• Title/Summary/Keyword: Intelligence Robot

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A Realization of FPGA-based Image Recognition System (FPGA기반 영상인식 시스템 구현)

  • Young Yun
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • 2022.11a
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    • pp.349-350
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    • 2022
  • Recently, AI (Artificial Intelligence) has been applied to various technologies such as automatic driving, robot and smart communication. Currently, AI system is developed by software-based method using tensor flow, and GPU (Graphic Processing Unit) is employed for processing unit. In this work, we developed an FPGA-based (Field Programmable Gate Array) AI system , and report on image recognition system to realize the AI system.

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Q&A Chatbot in Arabic Language about Prophet's Biography

  • Somaya Yassin Taher;Mohammad Zubair Khan
    • International Journal of Computer Science & Network Security
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    • v.24 no.3
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    • pp.211-223
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    • 2024
  • Chatbots have become very popular in our times and are used in several fields. The emergence of chatbots has created a new way of communicating between human and computer interaction. A Chatbot also called a "Chatter Robot," or conversational agent CA is a software application that mimics human conversations in its natural format, which contains textual material and oral communication with artificial intelligence AI techniques. Generally, there are two types of chatbots rule-based and smart machine-based. Over the years, several chatbots designed in many languages for serving various fields such as medicine, entertainment, and education. Unfortunately, in the Arabic chatbots area, little work has been done. In this paper, we developed a beneficial tool (chatBot) in the Arabic language which contributes to educating people about the Prophet's biography providing them with useful information by using Natural Language Processing.

A Study on Defense Robot Combat Concepts Using Fourth Industrial Revolution Technologies

  • Sang-Hyuk Park;Jae-Geon Lee;Moo-Chun Kim
    • International Journal of Advanced Culture Technology
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    • v.12 no.1
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    • pp.249-253
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    • 2024
  • The ultimate purpose of this study is as follows: The current primary concern in the defense sector revolves around how to strategically utilize Fourth Industrial Revolution technologies in combat. The Fourth Industrial Revolution denotes a shift towards an environment where automation and connectivity are maximized, driven by technologies such as artificial intelligence. Coined by Klaus Schwab in the 2015 Davos Forum, this term highlights the significant role of machine learning and artificial intelligence. Particularly, the military application of Fourth Industrial Revolution technologies is expected to be actively researched and implemented. Combat involves military actions between units, typically conducted as part of a larger war, with units striving to achieve one or more objectives. The concept of combat refers to the fundamental ideas of how units should engage with the enemy, both presently and in future scenarios, to achieve assigned objectives.

A Study on Methods to Train Experts in Robot and Artificial Intelligence-Based Data Signal Processing in Response to the Increased Use of Robots (로봇의 활용증가에 따른 로봇 및 인공지능 기반 데이터 신호처리 전문가 양성 방안에 관한 연구)

  • Chung-Ho Ju;Dae-Yeon Kim;Kyoung-Ho Kim;Tae-Woong Gwon;Dong-Seop Sohn
    • Journal of the Institute of Convergence Signal Processing
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    • v.25 no.2
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    • pp.58-66
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    • 2024
  • Robotics is a convergence technology with significant ripple effects across various industries, including manufacturing and services. Its importance has been increasingly underscored by advancements in artificial intelligence. As a crucial industry for addressing the challenges posed by a declining and aging production workforce and for enhancing manufacturing competitiveness, the training of robotics experts is now critically important. This paper examines the case of the Robot Job Innovation Center in Gumi City and proposes a strategy for training robotics experts and specialists in robot/AI-based signal processing. A core curriculum was carefully selected and implemented in actual educational settings, with key components necessary for developing a comprehensive educational framework detailed. The convergence of AI-based data signal processing and robotics represents a significant technological advancement poised to impact a wide array of industries. By proposing the comprehensive educational framework outlined in this paper, it is anticipated that related organizations will be able to effectively utilize these foundational elements to train experts in the field.

A Study on the Development of Industrial Field Technical Education Programs Integrating Robotics and Artificial Intelligence

  • Yong-Kwan Kwon
    • Journal of the Korea Society of Computer and Information
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    • v.29 no.8
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    • pp.209-221
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    • 2024
  • The purpose of this study is to develop technical education programs that integrate robotics and artificial intelligence for employees in the robotics industry. To achieve this, the current status of robotics education programs for employees at domestic robotics institutions was investigated, and a demand survey on integrated robotics and AI education was conducted for domestic robotics companies. Based on the results of the demand survey, a robotics and AI integration education program was developed, and the contents of the designed curriculum and the survey results were presented. The developed education program was applied to the actual industry, and through evaluations of participant satisfaction and learning effectiveness, it was confirmed that the program can be effectively utilized in the industry. Suggestions for further research were also made.

