Cooperative Agricultural Operations of Aerial and Ground Unmanned Vehicles

Precision agriculture comprises a set of technologies that combines sensors, information systems, enhanced machinery, and informed management to optimize production by accounting for variability and uncertainties within agricultural systems. Autonomous ground and aerial vehicle can lead to favorable improvements in management by performing in-field tasks in a time-effective way. Greater benefits can be achieved by allowing cooperation and collaborative action among Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs). A multi-phase approach is here proposed, where each unmanned vehicle involved has been conceived and will be designed to implement innovative solutions for automated navigation and infield operations within a complex irregular and unstructured scenario as vineyards in sloped terrains.

[1]  Lorenzo Comba,et al.  Individual plant definition and missing plant characterization in vineyards from high-resolution UAV imagery , 2017 .

[2]  Gonzalo Pajares,et al.  Fleets of robots for environmentally-safe pest control in agriculture , 2017, Precision Agriculture.

[3]  Lorenzo Comba,et al.  Unsupervised detection of vineyards by 3D point-cloud UAV photogrammetry for precision agriculture , 2018, Comput. Electron. Agric..

[4]  Roland Siegwart,et al.  Model Predictive Control for Trajectory Tracking of Unmanned Aerial Vehicles Using Robot Operating System , 2017 .

[5]  Chun Chang,et al.  Develop an unmanned aerial vehicle based automatic aerial spraying system , 2016, Comput. Electron. Agric..

[6]  Francisco R. Feito-Higueruela,et al.  Automatic Grapevine Trunk Detection on UAV-Based Point Cloud , 2020, Remote. Sens..

[7]  Marcello Chiaberge,et al.  UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture † , 2020, Sensors.

[8]  Frank Allgöwer,et al.  An Offline-Sampling SMPC Framework with Application to Automated Space Maneuvers , 2018, ArXiv.

[9]  Cristina Tortia,et al.  2D and 3D data fusion for crop monitoring in precision agriculture , 2019, 2019 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor).

[10]  Ning Li,et al.  An Improved Dynamic Window Approach Integrated Global Path Planning , 2019, IEEE International Conference on Robotics and Biomimetics.

[11]  Fabrizio Dabbene,et al.  Semantic interpretation and complexity reduction of 3D point clouds of vineyards , 2020 .

[12]  A. Calera,et al.  Remote sensing–based soil water balance for irrigation water accounting at plot and water user association management scale , 2020 .

[13]  G. Valmórbida,et al.  Design and parameter tuning of a robust model predictive controller for UAVs , 2017 .

[14]  Robin Gebbers,et al.  Precision Agriculture and Food Security , 2010, Science.

[15]  C. Tortia,et al.  Leaf Area Index evaluation in vineyards using 3D point clouds from UAV imagery , 2019, Precision Agriculture.

[16]  Girish Chowdhary,et al.  Agbots: Weeding a field with a team of autonomous robots , 2019, Comput. Electron. Agric..

[17]  Roland Siegwart,et al.  Robust Model Predictive Flight Control of Unmanned Rotorcrafts , 2016, J. Intell. Robotic Syst..

[18]  Giorgio Guglieri,et al.  Guidance and control algorithms for mini UAV autopilots , 2017 .

[19]  Marcello Chiaberge,et al.  Comparison of Satellite and UAV-Based Multispectral Imagery for Vineyard Variability Assessment , 2019, Remote. Sens..

[20]  D. Rose,et al.  Agriculture 4.0: Broadening Responsible Innovation in an Era of Smart Farming , 2018, Front. Sustain. Food Syst..

[21]  Lorenzo Comba,et al.  Cost-effective visual odometry system for vehicle motion control in agricultural environments , 2019, Comput. Electron. Agric..

[22]  Elisa Capello,et al.  Tube-based robust model predictive control for spacecraft proximity operations in the presence of persistent disturbance , 2018, Aerospace Science and Technology.

[23]  Elisa Capello,et al.  Tube-Based Robust MPC Processor-in-the-Loop Validation for Fixed-Wing UAVs , 2020, Journal of Intelligent & Robotic Systems.

[24]  Ahmad Fikri Abdullah,et al.  Droplet deposition density of organic liquid fertilizer at low altitude UAV aerial spraying in rice cultivation , 2019, Comput. Electron. Agric..

[25]  Rebecca L. Whetton,et al.  Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy , 2016 .

[26]  Alessandro Matese,et al.  NDVI-based vigour maps production using automatic detection of vine rows in ultra-high resolution aerial images , 2015 .

[27]  Emilio Gil,et al.  Ground Deposition and Airborne Spray Drift Assessment in Vineyard and Orchard: The Influence of Environmental Variables and Sprayer Settings , 2017 .