Abstract
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.
Highlights
After natural and man-made disasters, rapid search and rescue (SaR) operations need to be conducted to save surviving victims [1,2]
Fixed-wing Unmanned aerial vehicles (UAVs) are normally used at the initial survey stage due to the merits of fast moving speed and large sensing coverage [7,16,17], while mini helicopters and multi-rotors are being used for near-ground search and indoor application [3,18,19] because they are easy to operate and can hover at a fixed point
And effective search and rescue (SaR) operations in urban environments are crucial for mitigating fatalities brought by natural or manmade disasters
Summary
After natural and man-made disasters, rapid search and rescue (SaR) operations need to be conducted to save surviving victims [1,2]. In urban areas, the unstructured, occluded and complex disaster site poses challenges and risks to first responders during SaR operations. Effective and safe urban SaR operations, robots are being developed to assist first responders by offering unprecedented situational awareness about disaster areas. This study devised an aerial SaR robot by the integration of UAV and GPR to obtain both the above-rubble and below-rubble information in urban environments, and developed a simulation framework, AiRobSim, for robot testing and user training in various disaster scenarios. The hybrid use of UAV and GPR is adopted in urban search and rescue to provide first responders with holistic situation awareness of the occluded, and complex urban disaster areas, with which critical void locations that may contain surviving victims can be pinpointed. An interface was developed for interactive human–robot communication and control, which enables novice users to quickly acquaint themselves with knowledge and skills in operating the robot
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