基于机器学习和信息期望值的可快速部署移动传感器机器人动态布局
Dynamic Placement of Rapidly Deployable Mobile Sensor Robots Using Machine Learning and Expected Value of Information
摘要：
虽然工业物联网增加了工业工厂中永久安装的传感器数量，但由于传感器损坏或密度稀疏，在非常大的工厂中（如石化行业），覆盖范围会有差距。现代应急响应行动开始使用小型无人机系统（sUAS），这些系统能够将传感器机器人投放到精确位置。sUAS可以提供无人机无法提供的长期持续监测。尽管这些资产的成本相对较低，但在紧急响应期间，在复杂的工厂环境中选择将哪些机器人传感系统部署到工业过程的哪个部分仍然具有挑战性。本文介绍了一个框架，用于优化部署的紧急传感器，实现机器人在灾难情况下的响应能力的初步步骤。人工智能技术（长短期记忆、一维卷积神经网络、逻辑回归和随机森林）识别传感器最有价值的区域，而无需人类进入潜在危险区域。在所描述的案例研究中，用于优化的代价函数考虑了假阳性和假阴性错误的代价。缓解措施的决定包括实施维修或关闭工厂。信息期望值（EVI）用于识别最有价值的物理传感器类型和位置，以增加传感器网络的决策分析价值。这种方法被应用到一个案例研究，使用田纳西州伊士曼化工厂的过程数据集，我们讨论了我们的研究结果的影响，在工厂的紧急情况和恢复能力的情况下，传感器的操作，分配和决策。
英文摘要：
Although the Industrial Internet of Things has increased the number of sensors permanently installed in industrial plants, there will be gaps in coverage due to broken sensors or sparse density in very large plants, such as in the petrochemical industry. Modern emergency response operations are beginning to use Small Unmanned Aerial Systems (sUAS) that have the ability to drop sensor robots to precise locations. sUAS can provide longer-term persistent monitoring that aerial drones are unable to provide. Despite the relatively low cost of these assets, the choice of which robotic sensing systems to deploy to which part of an industrial process in a complex plant environment during emergency response remains challenging. This paper describes a framework for optimizing the deployment of emergency sensors as a preliminary step towards realizing the responsiveness of robots in disaster circumstances. AI techniques (Long short-term memory, 1-dimensional convolutional neural network, logistic regression, and random forest) identify regions where sensors would be most valued without requiring humans to enter the potentially dangerous area. In the case study described, the cost function for optimization considers costs of false-positive and false-negative errors. Decisions on mitigation include implementing repairs or shutting down the plant. The Expected Value of Information (EVI) is used to identify the most valuable type and location of physical sensors to be deployed to increase the decision-analytic value of a sensor network. This method is applied to a case study using the Tennessee Eastman process data set of a chemical plant, and we discuss implications of our findings for operation, distribution, and decision-making of sensors in plant emergency and resilience scenarios.
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。
