Multi-robot search and rescue team

2011 ◽  
Author(s):  
Cai Luo ◽  
Andre Possani Espinosa ◽  
Danu Pranantha ◽  
Alessandro De Gloria
Keyword(s):  
Search And Rescue ◽  
Rescue Team ◽  
Multi Robot

scholarly journals Design and Development of IoT enabled Multi Robot System for Search and Rescue Mission

2018 ◽  
Vol 10 (2) ◽  
pp. 51 ◽  
Author(s):  
Rajesh Singh ◽  
Rohit Samkaria ◽  
Anita Gehlot ◽  
Sushabhan Choudhary
Keyword(s):  
Search And Rescue ◽  
Design And Development ◽  
Robot System ◽  
Rescue Mission ◽  
Multi Robot

Human adaptation to latency in teleoperated multi-robot human-agent search and rescue teams

2019 ◽  
Vol 99 ◽  
pp. 265-277 ◽  
Author(s):  
Amro Khasawneh ◽  
Hunter Rogers ◽  
Jeffery Bertrand ◽  
Kapil Chalil Madathil ◽  
Anand Gramopadhye
Keyword(s):  
Search And Rescue ◽  
Human Adaptation ◽  
Human Agent ◽  
Multi Robot

Wilderness Search and Rescue with Heterogeneous Multi-Robot Systems*

2020 ◽  
Author(s):  
Marcos Rodriguez ◽  
Abdulla Al-Kaff ◽  
Angel Madridano ◽  
David Martin ◽  
Arturo de la Escalera
Keyword(s):  
Search And Rescue ◽  
Robot Systems ◽  
Multi Robot

scholarly journals Automatic Detection of Wrecked Airplanes from UAV Images

2019 ◽  
Vol 7 (2) ◽  
Author(s):  
Anhar Risnumawan ◽  
Muhammad Ilham Perdana ◽  
Alif Habib Hidayatulloh ◽  
A. Khoirul Rizal ◽  
Indra Adji Sulistijono ◽  
...  
Keyword(s):  
Visual Information ◽  
Detection System ◽  
Search And Rescue ◽  
Color Variation ◽  
Aerial Images ◽  
Rescue Team ◽  
Access Road ◽  
Uav Images ◽  
Exploration Area ◽  
Accident Site

Searching the accident site of a missing airplane is the primary step taken by the search and rescue team before rescuing the victims. However, due to the vast exploration area, lack of technology, no access road, and rough terrain make the search process nontrivial and thus causing much delay in handling the victims. Therefore, this paper aims to develop an automatic wrecked airplane detection system using visual information taken from aerial images such as from a camera. A new deep network is proposed to distinguish robustly the wrecked airplane that has high pose, scale, color variation, and high deformable object. The network leverages the last layers to capture more abstract and semantics information for robust wrecked airplane detection. The network is intertwined by adding more extra layers connected at the end of the layers. To reduce missing detection which is crucial for wrecked airplane detection, an image is then composed into five patches going feed-forwarded to the net in a convolutional manner. Experiments show very well that the proposed method successfully reaches AP=91.87%, and we believe it could bring many benefits for the search and rescue team for accelerating the searching of wrecked airplanes and thus reducing the number of victims.

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