![]() Bera, Abhishek ![]() in IEEE Wireless Communications and Networking Conference (2022, May 16) 5G-enabled UAV-based services have become popular for civilian applications. At the same time, certain no-fly zones will be highly dynamic, e.g. accident areas, large outdoor public events, VIP convoys ... [more ▼] 5G-enabled UAV-based services have become popular for civilian applications. At the same time, certain no-fly zones will be highly dynamic, e.g. accident areas, large outdoor public events, VIP convoys etc. An appropriate geofencing algorithm is required to avoid the no-fly zone in such scenarios. However, it is challenging to execute a high computing process such as a geofencing algorithm for a resource constraint UAV. This paper proposes an architecture and a geofencing algorithm for 5G-enabled UAV using Mobile Edge Computing (MEC). Also, the 5G-enabled UAV must fly within the coverage area during a mission. Hence, there must be an optimal trade-off between 5G coverage and distance to travel to design a new trajectory for a 5G-enabled UAV. To this end, we propose a cost minimization problem to generate a new trajectory while a no-fly zone exists. Specifically, we design a cost function considering 5G coverage and the velocity of the UAV. Then, we propose a geofencing algorithm running at the MEC by adopting the fast marching method (FMM) to generate a new trajectory for the UAV. Finally, a numerical example shows how the proposed geofencing algorithm generates an optimal trajectory for a UAV to avoid a dynamically created no-fly zone while on the mission. [less ▲] Detailed reference viewed: 52 (16 UL)![]() Dentler, Jan Eric ![]() ![]() ![]() in Autonomous Robots (2018) The global localization of multiple mobile robots can be achieved cost efficiently by localizing one robot globally and the others in relation to it using local sensor data. However, the drawback of this ... [more ▼] The global localization of multiple mobile robots can be achieved cost efficiently by localizing one robot globally and the others in relation to it using local sensor data. However, the drawback of this cooperative localization is the requirement of continuous sensor information. Due to a limited sensor perception space, the tracking task to continuously maintain this sensor information is challenging. To address this problem, this contribution is presenting a model predictive control (MPC) approach for such cooperative localization scenarios. In particular, the present work shows a novel workflow to describe sensor limitations with the help of potential functions. In addition, a compact motion model for multi-rotor drones is introduced to achieve MPC real-time capability. The effectiveness of the presented approach is demonstrated in a numerical simulation, an experimental indoor scenario with two quadrotors as well as multiple indoor scenarios of a quadrotor obstacle evasion maneuver. [less ▲] Detailed reference viewed: 285 (35 UL) |
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