4,053 research outputs found
A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones
Fully-autonomous miniaturized robots (e.g., drones), with artificial
intelligence (AI) based visual navigation capabilities are extremely
challenging drivers of Internet-of-Things edge intelligence capabilities.
Visual navigation based on AI approaches, such as deep neural networks (DNNs)
are becoming pervasive for standard-size drones, but are considered out of
reach for nanodrones with size of a few cm. In this work, we
present the first (to the best of our knowledge) demonstration of a navigation
engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based
visual navigation. To achieve this goal we developed a complete methodology for
parallel execution of complex DNNs directly on-bard of resource-constrained
milliwatt-scale nodes. Our system is based on GAP8, a novel parallel
ultra-low-power computing platform, and a 27 g commercial, open-source
CrazyFlie 2.0 nano-quadrotor. As part of our general methodology we discuss the
software mapping techniques that enable the state-of-the-art deep convolutional
neural network presented in [1] to be fully executed on-board within a strict 6
fps real-time constraint with no compromise in terms of flight results, while
all processing is done with only 64 mW on average. Our navigation engine is
flexible and can be used to span a wide performance range: at its peak
performance corner it achieves 18 fps while still consuming on average just
3.5% of the power envelope of the deployed nano-aircraft.Comment: 15 pages, 13 figures, 5 tables, 2 listings, accepted for publication
in the IEEE Internet of Things Journal (IEEE IOTJ
Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimization of Bit Allocation and Path Planning
Unmanned Aerial Vehicles (UAVs) have been recently considered as means to
provide enhanced coverage or relaying services to mobile users (MUs) in
wireless systems with limited or no infrastructure. In this paper, a UAV-based
mobile cloud computing system is studied in which a moving UAV is endowed with
computing capabilities to offer computation offloading opportunities to MUs
with limited local processing capabilities. The system aims at minimizing the
total mobile energy consumption while satisfying quality of service
requirements of the offloaded mobile application. Offloading is enabled by
uplink and downlink communications between the mobile devices and the UAV that
take place by means of frequency division duplex (FDD) via orthogonal or
non-orthogonal multiple access (NOMA) schemes. The problem of jointly
optimizing the bit allocation for uplink and downlink communication as well as
for computing at the UAV, along with the cloudlet's trajectory under latency
and UAV's energy budget constraints is formulated and addressed by leveraging
successive convex approximation (SCA) strategies. Numerical results demonstrate
the significant energy savings that can be accrued by means of the proposed
joint optimization of bit allocation and cloudlet's trajectory as compared to
local mobile execution as well as to partial optimization approaches that
design only the bit allocation or the cloudlet's trajectory.Comment: 14 pages, 5 figures, 2 tables, IEEE Transactions on Vehicular
Technolog
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