USING AN INTELLIGENT UAV SWARM IN NATURAL DISASTER ENVIRONMENTS
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Due to their volatile behavior, natural disasters are challenging problems that often cannot be accurately predicted. An efficient method to gather updated information of the status of a disaster, such as the location of any trapped survivors, is extremely important to properly conduct rescue operations. To accomplish this, an algorithm is presented to control a swarm of UAVs and optimize the value of the information gathered by this swarm. With sensor technology embedded, this swarm collects information from the environment as it navigates with a decentralized control method. By using the swarm’s location history, areas of the environment with the highest probability of containing unidentified survivors can be prioritized, ensuring an efficient search. Measures are also developed to prevent redundant exploration, which would reduce the value of the information gathered. A case study of the Puerto Rico floods in 2017 is examined and simulated for validation. Through this approach, the value of the proposed swarm algorithm can be tested by tracking the number of survivors found as well as the rate at which they are discovered.