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Register a new userSkyQuery: An Aerial Drone Video Sensing Platform
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Added by
Favyen Bastani
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Video-based sensing from aerial drones, especially small multirotor drones, can provide rich data for numerous applications, including traffic analysis (computing traffic flow volumes), precision agriculture (periodically evaluating plant health), and wildlife population management (estimating population sizes). However, aerial drone video sensing applications must handle a surprisingly wide range of tasks: video frames must be aligned so that we can equate coordinates of objects that appear in different frames, video data must be analyzed to extract application-specific insights, and drone routes must be computed that maximize the value of newly captured video. To address these challenges, we built SkyQuery, a novel aerial drone video sensing platform that provides an expressive, high-level programming language to make it straightforward for users to develop complex long-running sensing applications. SkyQuery combines novel methods for fast video frame alignment and detection of small objects in top-down aerial drone video to efficiently execute applications with diverse video analysis workflows and data distributions, thereby allowing application developers to focus on the unique qualities of their particular application rather than general video processing, data analysis, and drone routing tasks. We conduct diverse case studies using SkyQuery in parking monitoring, pedestrian activity mapping, and traffic hazard detection scenarios to demonstrate the generalizability and effectiveness of our system.
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We consider the positioning problem of aerial drone systems for efficient three-dimensional (3-D) coverage. Our solution draws from molecular geometry, where forces among electron pairs surrounding a central atom arrange their positions. In this paper, we propose a 3-D clustering algorithm for autonomous positioning (VBCA) of aerial drone networks based on virtual forces. These virtual forces induce interactions among drones and structure the system topology. The advantages of our approach are that (1) virtual forces enable drones to self-organize the positioning process and (2) VBCA can be implemented entirely localized. Extensive simulations show that our virtual forces clustering approach produces scalable 3-D topologies exhibiting near-optimal volume coverage. VBCA triggers efficient topology rearrangement for an altering number of nodes, while providing network connectivity to the central drone. We also draw a comparison of volume coverage achieved by VBCA against existing approaches and find VBCA up to 40% more efficient.
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We propose a novel resilient drone service composition framework for delivery in dynamic weather conditions. We use a skyline approach to select an optimal set of candidate drone services at the source node in a skyway network. Drone services are initially composed using a novel constraint-aware deterministic lookahead algorithm using the multi-armed bandit tree exploration. We propose a heuristic-based resilient service composition approach that adapts to runtime changes and periodically updates the composition to meet delivery expectations. Experimental results prove the efficiency of the proposed approach.
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