In the last years, unmanned aerial vehicles are becoming a reality in the context of precision agriculture, mainly for monitoring, patrolling and remote sensing tasks, but also for 3D map reconstruction. In this paper, we present an innovative approach where a fleet of unmanned aerial vehicles is exploited to perform remote sensing tasks over an apple orchard for reconstructing a 3D map of the field, formulating the covering control problem to combine the position of a monitoring target and the viewing angle. Moreover, the objective function of the controller is defined by an importance index, which has been computed from a multi-spectral map of the field, obtained by a preliminary flight, using a semantic interpretation scheme based on a convolutional neural network. This objective function is then updated according to the history of the past coverage states, thus allowing the drones to take situation-adaptive actions. The effectiveness of the proposed covering control strategy has been validated through simulations on a Robot Operating System.

3D map reconstruction of an orchard using an angle-aware covering control strategy

Lorenzo Comba;Alessandro Biglia;Paolo Gay;
2022-01-01

Abstract

In the last years, unmanned aerial vehicles are becoming a reality in the context of precision agriculture, mainly for monitoring, patrolling and remote sensing tasks, but also for 3D map reconstruction. In this paper, we present an innovative approach where a fleet of unmanned aerial vehicles is exploited to perform remote sensing tasks over an apple orchard for reconstructing a 3D map of the field, formulating the covering control problem to combine the position of a monitoring target and the viewing angle. Moreover, the objective function of the controller is defined by an importance index, which has been computed from a multi-spectral map of the field, obtained by a preliminary flight, using a semantic interpretation scheme based on a convolutional neural network. This objective function is then updated according to the history of the past coverage states, thus allowing the drones to take situation-adaptive actions. The effectiveness of the proposed covering control strategy has been validated through simulations on a Robot Operating System.
2022
7th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture (AGRICONTROL)
Munich, Germany
September 14-16, 2022
IFAC-PapersOnLine
Elsevier
55
271
276
https://www.sciencedirect.com/science/article/pii/S2405896322027847
Precision farming, Agricultural robotics, Autonomous vehicles in agriculture, Covering control, Crop modeling
Martina Mammarella, Cesare Donati, Takumi Shimizu, Masaya Suenaga, Lorenzo Comba, Alessandro Biglia, Kuniaki Uto, Takeshi Hatanaka, Paolo Gay, Fabrizio Dabbene
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1884522
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