Robotic Fertilisation Using Localisation Systems Based on Point Clouds in Strip-Cropping Fields

Cruz Ulloa, Christyan and Krus, Anne and Barrientos Cruz, Antonio and Cerro Giner, Jaime Del and Valero Ubierna, Constantino (2020). Robotic Fertilisation Using Localisation Systems Based on Point Clouds in Strip-Cropping Fields. "Agronomy", v. 11 (n. 1); p. 11. ISSN 2073-4395. https://doi.org/10.3390/agronomy11010011.

Description

Title: Robotic Fertilisation Using Localisation Systems Based on Point Clouds in Strip-Cropping Fields
Author/s:
  • Cruz Ulloa, Christyan
  • Krus, Anne
  • Barrientos Cruz, Antonio
  • Cerro Giner, Jaime Del
  • Valero Ubierna, Constantino
Item Type: Article
Título de Revista/Publicación: Agronomy
Date: 23 December 2020
ISSN: 2073-4395
Volume: 11
Subjects:
Freetext Keywords: organic farming; ROS; strip cropping; robotic systems; point cloud localisation; lidar
Faculty: Centro de Automática y Robótica (CAR) UPM-CSIC
Department: Ingeniería Agroforestal
UPM's Research Group: Robótica y Cibernética RobCib, Técnicas Avanzadas en Agroalimentación LPF-TAGRALIA
Creative Commons Licenses: Recognition - Share

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Abstract

The use of robotic systems in organic farming has taken on a leading role in recent years; the Sureveg CORE Organic Cofund ERA-Net project seeks to evaluate the benefits of strip-cropping to produce organic vegetables. This includes, among other objectives, the development of a robotic tool that facilitates the automation of the fertilisation process, allowing the individual treatment (at the plant level). In organic production, the slower nutrient release of the used fertilisers poses additional difficulties, as a tardy detection of deficiencies can no longer be corrected. To improve the detection, as well as counter the additional labour stemming from the strip-cropping configuration, an integrated robotic tool is proposed to detect individual crop deficiencies and react on a single-crop basis. For the development of this proof-of-concept, one of the main objectives of this work is implementing a robust localisation method within the vegetative environment based on point clouds, through the generation of general point cloud maps (G-PC) and local point cloud maps (L-PC) of a crop row. The plants’ geometric characteristics were extracted from the G-PC as a framework in which the robot’s positioning is defined. Through the processing of real-time lidar data, the L-PC is then defined and compared to the predefined reference system previously deduced. Both subsystems are integrated with ROS (Robot Operating System), alongside motion planning, and an inverse kinematics CCD (Cyclic Coordinate Descent) solver, among others. Tests were performed using a simulated environment of the crop row developed in Gazebo, followed by actual measurements in a strip-cropping field. During real-time data-acquisition, the localisation error is reduced from 13 mm to 11 mm within the first 120 cm of measurement. The encountered real-time geometric characteristics were found to coincide with those in the G-PC to an extend of 98.6%

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainC1920280080SUREVEGAntonio BarrientosERANET Core Organic Cofund "Strip-cropping and recycling for biodiverse and resource-efficient intensive vegetable production
Madrid Regional GovernmentS2018/NMT-4331Madrid Robotics DIHAntonio BarrientosMadrid Robotics Digital Innovation Hub

More information

Item ID: 65844
DC Identifier: http://oa.upm.es/65844/
OAI Identifier: oai:oa.upm.es:65844
DOI: 10.3390/agronomy11010011
Official URL: https://doi.org/10.3390/agronomy11010011
Deposited by: Profesor Constantino Valero Ubierna
Deposited on: 08 Jan 2021 06:54
Last Modified: 08 Jan 2021 06:56
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