Gotowe bibliografie tematyczne / Trainable robotic-arm

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  1. Artykuły w czasopismach
  2. Streszczenia konferencji

Gotowa bibliografia na temat „Trainable robotic-arm”

Autor: Grafiati
Data publikacji: 3 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Trainable robotic-arm"

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Arpitha, H. V. K. M. Shweta Nikhil S. Pattar Rahul Kuntoji Chetan B. V. "Arduino Based Trainable Robotic Arm." International Journal for Research Trends and Innovation An International Open Access Journal Published In: Volume 2 Issue 5, May-2017, Published In: Volume 2 Issue 5, May-2017 (2017): page no.337–339. https://doi.org/10.5281/zenodo.807564.

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The popular concept of a robot is of a machine that looks and works like a human being. The industry is moving from current state of automation to robotization, to increase productivity and to deliver uniform quality. A trainable robotic arm is proposed in this paper. The robotic arm is implemented based on the teach function of the arduino. The proposed robotic arm is capable of learning different movements and reproduces them as required. The industrial robots of today may need this type of capabilities to meet the increasing needs of the current trends.
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Hutapea, Marshal, Mhd Erpandi Dalimunthe, and Yuliarman Saragih. "RANCANG BANGUN TRAINABLE ROBOT ARM BERBASIS ARDUINO MEGA 2560 SEBAGAI MEDIA PRAKTIKUM DI LABORATORIUM TEKNIK ELEKTRO UNIVERSITAS PEMBANGUNAN PANCABUDI MEDAN." Aisyah Journal Of Informatics and Electrical Engineering (A.J.I.E.E) 6, no. 2 (2024): 151–59. http://dx.doi.org/10.30604/jti.v6i2.278.

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The design of the Trainable Robot Arm is carried out as a learning medium in the lab. Electrical Engineering University of Development Pancabudi Medan. Trainable Robot Arm is a robotic arm designed to facilitate the process of robot applications in carrying out object transfer tasks without the need to do complex programming every time you want to change the robot's movements. The design of this arm robot is divided into 3 parts, the first is the design and manufacture of robot mechanics, the second is the design and manufacture of robot control/electricity and the third is the manufacture of
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Serdaris, Panagiotis, Konstantinos Spinthiropoulos, Michael Agrafiotis, and Athanasios Zisopoulos. "The Minimum Agriculture-Chunk as an Elementary Data Science Component in ADAM, a Micro Targeted, Trainable, Modular, Multipurpose System for Land Farming." Journal of Agricultural Studies 6, no. 4 (2018): 84. http://dx.doi.org/10.5296/jas.v6i4.14116.

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The poor Data Science support of agriculture brought us to our main idea of the research is to analyze all micro-works for every plant or tree. Then we proceed to specify targeted actions for harvest collection, micro spraying and hundreds similar simple actions. Initially we collect data from the farm. The airborne, land and underwater unmanned vehicles scan the field area with customized various sensors and cameras in various multi spectral modes. The result is minimum agro-chunk Four-Dimensional model. The unmanned vehicle on the field area receives target data. It is equipped with a genera
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Aayush, Kumar Agarwal, Kishore Kaushal, Kishor Kunal, Sagar, and Chavan Pradyumna. "Automatic Weed Detection Robot." International Journal of Innovative Science and Research Technology 8, no. 5 (2023): 45–66. https://doi.org/10.5281/zenodo.7922767.

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Automated processes in the field of agriculture have become increasingly reliable and efficient. Farmers face many difficulties due shortage of manpower since it is a time-consuming and tedious task. Robotic systems integrated with various control methods can be very useful in doing repetitive work, such as the seed-sowing process where the same movement is continuous. This project utilizes a robot that acts as a cost-effective system to detect weeds in agricultural fields. An automated system is to develop a trainable-automatic robot that helps in removing unwanted weeds on agricultural field
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Ye, Chengze, Linda-Sophie Schneider, Yipeng Sun, Mareike Thies, Siyuan Mei, and Andreas Maier. "DRACO: differentiable reconstruction for arbitrary CBCT orbits." Physics in Medicine & Biology, February 27, 2025. https://doi.org/10.1088/1361-6560/adbb50.

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Abstract Objective: This study introduces a novel method for reconstructing cone beam computed tomography (CBCT) images for arbitrary orbits, addressing the computational and memory challenges associated with traditional iterative reconstruction algorithms. Approach: The proposed method employs a differentiable shift-variant filtered backprojection neural network, optimized for arbitrary trajectories. By integrating known operators into the learning model, the approach minimizes the number of trainable parameters while enhancing model interpretability. This framework adapts seamlessly to speci
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Streszczenia konferencji na temat "Trainable robotic-arm"

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Zaheer, Adam, Durga Sundaram, and Kiran George. "Trainable Robotic Arm for Disability Assistance." In 2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). IEEE, 2018. http://dx.doi.org/10.1109/uemcon.2018.8796741.

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Memon, Muhammad Hunain, Muhammad Hammad Memon, Aakash kumar, Syeda Munazza Marium, and Jalaluddin Khan. "Prototype of Smart Trainable Robotic Arm." In 2019 IEEE 5th International Conference for Convergence in Technology (I2CT). IEEE, 2019. http://dx.doi.org/10.1109/i2ct45611.2019.9033778.

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Sundaram, Durga, Abhishek Sarode, and Kiran George. "Vision-Based Trainable Robotic Arm for Individuals with Motor Disability." In 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). IEEE, 2019. http://dx.doi.org/10.1109/uemcon47517.2019.8993075.

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