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- Biological Systems Engineering: Papers and Publications (2)
- Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research (1)
- Department of Computer Science and Engineering: Dissertations, Theses, and Student Research (1)
- Department of Mechanical and Materials Engineering: Dissertations, Theses, and Student Research (1)
Articles 1 - 5 of 5
Full-Text Articles in Engineering
Extending Mission Duration Of Uas Multicopters: Multi-Disciplinary Approach, Marc Lussier
Extending Mission Duration Of Uas Multicopters: Multi-Disciplinary Approach, Marc Lussier
Department of Mechanical and Materials Engineering: Dissertations, Theses, and Student Research
Multicopters are important tools in industry, the military, and research but suffer from short flight times and mission durations. In this thesis, we discuss three different ways to increase flight times and therefore increase the viability of using multicopters in a variety of missions. Alternate fuel sources such as hydrogen fuel and solar cells are starting to be used on multicopters, in our research we simulate modern fuel cells and show how well they currently work as the power source for multicopters and how close they are to becoming useful in Unmanned Aircraft System (UAS) technology. Increasing the efficiency in …
Domain Adaptation In Unmanned Aerial Vehicles Landing Using Reinforcement Learning, Pedro Lucas Franca Albuquerque
Domain Adaptation In Unmanned Aerial Vehicles Landing Using Reinforcement Learning, Pedro Lucas Franca Albuquerque
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
Landing an unmanned aerial vehicle (UAV) on a moving platform is a challenging task that often requires exact models of the UAV dynamics, platform characteristics, and environmental conditions. In this thesis, we present and investigate three different machine learning approaches with varying levels of domain knowledge: dynamics randomization, universal policy with system identification, and reinforcement learning with no parameter variation. We first train the policies in simulation, then perform experiments both in simulation, making variations of the system dynamics with wind and friction coefficient, then perform experiments in a real robot system with wind variation. We initially expected that providing …
Uumanned Aerial Vehicle Data Analysis For High-Throughput Plant Phenotyping, Jiating Li
Uumanned Aerial Vehicle Data Analysis For High-Throughput Plant Phenotyping, Jiating Li
Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research
The continuing population is placing unprecedented demands on worldwide crop yield production and quality. Improving genomic selection for breeding process is one essential aspect for solving this dilemma. Benefitted from the advances in high-throughput genotyping, researchers already gained better understanding of genetic traits. However, given the comparatively lower efficiency in current phenotyping technique, the significance of phenotypic traits has still not fully exploited in genomic selection. Therefore, improving HTPP efficiency has become an urgent task for researchers. As one of the platforms utilized for collecting HTPP data, unmanned aerial vehicle (UAV) allows high quality data to be collected within short …
Design Of Plant Protection Uav Variable Spray System Based On Neural Networks, Sheng Wen, Quanyong Zhang, Xuanchun Yin, Yubin Lan, Jiantao Zhang, Yufeng Ge
Design Of Plant Protection Uav Variable Spray System Based On Neural Networks, Sheng Wen, Quanyong Zhang, Xuanchun Yin, Yubin Lan, Jiantao Zhang, Yufeng Ge
Biological Systems Engineering: Papers and Publications
Recently, unmanned aerial vehicles (UAVs) have rapidly emerged as a new technology in the fields of plant protection and pest control in China. Based on existing variable spray research, a plant protection UAV variable spray system integrating neural network based decision making is designed. Using the existing data on plant protection UAV operations, combined with artificial neural network (ANN) technology, an error back propagation (BP) neural network model between the factors affecting droplet deposition is trained. The factors affecting droplet deposition include ambient temperature, ambient humidity, wind speed, flight speed, flight altitude, propeller pitch, nozzles pitch and prescription value. Subsequently, …
Rapeseed Seedling Stand Counting And Seeding Performance Evaluation At Two Early Growth Stages Based On Unmanned Aerial Vehicle Imagery, Biquan Zhao, Jian Zhang, Chenghai Yang, Guangsheng Zhou, Youchun Ding, Yeyin Shi, Dongyan Zhang, Jing Xie, Qingxi Liao
Rapeseed Seedling Stand Counting And Seeding Performance Evaluation At Two Early Growth Stages Based On Unmanned Aerial Vehicle Imagery, Biquan Zhao, Jian Zhang, Chenghai Yang, Guangsheng Zhou, Youchun Ding, Yeyin Shi, Dongyan Zhang, Jing Xie, Qingxi Liao
Biological Systems Engineering: Papers and Publications
The development of unmanned aerial vehicles (UAVs) and image processing algorithms for field-based phenotyping offers a non-invasive and effective technology to obtain plant growth traits such as canopy cover and plant height in fields. Crop seedling stand count in early growth stages is important not only for determining plant emergence, but also for planning other related agronomic practices. The main objective of this research was to develop practical and rapid remote sensing methods for early growth stage stand counting to evaluate mechanically seeded rapeseed (Brassica napus L.) seedlings. Rapeseed was seeded in a field by three different seeding devices. A …