Motivation-Based Action Selection Mechanism with Bayesian Affordance Models for Intelligence Robot (지능로봇의 동기 기반 행동선택을 위한 베이지안 행동유발성 모델)

  • Son, Gwang-Hee;Lee, Sang-Hyoung;Huh, Il-Hong
    • Proceedings of the IEEK Conference
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    • 2009.05a
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    • pp.264-266
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    • 2009
  • A skill is defined as the special ability to do something well, especially as acquired by learning and practice. To learn a skill, a Bayesian network model for representing the skill is first learned. We will regard the Bayesian network for a skill as an affordance. We propose a soft Behavior Motivation(BM) switch as a method for ordering affordances to accomplish a task. Then, a skill is constructed as a combination of an affordance and a soft BM switch. To demonstrate the validity of our proposed method, some experiments were performed with GENIBO(Pet robot) performing a task using skills of Search-a-target-object, Approach-a-target-object, Push-up-in front of -a-target-object.

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A Framework for Cognitive Agents

  • Petitt, Joshua D.;Braunl, Thomas
    • International Journal of Control, Automation, and Systems
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    • v.1 no.2
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    • pp.229-235
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    • 2003
  • We designed a family of completely autonomous mobile robots with local intelligence. Each robot has a number of on-board sensors, including vision, and does not rely on global positioning systems The on-board embedded controller is sufficient to analyze several low-resolution color images per second. This enables our robots to perform several complex tasks such as navigation, map generation, or providing intelligent group behavior. Not being limited to playing the game of soccer and being completely autonomous, we are also looking at a number of other interesting scenarios. The robots can communicate with each other, e.g. for exchanging positions, information about objects or just the local states they are currently in (e.g. sharing their current objectives with other robots in the group). We are particularly interested in the differences between a behavior-based approach versus a traditional control algorithm at this still very low level of action.

A study on Precise Grasping Control of End-Effector for Parts Assembling and Handling (부품조립 및 핸들링을 위한 말단효과장치의 정밀 그리핑 제어에 관한 연구)

  • Ha, Un-Tae;Sung, Ki-Won;Kang, Eun-Wook
    • Journal of the Korean Society of Industry Convergence
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    • v.18 no.3
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    • pp.173-180
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    • 2015
  • In this paper, we propose a new precise control technology of robotic gripper for assembling and handling of part. When a robot manipulator interacts mechanically with its environment to perform tasks such as assembly or edge-finishing, the end-effector is thereby constrained by the environment. Therefore grasping force control is very important, since it increases safety due to monitoring of contact force. A comparison of various force control architecture is reported. Different force control methods can often be configured to achieve similar results for a given task, and the choice of control algorithm depends strongly on the application or on the characteristics of a particular robot. In the research, the adjustable gripping force can be controlled and improved the accuracy using the artificial intelligence techniques.

A study on the implementation of material handling system with part feeder (파트 피이더를 포함한 물류처리 시스템의 기술개발에 관한 연구)

  • 이원식;전흥주;이범희;고명삼
    • 제어로봇시스템학회:학술대회논문집
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    • 1990.10a
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    • pp.417-422
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    • 1990
  • For the robot manipulator in performing precision task, it is indispensable that the robot utilize the various sensors for intelligence. This paper presents the development and implementation of an integrated control system for the control of robotic manipulator, a feeder, a conveyor belt system, force/torque sensor system, and a photo sensor system. Micro controller board was constructed for hierarchical control of the system. To set up the program interactively, a user can make use of the software which includes the full-down menu and a dialog box. The user can make progress the program quickly and easily by a mouse. The related software was written in C and assembly languages.

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An inverse dynamic torque control of a six-jointed robot arm using neural networks (신경회로를 이용한 6축 로보트의 역동력학적 토크 제어)

  • 조문증;오세영
    • 제어로봇시스템학회:학술대회논문집
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    • 1990.10a
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    • pp.1-6
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    • 1990
  • Neural network is a computational model of ft biological nervous system developed ID exploit its intelligence and parallelism. Applying neural networks so robots creates many advantages over conventional control methods such as learning, real-time control, and continuous performance improvement through training and adaptation. In this paper, dynamic control of a six-link robot will be presented using neural networks. The neural network model used in this paper is the backpropagation network. Simulated control of the PUMA 560 am shows that it can move a high speed as well as adapt to unforseen load changes and sensor noise. The results are compared with the conventional PD control scheme.

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