There are many methodologies assisting in the detection and tracking of trapped victims in the context of disaster management. Disaster management in the aftermath of such sudden occurrences requires pre-paredness in terms of technology, availability, acces-sibility, perception, training, evaluation, and deploy-ability. This can be achieved through intensive test, evaluation and comparison of different techniques that are alternative to each other, eventually covering each module of the technology used for the search and rescue operation. Intensive research and development by academia and industry have led to an increased ro-bustness of deep learning techniques such as the use of convolutional neural networks, which has resulted in increased reliance of first responders on the unmanned aerial vehicle (UAV) technology equipped with state-of-the-art computers to process real-time sensory information from cameras and other sensors in quest of possibility of life. In this paper, we propose a method to implement simulated detection of life in the sudden onset of disasters with the help of a deep learning model, and simultaneously implement multi-robot coordination between the vehicles with the use of a suitable region-partitioning technique to further expedite the operation. A simulated test platform was developed with parameters resembling real-life disaster environments using the same sensors.
All Science Journal Classification (ASJC) codes
- General Computer Science
- Electrical and Electronic Engineering