和故障诊断系统已扩展到提供故障恢复机制的机器人群。因此，为了设计容错算法的机器人群体，我们需要超越传统的方法依赖于故障诊断信息的故障恢复。故障恢复在机器人群体中，而不是制定为一个在线的行为适应过程。通过这种方法，群中的机器人通过试错法学习新的补偿行为来适应持续的故障，尽管有故障。然而，目前的方法来学习新的机器人群体行为是耗时的，需要几个小时。因此，这种方法是不合适的行为自适应（学习新的群体行为）快速故障恢复。有效的故障恢复的行为自适应需要机器人群体创造性地和快速地在线学习新的补偿性群体行为，即使持续的故障，有效地从故障中恢复群体。该提案将通过研究数据高效的机器学习技术来解决这些要求，以快速在线行为适应，并以创造性和自动生成的直觉为指导-离线进化-工作群体行为。由此产生的系统将对机器人群的长期运作产生重大影响，并为其部署开辟新的有趣的应用，例如使用一群自主水面车辆监测大型水体的污染物。

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.Fault recovery in a robot swarm may instead be formulated as an online behavior-adaptation process. With such an approach, the robots of the swarm adapt their behavior to sustained faults by learning via trial-and-error new compensatory behaviors that work despite the faults. However, the current approaches to learning new robot swarm behaviors are time-consuming, requiring several hours. Therefore, such approaches are inappropriate for behavior adaptation (learning new swarm behaviors) for rapid 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 proposal 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 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.