873 research outputs found
Dynamic Base Station Repositioning to Improve Spectral Efficiency of Drone Small Cells
With recent advancements in drone technology, researchers are now considering
the possibility of deploying small cells served by base stations mounted on
flying drones. A major advantage of such drone small cells is that the
operators can quickly provide cellular services in areas of urgent demand
without having to pre-install any infrastructure. Since the base station is
attached to the drone, technically it is feasible for the base station to
dynamic reposition itself in response to the changing locations of users for
reducing the communication distance, decreasing the probability of signal
blocking, and ultimately increasing the spectral efficiency. In this paper, we
first propose distributed algorithms for autonomous control of drone movements,
and then model and analyse the spectral efficiency performance of a drone small
cell to shed new light on the fundamental benefits of dynamic repositioning. We
show that, with dynamic repositioning, the spectral efficiency of drone small
cells can be increased by nearly 100\% for realistic drone speed, height, and
user traffic model and without incurring any major increase in drone energy
consumption.Comment: Accepted at IEEE WoWMoM 2017 - 9 pages, 2 tables, 4 figure
Real-time on-board obstacle avoidance for UAVs based on embedded stereo vision
In order to improve usability and safety, modern unmanned aerial vehicles
(UAVs) are equipped with sensors to monitor the environment, such as
laser-scanners and cameras. One important aspect in this monitoring process is
to detect obstacles in the flight path in order to avoid collisions. Since a
large number of consumer UAVs suffer from tight weight and power constraints,
our work focuses on obstacle avoidance based on a lightweight stereo camera
setup. We use disparity maps, which are computed from the camera images, to
locate obstacles and to automatically steer the UAV around them. For disparity
map computation we optimize the well-known semi-global matching (SGM) approach
for the deployment on an embedded FPGA. The disparity maps are then converted
into simpler representations, the so called U-/V-Maps, which are used for
obstacle detection. Obstacle avoidance is based on a reactive approach which
finds the shortest path around the obstacles as soon as they have a critical
distance to the UAV. One of the fundamental goals of our work was the reduction
of development costs by closing the gap between application development and
hardware optimization. Hence, we aimed at using high-level synthesis (HLS) for
porting our algorithms, which are written in C/C++, to the embedded FPGA. We
evaluated our implementation of the disparity estimation on the KITTI Stereo
2015 benchmark. The integrity of the overall realtime reactive obstacle
avoidance algorithm has been evaluated by using Hardware-in-the-Loop testing in
conjunction with two flight simulators.Comment: Accepted in the International Archives of the Photogrammetry, Remote
Sensing and Spatial Information Scienc
A Decision-theoretic Approach to Detection-based Target Search with a UAV
Search and rescue missions and surveillance require finding targets in a
large area. These tasks often use unmanned aerial vehicles (UAVs) with cameras
to detect and move towards a target. However, common UAV approaches make two
simplifying assumptions. First, they assume that observations made from
different heights are deterministically correct. In practice, observations are
noisy, with the noise increasing as the height used for observations increases.
Second, they assume that a motion command executes correctly, which may not
happen due to wind and other environmental factors. To address these, we
propose a sequential algorithm that determines actions in real time based on
observations, using partially observable Markov decision processes (POMDPs).
Our formulation handles both observations and motion uncertainty and errors. We
run offline simulations and learn a policy. This policy is run on a UAV to find
the target efficiently. We employ a novel compact formulation to represent the
coordinates of the drone relative to the target coordinates. Our POMDP policy
finds the target up to 3.4 times faster when compared to a heuristic policy.Comment: Published in IEEE IROS 2017. 6 page
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