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3, 2, 1, Drone Go! A test to take off UAV swarm intelligence for distributed sensing


Feelings distributed by swarm of drones Used in a variety of fields, from traffic monitoring to early detection of forest fires. However, the design of such technologies is a highly complex research endeavor. The simulation environment allows for more targeted algorithms to be studied, while experimenting with real drones increases realism and external value.

A flying drone.  Image credit: Pok Rie via Pexels, free license

A flying drone. Image credit: Pok Rie qua Pexelsfree license

To bridge this gap, a recent arXiv.org paper introduces an experimental facility for studying swarm intelligence. Driverless car (UAV) for distributed sensing problems.

The test plate includes a sensor map. Images and videos are projected on the floor in an indoor lab environment. Low-cost drones can traverse the map and hover to “scan” points of interest. The test plate allows to study solutions to problems such as collision avoidance, automatic search and navigation.

A working prototype of the proposed model to coordinate and optimize the navigation and sensing of the drone in a completely decentralized manner.

This paper introduces an experimental machine to study distributed sensing problems of Unmanned Aerial Vehicles (UAVs) exhibiting swarm intelligence. Some Smart City applications, such as transportation and disaster response, require efficient sensor data collection by a collaborative and intelligent UAV team. This has often proven to be too complex and expensive to study systematically and rigorously without compromising on scale, realism, and external validity. With the proposed test plate, this paper provides a stepping stone to simulate, in small laboratory spaces, large sensing areas of interest derived from experimental data and simulation models. On this sensor map, a group of low-cost drones can fly allowing a wide range of issues such as energy consumption, charge control, navigation and collision avoidance to be studied. The applicability of a decentralized multi-agent collective learning algorithm (EPOS) for UAV swarm intelligence along with the evaluation of power consumption measurements provides a proof of concept and identification. get the recommended test bed accuracy.

Research articles: Qin, C., Candan, F., Mihaylova, LS and Pournaras, E., “3, 2, 1, Drone Go! A test to take off UAV swarm intelligence for distributed sensing”, 2022. Link: https://arxiv.org/abs/2208.05914






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