Purpose
– This paper aims to present a prototype of a capacitive angular-position sensor which exploits advantages of flexible/printed electronics. The novelty of the sensor is that the capacitor structure is placed at the circumference of the rotor and stator, that it posses two channels (capacitor structures) electrically shifted for p/4 and that the rotor is common for both channels. The electrodes of the sensing capacitor are digitated, providing a triangular transfer function.
Design/methodology/approach
– This sensor prototype consists of two flexible inkjet-printed silver electrodes forming a cylindrical capacitor structure. One of them is wrapped around the stator and another is wrapped around the rotor part of a simple mechanical platform used to precisely adjust the angular displacement.
Findings
– The capacitance as a function of angular position was measured using an inductance capacitance impedance (LCZ) Meter, and results are presented for a full-turn measurement range. The experimental results are compared with analytical ones and very good agreement has been achieved.
Originality/value
– The proposed capacitive sensor structure can be used as an absolute or an incremental encoder with different resolutions, and it can be applied in automotive industry or robotics.
This paper presents an autonomous robotic system, an unmanned ground vehicle (UGV), for in-field soil sampling and analysis of nitrates. Compared to standard methods of soil analysis it has several advantages: each sample is individually analyzed compared to average sample analysis in standard methods; each sample is georeferenced, providing a map for precision base fertilizing; the process is fully autonomous; samples are analyzed in real-time, approximately 30 min per sample; and lightweight for less soil compaction. The robotic system has several modules: commercial robotic platform, anchoring module, sampling module, sample preparation module, sample analysis module, and communication module. The system is augmented with an in-house developed cloud-based platform. This platform uses satellite images, and an artificial intelligence (AI) proprietary algorithm to divide the target field into representative zones for sampling, thus, reducing and optimizing the number and locations of the samples. Based on this, a task is created for the robot to automatically sample at those locations. The user is provided with an in-house developed smartphone app enabling overview and monitoring of the task, changing the positions, removing and adding of the sampling points. The results of the measurements are uploaded to the cloud for further analysis and the creation of prescription maps for variable rate base fertilization.
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