Search and Rescue Rotary-Wing UAV and Its Application to the Lushan Ms 7.0 Earthquake

• Published in: Journal of field robotics 2016-05, Vol.33 (3), p.290-321
• Main Authors: Qi, Juntong, Song, Dalei, Shang, Hong, Wang, Nianfa, Hua, Chunsheng, Wu, Chong, Qi, Xin, Han, Jianda
• Format: Article
• Language: English
• Subjects: Building failures; Buildings; China; Earthquakes; Evacuations & rescues; Search and rescue missions; Searching; Strategy; Strontium; Unmanned aerial vehicles; Unmanned air vehicles
• ISSN: 1556-4959; 1556-4967
• DOI: 10.1002/rob.21615

Rapid search and rescue responses after earthquakes or in postseismic evaluation tend to be extremely difficult. To solve this problem, we summarized the requirements of search and rescue rotary‐wing unmanned aerial vehicle (SR‐RUAV) systems according to related works, manual earthquake search and rescue, and our knowledge to guide our research works. Based on these requirements, a series of research and technical works have been conducted to present an efficient SR‐RUAV system. To help rescue teams locate interested areas quickly, a collapsed‐building detecting approach that integrates low‐altitude statistical image processing methods was proposed, which can increase survival rates by detecting collapsed buildings in a timely manner. The entire SR‐RUAV system was illustrated by simulated earthquake response experiments in the China National Training Base for Search and Rescue (CNTBSR) from 2008 to 2010. On April 20, 2013, Lushan (China) experienced a disastrous earthquake (magnitude 7.0). Because of the distribution of buildings in the rural areas, it was impossible to implement a rapid search and postseismic evaluation via ground searching. We provided our SR‐RUAV to the Chinese International Search and Rescue Team (CISAR) and accurately detected collapsed buildings for ground rescue guidance at low altitudes. This system was significantly improved with respect to its searching/planning strategy and vision‐based evaluation in different environments based on the lessons learned from actual missions after the earthquake. The SR‐RUAV has proved to be applicable and time saving. The physical structure, searching and planning strategy, image‐processing algorithm, and improvements in real missions are described in detail in this study.