Resilient Active Information Gathering with Mobile Robots
Resilient Active Information Gathering with Mobile Robots
复制标题
使用移动机器人进行弹性主动信息收集
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
George Pappas
中科院分区：
文献类型：
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作者：
Brent Schlotfeldt;Vasileios Tzoumas;Dinesh Thakur;George Pappas
机器人技术中的安全、安保和救援应用（例如多机器人目标跟踪）涉及移动机器人团队执行信息获取任务。然而，在容易发生故障或对抗性的环境中，机器人会受到攻击，其通信通道会被堵塞，传感器可能会发生故障，导致机器人从集体任务中退出，从而导致剩余的活跃机器人无法相互协调。因此，传统的设计范式变得不够，相反，针对系统范围的故障和攻击的弹性设计变得很重要。一般来说，弹性设计问题很困难，尽管它们通常涉及单调或子模的目标函数，但迄今为止其解决方案的可扩展近似算法仍然未知。在本文中，我们提供了第一个算法，实现了以下功能： 最小通信，即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，即该算法对于任意数量的拒绝服务攻击和故障都有效；以及可证明的近似性能，即该算法确保所有单调（不一定是子模）目标函数的解决方案有限地接近最优。我们使用单调集函数的曲率概念来量化我们的算法近似性能。通过考虑主动信息收集场景（即多机器人目标跟踪），我们通过模拟和现实实验来支持我们的理论分析。
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 algorithms 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.