Browsing by Subject "Unmanned aerial vehicles"

Now showing 1 - 3 of 3
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    Proceedings: Preparing for Unmanned Aircraft Systems in Minnesota
    (Center for Transportation Studies, University of Minnesota, 2014-04-30) Center for Transportation Studies
    About 110 state and national experts gathered for a daylong forum on April 30, 2014 at the University of Minnesota to discuss issues related to the rapidly growing industry of unmanned aerial vehicles (UAVs) and unmanned aircraft systems (UAS). Here is a summary of speakers and topics: Welcome and Opening Remarks by Cassandra Isackson; Why UAS? Opportunities and Challenges with Brigadier General Alan Palmer; What are UAS? with Kevin McKinnon and Mike Davin; Panel Discussion: UAS Education and Outreach with Curtis Zoller and Eric Euteneuer, moderated by Mike Davin; Impacts on Airspace and FAA Roadmap with Randy Willis, moderated by Eric Euteneuer; Current Applications and Impacts with David Mulla, Weston Merrick, Eric Taipale, Ian MacRae, Dave Gebhardt, moderated by Mostafa Kaveh; North Carolina Case Study: Establishing UAS in the State with Thomas Zajkowski, moderated by Todd Colton; Where Do We Go From Here? Opportunities and Challenges with Charles Samuelson, Brigadier General Alan Palmer, Thomas Zajkowski, Curtis Zoller, Randy Willis, moderated by Gina Baas.
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    Real-time strategies for enhancing aircraft performance in wind.
    (2012-08) Turkoglu, Kamran
    This thesis presents real-time guidance strategies for unmanned aerial vehicles (UAVs) that can be used to enhance their flight endurance by utilizing in-situ measurements of wind speeds and wind gradients. In these strategies, periodic adjustments can be made in the airspeed and/or heading angle command for the UAV to minimize a projected power requirement at some future time. In this thesis, UAV flights are described by a three-dimensional dynamic point-mass model. A stochastic wind field model has been developed not only to reflect the mean wind magnitude behaviour in both vertical and horizontal axis, but also has it been extended to characterize wind direction behaviour as well. Proposed wind field model is assumed that it is consisted of a constant term plus terms that vary sinusoidally with respect to the location and time. Onboard closed-loop trajectory tracking logics that follow airspeed vector commands are modeled using the method of feedback linearization. To evaluate the benefits of these strategies in enhancing UAV flight endurance, a reference strategy is introduced in which the UAV would follow the optimal airspeed command in a steady level flight under zero wind conditions. A performance measure is defined as the average power consumption both over a specified time interval and over different initial heading angles of the UAV. A relative benefit criterion is then defined as the percentage improvement in the performance measure of a proposed strategy over that of the reference strategy. Extensive numerical simulations are conducted to show efficiency and applicability of the proposed algorithms. Results demonstrate the efficiency, benefits and trends of power savings of the proposed real-time guidance strategies in level flights.
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    Spectral Detection of Soybean Aphid (Hemiptera: Aphididae) and Confounding Insecticide Effects in Soybean
    (2017-01) Alves, Tavvs
    Soybean aphid, Aphis glycines (Hemiptera: Aphididae) is the primary insect pest of soybean in the northcentral United States. Soybean aphid may cause stunted plants, leaf discoloration, plant death, and decrease soybean yield by 40%. Sampling plans have been developed for supporting soybean aphid management. However, growers’ perception about time involved in direct insect counts has been contributing to a lower adoption of traditional pest scouting methods and may be associated with the use of prophylactic insecticide applications in soybean. Remote sensing of plant spectral (light-derived) responses to soybean aphid feeding is a promising alternative to estimate injury without direct insect counts and, thus, increase adoption and efficiency of scouting programs. This research explored the use of remote sensing of soybean reflectance for detection of soybean aphids and showed that foliar insecticides may have implications for subsequent use of soybean spectral reflectance for pest detection. Chapter 1 was the first publication showing that feeding from soybean aphid affects soybean spectral reflectance. Using ground-based spectroradiometers at canopy-level, it was found that soybean aphids affected plant reflectance at a narrowband wavelength within the near-infrared spectral range (800 nm), but had no effect at a red narrowband wavelength (680 nm). Soybean aphids also affected a vegetation index referred to as NDVI (i.e., normalized difference vegetation index), which combines the near-infrared and red wavelengths into a value representing photosynthetic pigment content and potential ultrastructure changes in soybean leaves. In general, soybean aphids induced similar effects on canopy- and leaf-level spectral measurements, but there were a few instances that significant effects at leaf-level were not detected by canopy-level. Chapter 2 used hyperspectral data and simulated wide-band sensors for detection of soybean aphid. While the first chapter showed that remote sensing is a promising solution based on results from two narrowband wavelengths, the second chapter provided an extensive search for band sensors that could optimize the use of soybean canopy reflectance for soybean aphid detection. Akaike’s Information Criteria (AIC) was used to rank and select sensors. Lower AIC values were considered to provide better models. The subset of narrowband wavelengths that optimized estimation of soybean aphid abundance was similar to that obtained using simulated wide-band sensors. Increasing sensor bandwidth corresponded to larger AIC values (worse models). The smallest AIC values (better models) were observed with narrow- and wide-band sensors centered around 780 nm. Chapter 3 assessed effects of foliar insecticides on spectral response of soybean plants under greenhouse and field conditions. Such effects could potentially confound measures of pest-induced spectral changes. Representatives of the two most commonly used insecticides (i.e., chlorpyrifos and λ-cyhalothrin) and a novel active ingredient referred to as sulfoxaflor affected soybean leaf reflectance. λ-cyhalothrin had the least effect on spectral reflectance and only affected a few near-infrared wavelengths, but sulfoxaflor and chlorpyrifos affected leaf reflectance at several visible and near-infrared wavelengths. I speculated that foliar insecticides had immediate effects via surface residues on plants and delayed effects via morpho-physiological changes induced by the insecticides. The potential leaf surface residues had transitory effects on soybean reflectance and no consistent pattern of spectral changes was associated with the insecticides. Overall, my results hold promise to identify and characterize injury of soybean aphid using remote sensing of soybean canopy reflectance. The information provided in this research may help to design optimized sensors for soybean aphid detection and contribute to the understanding of insect- and insecticide-induced effects on plants. It may also improve the current field-wide management tactics by making decisions for pest control when plant spectral reflectance indicates that soybean aphid abundance reached its economic threshold. To incorporate remote sensing into IPM programs, this new scouting method based on plant spectral reflectance will need further research to adjust economic thresholds, application of insecticides with no or short-duration effects on plant spectral data, and better understanding of other plant-pest interactions affecting plant morpho-physiology. It will be important to distinguish spectral changes induced by soybean aphid from other confounding factors such as other herbivores, nutritional deficiencies, diseases, and water stress. Future research will be needed to determine if the ground-based effects documented in our studies can be detected from space- and air-based platforms, such as satellites and unmanned aerial systems. Moreover, advancing our results may contribute to determine where and when insecticides are needed by using the spatial location of soybean spectral responses to soybean aphid infestations. Remote sensing has the potential to expand the use of IPM practices and collaborate to the mission of feeding an increasing population that has been changing diet habits and will require more production of food.