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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.


Sensors used for position estimation of the AR.Drone in the x, y plane.
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f5-sensors-15-16688: Sensors used for position estimation of the AR.Drone in the x, y plane.

Mentions: Figure 5 describes the sensor's possibilities to estimate the position on the (X, Y) plane, each having positive and negative characteristics. On the one hand, odometry allows position estimation with almost no delay, but it suffers from drifting, producing an error that increases with time. On the other hand, to compute the position estimation based on optical measurements requires the use of patterns located in known positions, introducing also the problem of the additional time delays and noisy signals due to varying light conditions. An additional obvious difficulty is that once the pattern is out of the image, it is not possible to estimate the position.


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)

Sensors used for position estimation of the AR.Drone in the x, y plane.
© Copyright Policy
Related In: Results  -  Collection

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

f5-sensors-15-16688: Sensors used for position estimation of the AR.Drone in the x, y plane.
Mentions: Figure 5 describes the sensor's possibilities to estimate the position on the (X, Y) plane, each having positive and negative characteristics. On the one hand, odometry allows position estimation with almost no delay, but it suffers from drifting, producing an error that increases with time. On the other hand, to compute the position estimation based on optical measurements requires the use of patterns located in known positions, introducing also the problem of the additional time delays and noisy signals due to varying light conditions. An additional obvious difficulty is that once the pattern is out of the image, it is not possible to estimate the position.

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.