Types of Unmanned Aerial Vehicles (UAVs), Sensing Technologies, and Software for Agricultural Applications

Kakarla, Sri Charan; Ampatzidis, Yiannis

doi:10.32473/edis-ae565-2021

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Many approaches try to provide that by simultaneously minimizing the demanded time and energy to perform a specific mission. With the objective to collect data out of the whole region, one or more UAV(s) that could be multi-rotor, fixed-wing, single-rotor or hybrid-VTOL [25] are utilized, each of them carrying a sensor with certain specifications (e.g., horizontal and vertical resolution (res h , res v ) and field of view (HFOV, VFOV, usually provided by the sensors' manufacturers in degrees)). The post-processing data analysis required for a specific application (e.g., generation of orthomosaics, elevation models, 3D representations, etc.)…”

Section: Problem Formulation 21 Cpp For Uavs' Remote Sensing Operationsmentioning

confidence: 99%

Systematically Improving the Efficiency of Grid-Based Coverage Path Planning Methodologies in Real-World UAVs’ Operations

Apostolidis

Vougiatzis

Kapoutsis

et al. 2023

Drones

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This work focuses on the efficiency improvement of grid-based Coverage Path Planning (CPP) methodologies in real-world applications with UAVs. While several sophisticated approaches are met in literature, grid-based methods are not commonly used in real-life operations. This happens mostly due to the error that is introduced during the region’s representation on the grid, a step mandatory for such methods, that can have a great negative impact on their overall coverage efficiency. A previous work on UAVs’ coverage operations for remote sensing, has introduced a novel optimization procedure for finding the optimal relative placement between the region of interest and the grid, improving the coverage and resource utilization efficiency of the generated trajectories, but still, incorporating flaws that can affect certain aspects of the method’s effectiveness. This work goes one step forward and introduces a CPP method, that provides three different ad-hoc coverage modes: the Geo-fenced Coverage Mode, the Better Coverage Mode and the Complete Coverage Mode, each incorporating features suitable for specific types of vehicles and real-world applications. For the design of the coverage trajectories, user-defined percentages of overlap (sidelap and frontlap) are taken into consideration, so that the collected data will be appropriate for applications like orthomosaicing and 3D mapping. The newly introduced modes are evaluated through simulations, using 20 publicly available benchmark regions as testbed, demonstrating their stenghts and weaknesses in terms of coverage and efficiency. The proposed method with its ad-hoc modes can handle even the most complex-shaped, concave regions with obstacles, ensuring complete coverage, no-sharp-turns, non-overlapping trajectories and strict geo-fencing. The achieved results demonstrate that the common issues encountered in grid-based methods can be overcome by considering the appropriate parameters, so that such methods can provide robust solutions in the CPP domain.

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Section: Problem Formulation 21 Cpp For Uavs' Remote Sensing Operationsmentioning

confidence: 99%