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A Mobility Model Based on Improved Artificial Potential Fields for Swarms of UAVs

Abstract : — A combination of several autonomous UAVs can be used to perform collaborative tasks. Such a combination is referred to as a swarm of drones. The use of multiple platforms can extend the system global capacities thanks to the resulting variety of embedded sensors and to information sharing. In this case, path planning and thus obstacles avoidance is still a major task. To deal with this issue, mobility models have to be implemented. Our contribution presented in this paper is a mobility model for swarms of UAVs based on the Artificial Potential Fields (APF) principle. In our model, the involved UAVs collaborate by sharing data about the obstacles that they detected. By doing so, a UAV which is not close enough to an obstacle to detect it thanks to its own sensors will still have the proper data to take this obstacle into account in its path planning. To validate our mobility strategies with realistic constraints we simulate the performances of existing sensors and transmitters, and consider real-world environment.
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Contributor : Ema Falomir <>
Submitted on : Wednesday, July 25, 2018 - 11:39:33 AM
Last modification on : Monday, August 6, 2018 - 10:46:35 AM
Long-term archiving on: : Friday, October 26, 2018 - 1:26:26 PM


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  • HAL Id : hal-01848857, version 1



Ema Falomir, Serge Chaumette, Gilles Guerrini. A Mobility Model Based on Improved Artificial Potential Fields for Swarms of UAVs. IROS 2018 (International Conference on Intelligent Robots and Systems), IEEE, Oct 2018, Madrid, Spain. ⟨hal-01848857⟩



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