An Approach for Movement Coordination of Swarms of Unmanned Aerial Vehicles Using Mobile Networks

Souza, Bruno José Olivieri de

doi:10.17771/pucrio.acad.27092

Cited by 1 publication

(2 citation statements)

References 18 publications

(23 reference statements)

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“…Therefore, if the fleet comprises Phantom 4 Pro UAVs, the maximum distance between any two consecutive locations l i , l j ∈ AREA | i = j should be less than or equal to 30 meters. The safe distance dis s for UAVs depends on their maximum speed Sp, obstacle detection and processing time P t, and wireless communication latency Cl [5]. For example, if two UAVs are found heading towards each other at a maximum speed Sp of 5 meter per second each and with an obstacle detection and processing time P t of 0.5 seconds and a wireless communication latency Cl of 0.2 seconds, the safe distance dis s can be estimated as…”

Section: Safety Requirements For a Uav Fleetmentioning

confidence: 99%

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Towards a realtime, collision-free motion coordination and navigation system for a UAV fleet

Ashraf

Majd

Troubitsynå

2017

Proceedings of the Fifth European Conference on the Engineering of Computer-Based Systems

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This paper presents a realtime, collision-free motion coordination and navigation system for an Unmanned Aerial Vehicle (UAV) fleet. The proposed system uses geographical locations of the UAVs and of the successfully detected, static and moving obstacles to predict and avoid: (1) UAVto-UAV collisions, (2) UAV-to-static-obstacle collisions, and (3) UAV-tomoving-obstacle collisions. Our collision prediction approach leverages efficient runtime monitoring and Complex Event Processing (CEP) to make timely predictions. A distinctive feature of the proposed system is its ability to foresee a risk of a collision in realtime and proactively find best ways to avoid the predicted collisions in order to ensure safety of the entire fleet. We also present a simulation-based implementation of the proposed system along with an experimental evaluation involving a series of experiments. The results demonstrate that the proposed system successfully predicts and avoids all three kinds of collisions in realtime. Moreover, it generates efficient UAV routes, has an excellent runtime performance, efficiently scales to large-sized problem instances involving dozens of UAVs and obstacles, and is suitable for some densely populated, cluttered flying zones.

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Section: Safety Requirements For a Uav Fleetmentioning

confidence: 99%

“…Olivieri [5] and Olivieri and Endler [6] presented an approach for movement coordination of swarms of drones using smart phones and mobile communication networks. They used CEP, but only to analyze and evaluate the formation accuracy of the swarm.…”

Section: Related Workmentioning

confidence: 99%