A New GNSS Relative Positioning Algorithm Based on Alternative Use of the Positions of Reference Receivers

Hasegawa, Hirokazu; Okuda, Goshi; Kubo, Yukihiro; Sugimoto, Sueo

doi:10.5687/sss.2018.137

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Various methods are employed by satellites to obtain high positioning accuracy. To achieve this, we acquired multiple position information and calculated the relative positions [32], [33], [34]. Additionally, instead of positioning based on signals from satellites, the wavelength of the received waveform is used to attain high positioning accuracy with an error of a few centimeters.…”

Section: Drone Positioningmentioning

confidence: 99%

Autonomous Drone-Based Pollination System Using AI Classifier to Replace Bees for Greenhouse Tomato Cultivation

Hiraguri,

Shimizu,

Kimura

et al. 2023

IEEE Access

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In greenhouse tomato cultivation, three primary methods of flower pollination exist: insect pollination, physical pollination by vibrating flowers, and artificial pollination using hormone-based chemicals. Insect pollination, the natural method, involves insects (e.g., honeybees) vibrating flowers to collect pollen and nectar. This paper proposes an alternate approach, using small drones to search and pollinate flowers in place of bees autonomously. We report field experiments conducted using these drone technologies. The drone must locate flowers ready for pollination. We developed an artificial intelligence (AI) image classification system (AI classifier) using machine learning to identify these flowers. Equipped with an AI classifier, the drone searches for flowers through autonomous flight and positioning technology. Upon identifying a suitable flower during its search, the drone makes contact to pollinate it. Integrating AI-based flower detection, autonomous flight control for flower search, and a pollination control device allows the drone to perform pollination. This study devises these technologies, implements them in a drone, and evaluates their effectiveness through a pollination experiment.

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Section: Drone Positioningmentioning

confidence: 99%

Autonomous Drone-Based Pollination System Using AI Classifier to Replace Bees for Greenhouse Tomato Cultivation

Hiraguri,

Shimizu,

Kimura

et al. 2023

IEEE Access

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VPPP Algorithms with Multiple Antennas by Applying MADOCA Products

Hasegawa

Mimura

Shibayama

et al. 2019

Stochastic Systems Theory and its Applications (SSS)

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According to our previous research works, we present the algorithms of the VPPP (Very Precise Point Positions) using multiple GNSS antennas with known distances each other. VPPP algorithms contain the very precise GNSS positioning as well as the so-called GNSS Gyro by estimating the multiple baseline vectors from the reference antenna to other antennas. In this paper, we attempt to implement the satellite orbits and clocks correction data from the MADOCA Products which had been developed by JAXA, into our VPPP algorithms. Further, finally, we show the positioning accuracy as well as the estimation accuracy of the Euler's angles in the GNSS Gyro, in comparison with using the so-called GPS broadcast ephemeris.

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A New GNSS Relative Positioning Algorithm Based on Alternative Use of the Positions of Reference Receivers

Cited by 2 publications

References 9 publications

Autonomous Drone-Based Pollination System Using AI Classifier to Replace Bees for Greenhouse Tomato Cultivation

Autonomous Drone-Based Pollination System Using AI Classifier to Replace Bees for Greenhouse Tomato Cultivation

VPPP Algorithms with Multiple Antennas by Applying MADOCA Products

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