Unmanned aerial vehicles (UAVs), equipped with a variety of sensors, are being used to provide actionable information to augment first responders’ situational awareness in disaster areas for urban search and rescue (SaR) operations. However, existing aerial robots are unable to sense the occluded spaces in collapsed structures, and voids buried in disaster rubble that may contain victims. In this study, we developed a framework, AiRobSim, to simulate an aerial robot to acquire both aboveground and underground information for post-disaster SaR. The integration of UAV, ground-penetrating radar (GPR), and other sensors, such as global navigation satellite system (GNSS), inertial measurement unit (IMU), and cameras, enables the aerial robot to provide a holistic view of the complex urban disaster areas. The robot-collected data can help locate critical spaces under the rubble to save trapped victims. The simulation framework can serve as a virtual training platform for novice users to control and operate the robot before actual deployment. Data streams provided by the platform, which include maneuver commands, robot states and environmental information, have potential to facilitate the understanding of the decision-making process in urban SaR and the training of future intelligent SaR robots.
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 »
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- ASME 2021 International Mechanical Congress Exposition (IMECE 2021
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- National Science Foundation
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