A distributed control strategy of multi quadrotor UAV systems is proposed in this work to address area coverage in precision agriculture. It adopts a region-based control approach by restricting the motion of UAVs wit...
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A distributed control strategy of multi quadrotor UAV systems is proposed in this work to address area coverage in precision agriculture. It adopts a region-based control approach by restricting the motion of UAVs within a desired dynamical region with planned dynamics while providing collision avoidance between UAVs. Voronoi partitions that can be computed in a distributed way are used to determine UAVs positions within the moving region which provides a robust and scalable solution. Simulations are done using Gazebo and Robot Operating System (ROS) to evaluate the performance of the proposed method showing good results. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
3D vision systems will play an important role in next-generation dairy farming due to the sensing capabilities they provide in the automation of animal husbandry tasks such as the monitoring, herding, feeding, milking...
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3D vision systems will play an important role in next-generation dairy farming due to the sensing capabilities they provide in the automation of animal husbandry tasks such as the monitoring, herding, feeding, milking and bedding of animals This paper will review 3D computer vision systems and techniques that are and may be implemented in Precision Dairy Farming. This review will include evaluations of the applicability of Time of Flight and Streoscopic Vision systems to agricultural applications as well as a breakdown of the categories of computer vision algorithms which are being explored in a variety of use cases. These use cases range from robotic platforms such as milking robots and autonomous vehicles which must interact closely and safely with animals to intelligent systems which can identify dairy cattle and detect deviations in health indicators such as Body Condition Score and Locomotion Score. Upon analysis of each use case, it is apparent that systems which can operate in unconstrained environments and adapt to variations in herd characteristics, weather conditions, farmyard layout and different scenarios in animal-robot interaction are required. Considering this requirement, this paper proposes the application of techniques arising from the emerging field of research in Artificial Intelligence that is Geometric Deep Learning. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
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