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Rescue Robots for the Urban Earthquake Environment

Published online by Cambridge University Press:  30 June 2022

Fuhao Li Open the ORCID record for Fuhao Li [Opens in a new window] ,
Shike Hou ,
Chunguang Bu  and
Bo Qu Open the ORCID record for Bo Qu [Opens in a new window]
Fuhao Li
Affiliation:
Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China
Shike Hou
Affiliation:
Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China
Chunguang Bu
Affiliation:
Robotics Laboratory, Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang, Liaoning, China
Bo Qu*
Affiliation:
Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China
*
Corresponding author: Bo Qu, Email: [email protected]
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Abstract

Robotics have important applications in the field of disaster medical rescue. The deployment of urban rescue robots at the earthquake site can help shorten response time, improve rescue efficiency and keep rescue personnel away from danger. This discussion introduces the performance of some robots in actual rescue scenarios, focuses on the current research status of robots that can provide medical assistance, and analyzes the merits and shortcomings of each system. Based on existing studies, the limitations and development directions of urban rescue robots are also discussed.

Keywords

earthquakesrescue robotsfield diagnosisinjured evacuation
Type
Concepts in Disaster Medicine
Information
Disaster Medicine and Public Health Preparedness , Volume 17 , 2023 , e181
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Society for Disaster Medicine and Public Health, Inc.

