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Title: Dynamic Placement of Rapidly Deployable Mobile Sensor Robots Using Machine Learning and Expected Value of Information
The Industrial Internet of Things has increased the number of sensors permanently installed in industrial plants. Yet there will be gaps in coverage due to broken sensors or sparce density in very large plants, such as in the petrochemical industry. Modern emergency response operations are beginning to use Small Unmanned Aerial Systems (sUAS) as remote sensors to provide rapid improved situational awareness. Ground-based sensors are an integral component of overall situational awareness platforms, as they can provide longer-term persistent monitoring that aerial drones are unable to provide. Squishy Robotics and the Berkeley Emergent Space Tensegrities Laboratory have developed hardware and a framework for rapidly deploying sensor robots for integrated ground-aerial disaster response. The semi-autonomous delivery of sensors using tensegrity (tension-integrity) robotics uses structures that are flexible, lightweight, and have high stiffness-to-weight ratios, making them ideal candidates for robust high-altitude deployments. Squishy Robotics has developed a tensegrity robot for commercial use in Hazardous Materials (HazMat) scenarios that is capable of being deployed from commercial drones or other aircraft. Squishy Robots have been successfully deployed with a delicate sensing and communication payload of up to 1,000 ft. This paper describes the framework for optimizing the deployment of emergency sensors spatially over time. AI more » techniques (e.g., Long Short-Term Memory neural networks) identify regions where sensors would be most valued without requiring humans to enter the potentially dangerous area. The cost function for optimization considers costs of false-positive and false-negative errors. Decisions on mitigation include shutting down the plant or evacuating the local community. 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. A case study using data from the Tennessee Eastman process dataset of a chemical plant displayed in OSI Soft is provided. « less
Authors:
Award ID(s):
1927010
Publication Date:
NSF-PAR ID:
10317188
Journal Name:
ASME 2021 International Mechanical Congress Exposition (IMECE 2021
Sponsoring Org:
National Science Foundation
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