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Towards the Development of a Smart Flying Sensor: Illustration in the Field of Precision Agriculture.

Hernandez A, Murcia H, Copot C, De Keyser R - Sensors (Basel) (2015)

Bottom Line: Sensing is an important element to quantify productivity, product quality and to make decisions.Novelty lies in: (1) the development of a position-estimation method with time delay compensation based on inertial measurement unit (IMU) sensors and image processing; (2) a method to build a 3D map using information obtained from a regular camera; and (3) the design and implementation of a path-following control algorithm using model predictive control (MPC).Experimental results on a lab-scale system validate the effectiveness of the proposed methodology.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Energy, Systems and Automation (EeSA), Ghent University, 9000 Ghent, Belgium. Andres.Hernandez@ugent.be.

ABSTRACT
Sensing is an important element to quantify productivity, product quality and to make decisions. Applications, such as mapping, surveillance, exploration and precision agriculture, require a reliable platform for remote sensing. This paper presents the first steps towards the development of a smart flying sensor based on an unmanned aerial vehicle (UAV). The concept of smart remote sensing is illustrated and its performance tested for the task of mapping the volume of grain inside a trailer during forage harvesting. Novelty lies in: (1) the development of a position-estimation method with time delay compensation based on inertial measurement unit (IMU) sensors and image processing; (2) a method to build a 3D map using information obtained from a regular camera; and (3) the design and implementation of a path-following control algorithm using model predictive control (MPC). Experimental results on a lab-scale system validate the effectiveness of the proposed methodology.

No MeSH data available.


Quadrotor layers: the low layer represents the electronic assistance and the embedded operative system on the AR.Drone; the high layer represents the pilot (natively a smart device i.e., iPhone).
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f1-sensors-15-16688: Quadrotor layers: the low layer represents the electronic assistance and the embedded operative system on the AR.Drone; the high layer represents the pilot (natively a smart device i.e., iPhone).

Mentions: This on-board black-box system in the AR.Drone 2.0 can be considered the low layer. The high layer is represented by the command station, which defines the references to the internal controllers located in the low layer. A schematic representation is depicted in Figure 1.


Towards the Development of a Smart Flying Sensor: Illustration in the Field of Precision Agriculture.

Hernandez A, Murcia H, Copot C, De Keyser R - Sensors (Basel) (2015)

Quadrotor layers: the low layer represents the electronic assistance and the embedded operative system on the AR.Drone; the high layer represents the pilot (natively a smart device i.e., iPhone).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541901&req=5

f1-sensors-15-16688: Quadrotor layers: the low layer represents the electronic assistance and the embedded operative system on the AR.Drone; the high layer represents the pilot (natively a smart device i.e., iPhone).
Mentions: This on-board black-box system in the AR.Drone 2.0 can be considered the low layer. The high layer is represented by the command station, which defines the references to the internal controllers located in the low layer. A schematic representation is depicted in Figure 1.

Bottom Line: Sensing is an important element to quantify productivity, product quality and to make decisions.Novelty lies in: (1) the development of a position-estimation method with time delay compensation based on inertial measurement unit (IMU) sensors and image processing; (2) a method to build a 3D map using information obtained from a regular camera; and (3) the design and implementation of a path-following control algorithm using model predictive control (MPC).Experimental results on a lab-scale system validate the effectiveness of the proposed methodology.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Energy, Systems and Automation (EeSA), Ghent University, 9000 Ghent, Belgium. Andres.Hernandez@ugent.be.

ABSTRACT
Sensing is an important element to quantify productivity, product quality and to make decisions. Applications, such as mapping, surveillance, exploration and precision agriculture, require a reliable platform for remote sensing. This paper presents the first steps towards the development of a smart flying sensor based on an unmanned aerial vehicle (UAV). The concept of smart remote sensing is illustrated and its performance tested for the task of mapping the volume of grain inside a trailer during forage harvesting. Novelty lies in: (1) the development of a position-estimation method with time delay compensation based on inertial measurement unit (IMU) sensors and image processing; (2) a method to build a 3D map using information obtained from a regular camera; and (3) the design and implementation of a path-following control algorithm using model predictive control (MPC). Experimental results on a lab-scale system validate the effectiveness of the proposed methodology.

No MeSH data available.