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References

Li, YC, Zhang, JH, Yu, GD. A scenario-based hybrid robust and stochastic approach for joint planning of relief logistics and casualty distribution considering secondary disasters. Transport Res E-Log. 2020;141:102029.CrossRefGoogle Scholar
Akbari, ME, Farshad, AA, Asadi-Lari, M. The devastation of Bam: an overview of health issues 1 month after the earthquake. Public Health. 2004;118(6):403-408.CrossRefGoogle ScholarPubMed
Zhang, Q, Sun, XY, Tong, F, et al. A review of intelligent control algorithms applied to robot motion control. In: 2018 IEEE 8th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). Tianjin: IEEE. 2018:105-109.CrossRefGoogle Scholar
Campbell, S, Niall O’Mahony, N, Carvalho, A, et al. Path planning techniques for mobile robots: a review. In: 2020 6th International Conference on Mechatronics and Robotics Engineering (ICMRE). Barcelona: IEEE, 2020:12-16.CrossRefGoogle Scholar
Blackburn, MR, Everett, HR, Laird, RT. After action report to the joint program office: Center for the Robotic Assisted Search and Rescue (CRASAR) related efforts at the World Trade Center. 2002. Accessed May 7, 2021. https://www.hsdl.org/?view&did=713309 CrossRefGoogle Scholar
Matsuno, F, Sato, N, Kon, K, et al. Utilization of robot systems in disaster sites of the Great Eastern Japan Earthquake. Field and Service Robotics: Results of the 8th International Conference. Berlin, Heidelberg: Springer. 2014;92:1-17.CrossRefGoogle Scholar
Rohmer, E, Ohno, K, Yoshida, T, et al. Integration of a sub-crawlers’ autonomous control in Quince highly mobile rescue robot. In: 2010 IEEE/SICE International Symposium on System Integration. Sendai: IEEE. 2010:78-83.CrossRefGoogle Scholar
Nagatani, K, Kiribayashi, S, Okada, Y, et al. Gamma-ray irradiation test of electric components of rescue mobile robot Quince. In: 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics. Kyoto: IEEE, 2011:56-60.CrossRefGoogle Scholar
Nagatani, K, Kiribayashi, S, Okada, Y, et al. Redesign of rescue mobile robot Quince. In: 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics. Kyoto: IEEE. 2011:13-18.CrossRefGoogle Scholar
Kruijff, GJM, Pirri, F, Gianni, M, et al. Rescue robots at earthquake-hit Mirandola, Italy: a field report. In: 2012 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). College Station, TX: IEEE. 2012:1-8.Google Scholar
Kruijff-Korbayová, I, Colas, F, Gianni, M, et al. TRADR Project: long-term human-robot teaming for robot assisted disaster response. Künstl Intell. 2015;29(2):193-201.CrossRefGoogle Scholar
Kruijff-Korbayová, I, Freda, L, Gianni, M, et al. Deployment of ground and aerial robots in earthquake-struck Amatrice in Italy (brief report). In: 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). Lausanne: IEEE. 2016:278-279.CrossRefGoogle Scholar
Wright, C, Buchan, A, Brown, B, et al. Design and architecture of the unified modular snake robot. In: 2012 IEEE International Conference on Robotics and Automation. Saint Paul: IEEE. 2012:4347-4354.CrossRefGoogle Scholar
Rollinson, D, Choset, H. Gait-based compliant control for snake robots. In: 2013 IEEE International Conference on Robotics and Automation. Karlsruhe: IEEE. 2013:5138-5143.CrossRefGoogle Scholar
Rollinson, D, Bilgen, Y, Brown, B, et al. Design and architecture of a series elastic snake robot. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. Chicago: IEEE. 2014:4630-4636.CrossRefGoogle Scholar
Whitman, J, Zevallos, N, Travers, M, et al. Snake robot urban search after the 2017 Mexico City Earthquake. In: 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). Philadelphia, PA: IEEE. 2018:1-6.Google Scholar
Yamada, S, Hirose, S, Endo, G, et al. R-Crank: amphibious all terrain mobile robot. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Daejeon: IEEE. 2016:1067-1072.CrossRefGoogle Scholar
Klamt, T, Behnke, S. Anytime hybrid driving-stepping locomotion planning. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems. Vancouver: IEEE. 2017:4444-4451.Google Scholar
Kebritchi, A, Havashinezhadian, S, Rostami, M. Design and experimental development of hexapod robot with fiberglass-fibercarbon composite legs. In: 2018 6th RSI International Conference on Robotics and Mechatronics (IcRoM). Tehran: IEEE. 2018:439-444.CrossRefGoogle Scholar
