AiRobSim: Simulating a Multisensor Aerial Robot for Urban Search and Rescue Operation and Training
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 sp...
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2020-09-01
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doaj-019daa22316a475195b1ea373aa17c5b2020-11-25T03:35:50ZengMDPI AGSensors1424-82202020-09-01205223522310.3390/s20185223AiRobSim: Simulating a Multisensor Aerial Robot for Urban Search and Rescue Operation and TrainingJunjie Chen0Shuai Li1Donghai Liu2Xueping Li3Department of Civil and Environmental Engineering, The University of Tennessee, Knoxville, TN 37902, USADepartment of Civil and Environmental Engineering, The University of Tennessee, Knoxville, TN 37902, USAState Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300350, ChinaDepartment of Industrial and Systems Engineering, The University of Tennessee, Knoxville, TN 37902, USAUnmanned 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.https://www.mdpi.com/1424-8220/20/18/5223situational awarenesssearch and rescuerobot simulationground-penetrating radarunmanned aerial vehicletraining |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Junjie Chen Shuai Li Donghai Liu Xueping Li |
spellingShingle |
Junjie Chen Shuai Li Donghai Liu Xueping Li AiRobSim: Simulating a Multisensor Aerial Robot for Urban Search and Rescue Operation and Training Sensors situational awareness search and rescue robot simulation ground-penetrating radar unmanned aerial vehicle training |
author_facet |
Junjie Chen Shuai Li Donghai Liu Xueping Li |
author_sort |
Junjie Chen |
title |
AiRobSim: Simulating a Multisensor Aerial Robot for Urban Search and Rescue Operation and Training |
title_short |
AiRobSim: Simulating a Multisensor Aerial Robot for Urban Search and Rescue Operation and Training |
title_full |
AiRobSim: Simulating a Multisensor Aerial Robot for Urban Search and Rescue Operation and Training |
title_fullStr |
AiRobSim: Simulating a Multisensor Aerial Robot for Urban Search and Rescue Operation and Training |
title_full_unstemmed |
AiRobSim: Simulating a Multisensor Aerial Robot for Urban Search and Rescue Operation and Training |
title_sort |
airobsim: simulating a multisensor aerial robot for urban search and rescue operation and training |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-09-01 |
description |
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. |
topic |
situational awareness search and rescue robot simulation ground-penetrating radar unmanned aerial vehicle training |
url |
https://www.mdpi.com/1424-8220/20/18/5223 |
work_keys_str_mv |
AT junjiechen airobsimsimulatingamultisensoraerialrobotforurbansearchandrescueoperationandtraining AT shuaili airobsimsimulatingamultisensoraerialrobotforurbansearchandrescueoperationandtraining AT donghailiu airobsimsimulatingamultisensoraerialrobotforurbansearchandrescueoperationandtraining AT xuepingli airobsimsimulatingamultisensoraerialrobotforurbansearchandrescueoperationandtraining |
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