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GNSS PRECISE POINT POSITIONING FOR AUTONOMOUS ROBOT NAVIGATION IN GREENHOUSE ENVIRONMENT FOR INTEGRATED PEST MONITORING

Authors: Pattinson, Michael; Tiwari, Smita; Yuheng Zheng; Campo-Cossio, María; Arnau, Raúl; Obregón, David; Ansuategui, Ander; +6 Authors

GNSS PRECISE POINT POSITIONING FOR AUTONOMOUS ROBOT NAVIGATION IN GREENHOUSE ENVIRONMENT FOR INTEGRATED PEST MONITORING

Abstract

GreenPatrol robot is an autonomous robotic solution for early detection and control of pests in greenhouses. The importance of robot precise positioning inside the greenhouse is a key aspect to endow the robot with the ability to scout the environment, precisely register the detected pest location into accurate maps and to allow the later treatment. Greenhouses are a challenging environment in terms of multipath and signal blockage due to its metal-reinforced complex structures of glass or polycarbonate. GreenPatrol robot localization takes advantage of the higher accuracy and the multiple signal frequencies provided by the European Global Navigation Satellite System (EGNSS) of the Galileo constellation (E5Alt BOC), by means of precise positioning techniques combined with inertial measurement sensors, odometry and maps to provide an accurate global localization mechanism. This paper shows the results of a comparative analysis carried out in a Greenhouse environment in order to evaluate the performance of different processing techniques such as Precise Point Positioning (PPP) and Post Processed Kinematic (PPK). The purpose of this analysis is to study the advantages of the use of Galileo new signals and to determine the best global localization solution for the GreenPatrol robot. The results experimentally show that the use of PPP Galileo E5 AltBOC signal in a multi-constellation solution offers better signal quality and better positioning performance for the intended environment.

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Keywords

GNSS, Precise Point Positioning, Precision Farming, Integrated Pest Monitoring

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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