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Communication Dans Un Congrès Année : 2016

Towards a generic model of adaptative geofencing focused on operationnal issues

Résumé

We introduce the reasoning and concepts behind a generic approach to obstacle avoidance based on geofencing. In this paper we try to tackle the issue of UAV safety from a practical point of view and propose a collision avoidance system based on safety considerations in real life scenarios. Before doing so, we review some of the related technical aspects in an operational context: global positioning in different reference systems and how to switch between the associated coordinates systems. After looking at the different cartographic representations used in aeronautics and how to apprehend them, we introduce the concept of fence. We then discuss the different possible representations of such geometries. For some, we expose the formulas for calculating distances to the surface of the fence and whether or not the drone has trespassed it. We finally propose two collision avoidance strategies: one in the case of autonomous flight and another in the case of piloted flight. To our knowledge, this second scenario has not been extensively covered and we feel it deserves some attention since most of the UAV’s flying todays are flown manually. This avoidance is based on constrained optimization to find an input for the controller which prevent collision while staying as true as possible to the original command from the human operator. This strategy allows for “smooth” collision avoidance since it gently and continuously restrict the flight possibilities of the UAV while giving as much control freedom as possible to the operator. Finally, we discuss the impact of drone dynamics on our system and how we could take moving fences into consideration.
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Dates et versions

hal-01251309 , version 1 (05-01-2016)

Identifiants

  • HAL Id : hal-01251309 , version 1

Citer

Laurent Ciarletta, Thomas Gurriet. Towards a generic model of adaptative geofencing focused on operationnal issues. 3rd AETOS International Workshop on Research Challenges for Future RPAS/UAS , May 2015, Atlanta, United States. ⟨hal-01251309⟩
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