Sanfilippo, F, Helgerud, E, Stadheim, PA. Serpens, a low-cost snake robot with series elastic torque-controlled actuators and a screw-less assembly mechanism. In: 2019 5th International Conference on Control, Automation and Robotics (ICCAR). Beijing: IEEE. 2019:133-139.CrossRefGoogle Scholar
Recchiuto, CT, Sgorbissa, A. Post-disaster assessment with unmanned aerial vehicles: a survey on practical implementations and research approaches. J Field Robot. 2018;35(4):459-490.CrossRefGoogle Scholar
Delmerico, J, Mintchev, S, Giusti, A, et al. The current state and future outlook of rescue robotics. J Field Robot. 2019;36(7):1171-1191.CrossRefGoogle Scholar
Pretto, EA, Barach, P. Principles and practice of disaster medicine: what every anesthesiologist should know about responding to medical disasters. Adv Anesth. 2008;26:175-199.CrossRefGoogle Scholar
Wuthisuthimethawee, P, Lindquist, SJ, Sandler, N, et al. Wound management in disaster settings. World J Surg. 2015;39(4):842-853.CrossRefGoogle ScholarPubMed
Blaivas, M, Sargsyan, AE, Karakitsos, D. Tactical emergency medicine, procedures, and point-of-care evaluation in austere environments. Trauma Team Dynamics. Cham: Springer. 2016:233-236.CrossRefGoogle Scholar
Ito, K, Sugano, S, Iwata, H. Wearable echography robot for trauma patient. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. Taipei: IEEE. 2010:4794-4799.CrossRefGoogle Scholar
Ito, K, Tsuruta, K, Sugano, S, et al. Evaluation of a wearable tele-echography robot system: FASTele in a vehicle using a mobile network. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Boston, MA: IEEE. 2011:2093-2096.CrossRefGoogle Scholar
Ito, K, Sugano, S, Iwata, H. Internal bleeding detection algorithm based on determination of organ boundary by low-brightness set analysis. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. Vila Moura: IEEE. 2012:4131-4136.CrossRefGoogle Scholar
Ito, K, Sugano, S, Iwata, H. Organ boundary determination algorithm for detecting internal bleeding. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. San Diego, CA: IEEE. 2012:3183-3186.CrossRefGoogle Scholar
Suthakorn, J, Shah, SSH, Jantarajit, S, et al. On the design and development of a rough terrain robot for rescue missions. In: 2008 IEEE International Conference on Robotics and Biomimetics. Bangkok: IEEE. 2009:1830-1835.CrossRefGoogle Scholar
Treratanakulchai, S, Suthakorn, J. Effective vital sign sensing algorithm and system for autonomous survivor detection in rough-terrain autonomous rescue robots. In: 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014). Bali: IEEE. 2014:831-836.CrossRefGoogle Scholar
Phuengsuk, R, Suthakorn, J. A study on risk assessment for improving reliability of rescue robots. In: 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO 2016). Qingdao: IEEE. 2016:667-672.CrossRefGoogle Scholar
Theobald, D. Mobile extraction-assist robot. US Patent 20080265821 A1. 2008.Google Scholar
Wen, J, Sun, X, Shi, Y, et al. Patient influx and trauma types in a front-line hospital and a secondary referral hospital after the Wenchuan earthquake: a retrospectively comparative study. Eur J Trauma Emerg Surg. 2012;38(3):261-267.CrossRefGoogle Scholar
Bar-On, E, Lebel, E, Kreiss, Y, et al. Orthopaedic management in a mega mass casualty situation. The Israel Defence Forces Field Hospital in Haiti following the January 2010 earthquake. Injury. 2011;42(10):1053-1059.CrossRefGoogle Scholar
Awais, S, Saeed, A. Study of the severity of musculoskeletal injuries and triage during the 2005 Pakistan earthquake. Int Orthop (SICOT). 2013;37(8):1443-1447.CrossRefGoogle ScholarPubMed
Kang, P, Tang, BH, Liu, Y, et al. Profile and procedures for fractures among 1323 fracture patients from the 2010 Yushu earthquake, China. Am J Emerg Med. 2016;34(11):2132-2139.CrossRefGoogle ScholarPubMed
Bar-On, E, Blumberg, N, Joshi, A, et al. Orthopedic activity in field hospitals following earthquakes in Nepal and Haiti. World J Surg. 2016;40(9):2117-2122.CrossRefGoogle ScholarPubMed
Williams, A, Sebastian, B, Ben-Tzvi, P. Review and analysis of search, extraction, evacuation, and medical field treatment robots. J Intell Robot Syst. 2019;96(3):401-418.CrossRefGoogle Scholar
Sebastian, B, Ben-Tzvi, P. Physics based path planning for autonomous tracked vehicle in challenging terrain. J Intell Robot Syst. 2019;95(2):511-526.CrossRefGoogle Scholar
