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The use of unmanned aerial vehicles (UAVs) or drones, has significantly increased over the past few years. There is a growing demand in the drone industry, creating new workforce opportunities such as package delivery, search and rescue, real estate, transportation, agriculture, infrastructure inspection, and many others, signifying the importance of effective and efficient control techniques. We propose a scheme for controlling a drone through gaze extracted from eye-trackers, enabling an operator to navigate through a series of waypoints. Then we demonstrate and test the utility of our approach through a pilot study against traditional controls. Our results indicate gaze as a promising control technique for navigating drones revealing novel research avenues.
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Heuristic Algorithms for Co-scheduling of Edge Analytics and Routes for UAV Fleet Missions
Unmanned Aerial Vehicles (UAVs) or drones are increasingly used for urban applications like traffic monitoring and construction surveys. Autonomous navigation allows drones to visit waypoints and accomplish activities as part of their mission. A common activity is to hover and observe a location using on-board cameras. Advances in Deep Neural Networks (DNNs) allow such videos to be analyzed for automated decision making. UAVs also host edge computing capability for on-board inferencing by such DNNs. To this end, for a fleet of drones, we propose a novel Mission Scheduling Problem (MSP) that co-schedules the flight routes to visit and record video at waypoints, and their subsequent on-board edge analytics. The proposed schedule maximizes the utility from the activities while meeting activity deadlines as well as energy and computing constraints. We first prove that MSP is NP-hard and then optimally solve it by formulating a mixed integer linear programming (MILP) problem. Next, we design two efficient heuristic algorithms, JSC and VRC, that provide fast sub-optimal solutions. Evaluation of these three schedulers using real drone traces demonstrate utility–runtime trade-offs under diverse workloads.
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- Award ID(s):
- 1725755
- PAR ID:
- 10290967
- Date Published:
- Journal Name:
- IEEE International Conference on Computer Communications (INFOCOM)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
