期刊名称：The International Arab Journal of Information Technology
印刷版ISSN：1683-3198
出版年度：2021
卷号：18
期号：1
DOI：10.34028/iajit/18/1/3
语种：English
出版社：Zarqa Private University
摘要：Every year, flood hits the world economy by billions of dollars, costs thousands of human and animal lives, destroys a vast area of land and crops, and displaces large populations from their homes. The flood affected require a time-critical help, and a delay may cause the loss of precious human lives. The ground rescue operations are difficult to carry out because of the unavailability of transport infrastructure. However, drones, Unmanned Vehicles, can easily navigate to the areas where road networks have been destroyed or become ineffective. The fleet participating in the rescue operation should have drones with different capabilities in order to make the efforts more successful. A majority of existing systems in the literature offered a centralized system for these drones. However, the performance of the existing system starts decreasing as the required number of tasks increases. This research is based on the hypothesis that a distributed intelligent method is more effective than the centralized technique for relief operations performed by multiple drones. The research aims to propose a distributed method that allows a fleet of drones with diverse capabilities to communicate and collaborate, so that the task completion rate of rescue operations could be increased. The proposed solution consists of three main modules: 1) communication and message transmission module that enables collaboration between drones, 2) realignment module that allows drones to negotiate and occupy the best position in the air to optimize the coverage area, 3) situation monitoring module that identifies the ground situation and acts accordingly. To validate the proposed solution, we have performed a simulation using AirSim simulator and compared the results with the centralized system. The proposed distributed method performed better than legacy systems. In the future, the work can be extended using reinforcement learning and other intelligent algorithms.