Sebastian, B, Williams, A, Ben-Tzvi, P. Control of a head stabilization system for use in robotic disaster response. In: ASME 2017 International Mechanical Engineering Congress and Exposition. Tampa, Florida. USA: ASME. 2017. 4A: Dynamics, Vibration, and Control.Google Scholar
Sahashi, T, Sahshi, A, Uchiyama, H, et al. Study and development of the rescue robot to accommodate victims under earthquake disasters. In: Informatics in Control, Automation and Robotics. Berlin, Heidelberg: Springer. 2013:89-100.CrossRefGoogle Scholar
Gibney, RTN, Sever, MS, Vanholder, RC. Disaster nephrology: crush injury and beyond. Kidney Int. 2014;85(5):1049-1057.CrossRefGoogle ScholarPubMed
Sahashi, T, Sahashi, A, Uchiyama, H, et al. A study of operational liability of the medical rescue robot under disaster. In: 2011 IEEE/SICE International Symposium on System Integration (SII). Kyoto: IEEE. 2011:1281-1286.CrossRefGoogle Scholar
Zacharaki, A, Kostavelis, I, Gasteratos, A, et al. Safety bounds in human robot interaction: a survey. Safety Sci. 2020;127:104667.CrossRefGoogle Scholar
Sugiura, R, Fujikawa, T, Nishikata, R, et al. Soft tissue bruise injury by blunt impact in human-robot interaction - difference of tolerance between chest and extremities. In: 2019 19th International Conference on Control, Automation and Systems (ICCAS). Jeju, Korea (South): IEEE. 2019:792-797.CrossRefGoogle Scholar
Robinette, P, Howard, A, Wagner, AR. Conceptualizing people trust a robot that previously failed? Autonomy and Artificial Intelligence: A Threat or Savior? Cham: Springer. 2017:129-155.CrossRefGoogle Scholar
Nayyar, M, Zoloty, Z, McFarland, C, et al. Exploring the effect of explanations during robot-guided emergency evacuation. In: International Conference on Social Robotics (ICSR). Cham: Springer. 2020:13-22.CrossRefGoogle Scholar
Rossi, A, Dautenhahn, K, Koay, KL, et al. How the timing and magnitude of robot errors influence peoples’ trust of robots in an emergency scenario. In: International Conference on Social Robotics (ICSR). Cham: Springer. 2017:42-52.CrossRefGoogle Scholar
Schneider, FE, Wildermuth, D. Results of the European Land Robot Trial and their usability for benchmarking outdoor robot systems. In: Conference Towards Autonomous Robotic Systems (TAROS). Berlin, Heidelberg: Springer. 2011:408-409.CrossRefGoogle Scholar
Sheh, R, Schwertfeger, S, Visser, A. 16 years of RoboCup rescue. Künstl Intell. 2016;30(3):267-277.CrossRefGoogle Scholar
Winfield, AFT, Franco, MP, Brueggemann, B, et al. euRathlon and ERL emergency: a multi-domain multi-robot grand challenge for search and rescue robots. In: ROBOT 2017: Third Iberian Robotics Conference. Cham: Springer. 2017:263-271.Google Scholar
Brüggemann, B, Wildermuth, D, Schneider, FE. Search and retrieval of human casualties in outdoor environments with unmanned ground systems—system overview and lessons learned from ELROB 2014. Field and Service Robotics. Cham: Springer. 2016;113:533-546.CrossRefGoogle Scholar
Yanase, J, Triantaphyllou, E. The seven key challenges for the future of computer-aided diagnosis in medicine. Int J Med Inform. 2019;129:413-422.CrossRefGoogle ScholarPubMed
Yanase, J, Triantaphyllou, E. A systematic survey of computer-aided diagnosis in medicine: past and present developments. Expert Syst Appl. 2019;138:112821.CrossRefGoogle Scholar
Kelton, DK, Szulewski, A, Howes, D. Real-time video telemedicine applications in the emergency department: a scoping review of literature. Can J Emerg Med. 2018;20(6):920-928.CrossRefGoogle ScholarPubMed
Zhang, F, Yu, H, Jiang, J, et al. Brain–computer control interface design for virtual household appliances based on steady-state visually evoked potential recognition. Visual Inform. 2020;4(1):1-7.CrossRefGoogle Scholar
Yadav, D, Yadav, S, Veer, K. A comprehensive assessment of Brain Computer Interfaces: Recent trends and challenges. J Neurosci Methods. 2020;346:108918.CrossRefGoogle ScholarPubMed
Aricò, P, Borghini, G, Di Flumeri, G, et al. Chapter 10 - a passive brain–computer interface application for the mental workload assessment on professional air traffic controllers during realistic air traffic control tasks. Prog Brain Res. 2016;228:295-328.CrossRefGoogle Scholar
Liu, YZ, Habibnezhad, M, Jebelli, H. Brain-computer interface for hands-free teleoperation of construction robots. Automat Constr. 2021;123:103523.CrossRefGoogle Scholar