Dynamic Heterogeneous Multi-Robot Team Management in Dangerous Domains

在危险的环境中工作，如城市搜索和救援(USAR，灾后建筑物的勘探和测绘，搜索受害者和协助救灾)对智能机器人团队来说是一个极端的挑战。世界是复杂和不可预测的，机器人可能被损坏或摧毁，通信因基础设施受损而受到干扰。需要由各种类型的机器人组成的团队(异类团队)，因为损失单位所涉及的风险可以通过数量更多、更具消耗性的个人来更好地摊销。在这种情况下，团队必须随时围绕可用的个人组合动态地组建和重组，利用可用的技能做有用的工作，积极寻找新的个人来补充技能，并在可行的程度上利用受损的个人(包括在可能的情况下协助这些人)。个人同样必须适应损伤(例如，如果不再活动，则使用抓取的四肢)，并尽可能地与团队合作。在所有这一切中，风险必须与为团队执行任何任务的价值进行积极的平衡，并且这些任务都必须在快速变化和有限的沟通下完成。 这里提出的研究计划将推进危险环境中动态异质机器人团队工作的最新水平，整合和扩展所有上述主题。在这些主题上已经单独完成了工作(例如，关于更换团队、招募代理的任务分配)，但支持像USAR这样危险领域的广泛机器人集合的可部署的分散解决方案仍然难以捉摸。在最近的工作中，我和我的学生为此目的开发了一个框架，包括在改变团队成员的情况下完成任务，适应机器人的损失和招募新的个人。我们还一直致力于改进机器人控制和复杂领域的规划。我将利用这一经验更广泛地推进动态的异质团队合作，包括最大限度地管理受损机器人、帮助受损机器人、平衡任务风险与价值，以及制定团队战略来管理风险等重大问题。其结果将是一个软件系统，它将在现实世界中使用机器人团队进行物理演示。我们将探索类人机器人的使用，特别是在这些条件下的团队，因为这种形式的性质支持广泛的活动，尽管受损(例如，类人机器人可以爬行或拖拽自己，如果不能行走)。这项工作适用于USAR以外的广泛危险领域，如采矿、空间探索和国防。 这项拟议的研究将为4名博士生和9名硕士学生提供人工智能、复杂软件开发和机器人技术方面的宝贵培训，这些技能是工业界至关重要的技能。

Work in dangerous environments such as Urban Search and Rescue (USAR, the post-disaster exploration and mapping of structures, searching for victims and aiding disaster response) represents an extreme challenge to teams of intelligent robots. The world is complex and unpredictable, robots can be damaged or destroyed, and communication is interfered with due to damaged infrastructure. Teams consisting of various types of robots (heterogeneous teams) are required, because the risk involved in losing units can be better amortized with larger numbers of more expendable individuals. Under such conditions, teams must form and reorganize dynamically around the mix of individuals available at any time, doing useful work with the skills available, actively looking for new individuals to supplement skills, and making use of damaged individuals to the degree practicable (including assisting these if possible). Individuals must similarly adapt to damage (e.g. make use of grasping limbs if no longer mobile) and work with a team as well as possible. Permeating all of this, risk must be actively balanced with the value of carrying out any task for the team, and these tasks must all be accomplished under rapid change and limited communication. The program of research proposed here will advance the state of the art in dynamic heterogeneous robotic teamwork in dangerous environments, incorporating and extending all of the above themes. Work has been done in these themes individually (e.g. task allocation on changing teams, recruiting agents) but a deployable decentralized solution supporting a broad collection of robots for a domain as dangerous as USAR is still elusive. In recent work my students and I have developed a framework for this purpose that includes accomplishing tasks under changing team membership, adapting to robot loss and recruiting new individuals. We have also been working on improved robot control and planning for complex domains. I will be leveraging this experience to more broadly advance dynamic heterogeneous teamwork, encompassing major issues such as managing damaged robots to the best extent possible, assisting damaged robots, balancing task risk vs. value, and developing team strategies to manage risk. The outcome of this will be a software system that will be physically demonstrated using teams of robots in the real world. We will be exploring the use of humanoid robots in particular on teams under these conditions, since the nature of this form supports a broad range of activities despite damage (e.g. humanoids can crawl or drag themselves if unable to walk). This work is applicable in a broad range of dangerous domains beyond USAR, such as mining, space exploration, and defense. The proposed research will provide valuable training for four PhD students and nine MSc students in artificial intelligence, complex software development, and robotics: skills that are vitally needed by industry.