Precise Navigation of Small Agricultural Robots in Sensitive Areas with a Smart Plant Camera

Dworak, Volker; Huebner, M.; Selbeck, Jörn

doi:10.3390/jimaging1010115

Cited by 5 publications

(2 citation statements)

References 22 publications

(32 reference statements)

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“…These "things" can be part of complex systems for healthcare [18][19][20][21][22][23][24][25], smart agriculture [26], science experiments [27], vehicles [28][29][30][31][32], satellites [33][34][35], domotics [36,37], robots/drones or industrial machines [38][39][40], telecom or surveillance apparatus [41][42][43], sport [44,45] or energy/smart-grid systems [46][47][48][49][50], and/or consumer electronics [51][52][53][54][55][56][57]. These units are usually wireless nodes, working at sub-GHz frequencies or in the range 2.4 GHz to 6 GHz [58][59][60][61], or at mm-waves [62][63][64].…”

Section: Introductionmentioning

confidence: 99%

Fully-Integrated Converter for Low-Cost and Low-Size Power Supply in Internet-of-Things Applications

Gutiérrez¹

2017

Electronics

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Abstract:The paper presents a fully-integrated and universal DC/DC converter to minimize cost and size of power supply systems in wireless nodes for Internet-of-Things (IoT) applications. The proposed converter avoids the use of inductors and is made by a cascade of switching capacitor stages, implementing both step-down and step-up converting ratios, which regulate input sources from 1 V to 60 V to a voltage of about 4 V. Multiple linear regulators are placed at the end of the cascade to provide multiple and stable output voltages for loads such as memories, sensors, processors, wireless transceivers. The multi-output power converter has been integrated in a Bipolar-CMOS-DMOS (BCD) 180 nm technology. As case study, the generation of 3 output voltages has been considered (3 V, 2.7 V, and 1.65 V with load current requirements of 0.3 A, 0.3 A, and 0.12 A, respectively). Thanks to the adoption of a high switching frequency, up to 5 MHz, the only needed passive components are flying capacitors, whose size is below 10 nF, and buffer capacitors, whose size is below 100 nF. These capacitors can be integrated on top of the chip die, creating a 3D structure. This way, the size of the power management unit for IoT and CPS nodes is limited at 18 mm 2 . The proposed converter can also be used with changing input power sources, like power harvesting systems and/or very disturbed power supplies.

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Section: Introductionmentioning

confidence: 99%

Fully-Integrated Converter for Low-Cost and Low-Size Power Supply in Internet-of-Things Applications

Gutiérrez¹

2017

Electronics

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“…A variable field of view setup has been experimented for guidance in [22]. An adapted NDVI was used in [60] for distinguishing soil and plants trough a camera-based system for precise guidance in small vehicles.…”

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confidence: 99%

Machine-Vision Systems Selection for Agricultural Vehicles: A Guide

et al. 2016

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Machine vision systems are becoming increasingly common onboard agricultural vehicles (autonomous and non-autonomous) for different tasks. This paper provides guidelines for selecting machine-vision systems for optimum performance, considering the adverse conditions on these outdoor environments with high variability on the illumination, irregular terrain conditions or different plant growth states, among others. In this regard, three main topics have been conveniently addressed for the best selection: (a) spectral bands (visible and infrared); (b) imaging sensors and optical systems (including intrinsic parameters) and (c) geometric visual system arrangement (considering extrinsic parameters and stereovision systems). A general overview, with detailed description and technical support, is provided for each topic with illustrative examples focused on specific applications in agriculture, although they could be applied in different contexts other than agricultural. A case study is provided as a result of research in the RHEA (Robot Fleets for Highly Effective Agriculture and Forestry Management) project for effective weed control in maize fields (wide-rows crops), funded by the European Union, where the machine vision system onboard the autonomous vehicles was the most important part of the full perception system, where machine vision was the most relevant. Details and results about crop row detection, weed patches identification, autonomous vehicle guidance and obstacle detection are provided together with a review of methods and approaches on these topics.

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