Abstract
The unmanned aerial vehicles (UAV) have great potential to support search tasks in unstructured environments. These are agile, small and lightweight which can incorporate many sensors that are suitable for detecting an object of interest across various terrains. These UAVs or Drones are perfectly suited for reconnaissance and perimeter sweeps. However, these have their limits and are vulnerable when operated alone. The solution to the problem is using a number of these drones to form a swarm. The swarm of drones can cover a larger area in a short period. The drones will be resembling the flocking nature of birds and the hive nature of bees which means there would never be a single drone operating alone. These drones are connected and the main controller i.e. the hive. The Hive is a narrow AI which is a goal-based system. The goals can be sweeping an area or periodic border patrols. The hive monitors the drones, organizes them. The drones communicate in the swarm wirelessly creating a Flying Adhoc Network. For navigation, this network utilizes GPS technology. This project aims to develop an AI-based system that controls these drones and achieve the goal that is assigned to it.
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Awasthi, S., Balusamy, B., Porkodi, V. (2020). Artificial Intelligence Supervised Swarm UAVs for Reconnaissance. In: Batra, U., Roy, N., Panda, B. (eds) Data Science and Analytics. REDSET 2019. Communications in Computer and Information Science, vol 1229. Springer, Singapore. https://doi.org/10.1007/978-981-15-5827-6_33
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DOI: https://doi.org/10.1007/978-981-15-5827-6_33
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