arXiv 1803.097302018-03cs.ROcs.MAmath.OCstat.APstat.ML

利用移动机器人进行弹性主动信息收集

Resilient Active Information Gathering with Mobile Robots

University of Pennsylvania（宾夕法尼亚大学）；Massachusetts Institute of Technology（麻省理工学院）

Brent Schlotfeldt, Vasileios Tzoumas, Dinesh Thakur, George J. Pappas

摘要：

机器人技术中的安全、安保和救援应用，如多机器人目标跟踪，涉及由移动机器人团队执行信息采集任务。然而，在容易发生故障或敌对的环境中，机器人会受到攻击，它们的通信信道会被堵塞，它们的传感器可能会发生故障，导致机器人从集体任务中退出，从而使剩余的主动机器人无法相互协调。因此，传统的设计范式变得不足，相比之下，针对系统范围故障和攻击的弹性设计变得重要。在一般情况下，弹性设计问题是很难的，即使他们经常涉及的目标函数是单调的或submodular，可扩展的近似算法，他们的解决方案一直是未知的。在本文中，我们提供了第一个算法，使以下能力：最小的通信，即，该算法由机器人仅基于它们之间的最小通信来执行;系统范围的弹性，即，该算法对于任何数量的拒绝服务攻击和故障都是有效的;以及可证明的近似性能，即，该算法确保对于所有单调（并且不一定是次模）目标函数，都有一个非常接近最优的解。我们量化我们的算法的近似性能，使用单调集函数的曲率的概念。我们支持我们的理论分析与模拟和现实世界的实验，通过考虑一个积极的信息收集的情况下，即多机器人目标跟踪。

英文摘要：

Applications of safety, security, and rescue in robotics, such as multi-robot target tracking, involve the execution of information acquisition tasks by teams of mobile robots. However, in failure-prone or adversarial environments, robots get attacked, their communication channels get jammed, and their sensors may fail, resulting in the withdrawal of robots from the collective task, and consequently the inability of the remaining active robots to coordinate with each other. As a result, traditional design paradigms become insufficient and, in contrast, resilient designs against system-wide failures and attacks become important. In general, resilient design problems are hard, and even though they often involve objective functions that are monotone or submodular, scalable approximation algorithms for their solution have been hitherto unknown. In this paper, we provide the first algorithm, enabling the following capabilities: minimal communication, i.e., the algorithm is executed by the robots based only on minimal communication between them; system-wide resiliency, i.e., the algorithm is valid for any number of denial-of-service attacks and failures; and provable approximation performance, i.e., the algorithm ensures for all monotone (and not necessarily submodular) objective functions a solution that is finitely close to the optimal. We quantify our algorithm's approximation performance using a notion of curvature for monotone set functions. We support our theoretical analyses with simulated and real-world experiments, by considering an active information gathering scenario, namely, multi-robot target tracking.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

摘要：

英文摘要：

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