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We present results arising from the problem of sweeping a mosquito-infested area with an Un-manned Aerial Vehicle (UAV) equipped with an electrified metal grid. This is related to the Traveling Salesman Problem, the Lawn Mower Problem and, most closely, Milling with TurnCost. Planning a good trajectory can be reduced to considering penalty and budget variants of covering a grid graph with minimum turn cost. On the theoretical side, we show the solution of a problem from The Open Problems Project that had been open for more than 15 years, and hint at approximation algorithms. On the practical side, we describe an exact method based on Integer Programming that is able to compute provably optimal instances with over 500 pixels. These solutions are actually used for practical trajectories, as demonstrated in the video.
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Using a UAV for Destructive Surveys of Mosquito Population
This paper introduces techniques for mosquito population surveys in the field using electrified screens (bug zappers) mounted to a UAV. Instrumentation on the UAV logs the UAV path and the GPS location, altitude, and time of each mosquito elimination. Hardware experiments with a UAV equipped with an electrified screen provide real-time measurements of (former) mosquito locations and mosquito-free volumes. Planning a trajectory for the UAV that maximizes the number of mosquito kills is related to the Traveling Salesman Problem, the Lawn Mower Problem and, most closely, Milling with Turn Cost. We reduce this problem to considering variants of covering a grid graph with minimum turn cost, corresponding to optimized energy consumption. We describe an exact method based on Integer Programming that is able to compute provably optimal instances with over 1,500 pixels. These solutions are then implemented on the UAV.
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- PAR ID:
- 10082516
- Date Published:
- Journal Name:
- 2018 IEEE International Conference on Robotics and Automation (ICRA)
- Page Range / eLocation ID:
- 7812 to 7819
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICRA.2018.8463184
