Limits...
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.


Possible limitations when using a fixed sensor and advantages of using a smart flying sensor for a loading application during forage harvesting.
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f11-sensors-15-16688: Possible limitations when using a fixed sensor and advantages of using a smart flying sensor for a loading application during forage harvesting.

Mentions: Achieving a flat material profile inside the trailer is possible under good visibility conditions and flat terrains, with the additional help of a system to supervise the loading process as depicted in Figure 11a. Nevertheless, some limitations appear when placing the sensor (i.e., camera) in the arm of the harvester machine. For example, noise in the images due to interference coming from dust, the small particles of chopped material and mechanical vibrations (Figure 11b) or a decrease of visibility due to an increase of the distance between the vehicles (Figure 11c). These difficulties can be diminished by using a flying sensor, because the camera can be placed in a better position, thus increasing visibility inside the trailer despite dust or a large distance between the vehicles (Figure 11d). Additional advantages can be obtained if other information is extracted from images during flight (e.g., density of the crop in front of the harvester to regulate the harvester speed properly) or if other sensors are installed.


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)

Possible limitations when using a fixed sensor and advantages of using a smart flying sensor for a loading application during forage harvesting.
© Copyright Policy
Related In: Results  -  Collection

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

f11-sensors-15-16688: Possible limitations when using a fixed sensor and advantages of using a smart flying sensor for a loading application during forage harvesting.
Mentions: Achieving a flat material profile inside the trailer is possible under good visibility conditions and flat terrains, with the additional help of a system to supervise the loading process as depicted in Figure 11a. Nevertheless, some limitations appear when placing the sensor (i.e., camera) in the arm of the harvester machine. For example, noise in the images due to interference coming from dust, the small particles of chopped material and mechanical vibrations (Figure 11b) or a decrease of visibility due to an increase of the distance between the vehicles (Figure 11c). These difficulties can be diminished by using a flying sensor, because the camera can be placed in a better position, thus increasing visibility inside the trailer despite dust or a large distance between the vehicles (Figure 11d). Additional advantages can be obtained if other information is extracted from images during flight (e.g., density of the crop in front of the harvester to regulate the harvester speed properly) or if other sensors are installed.

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.