Present Research
AI technology development for furniture assembly in real and virtual environments from assembly instructions
- 작성자Artificial Intelligence Laboratory
- 조회수13
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Overview- Development of an artificial intelligence system to generate a work plan for assembling robot articles from assembly instructions- Establish work steps through understanding assembly instructions- Assembly stage verification through virtual assembly in simulator- Recognition of assembly situation in real environment, establishment of recovery strategy through failure recognition Key Researches- Virtual data generation through rendering - Object detection and xss-document.segmentation in assembly instructions- Object detection and tracking in real environment, pose estimation- Point cloud-based object detection and pose estimation Publications- Seunghyeok Back, Jongwon Kim, Raeyoung Kang, Seungjun Choi, Kyoobin Lee, "Segmenting Unseen Industrial Components in a Heavy Clutter using RGB-D Fusion and Synthetic Data", arXiv prepirnt arXiv:https//arxiv.org/abs/2002.03501 (Accepted at IEEE ICIP 2020) Project Detail- Donation: MOTIE, KEIT- Term: 2019.04.01. ~ 2021.12.31. (33months)- Expences: 3,680,000,000 WON- Consortium: SNU DYROS Contact- name: Seunghyeok Back, Integrated Ph.d corse- E-mail: shback@gist.ac.kr
- 등록일2020-07-08 16:19:40
Shared autonomy based on deep reinforcement learning for responding intelligently to unfixed environments such as robotic assembly tasks
- 작성자Artificial Intelligence Laboratory
- 조회수18
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OverviewGrasping algorithm for unfixed environmentsWorker intention recognition algorithm for shared autonomy environment.Key Researches- Grasp Point Detection- Robot Grasping- Human Intention RecognitionProject ExplainationThis project is about development of assembly robot which is robust to unfixed work environment. With Korea university, we develop grasp algorithm and human intention recognition algorithm for assembly tasks in unfixed environment.We, GIST artificial intelligence laboratory, develop object grasp algorithm based on deep learning. Also, we research about Sim-to-Real technology for grasp algorithm, and we research about deep learning based intention recognition algorithm to make robot recognize the workers command in many situation.Project DetailDonation: Ministry of Trade, Industry and Energy, Korea Evaluation Institute of Industrial Technologyterm: 2020 ~ 2021 (1단계)Expenses: 1,030,920,000 WONConsortium: Korea University, GISTContact- 이름 : SeongHo Bak, Ph.D Course- 메일 : bakseongho@gm.gist.ac.kr
- 등록일2020-07-07 10:18:56
Develop Algorithm of Swarm USV Autonomous Navigate
- 작성자Artificial Intelligence Laboratory
- 조회수29
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OverviewDevelopment of multi-modal embedding and deduction situational awareness algorithm.Development of inference intention algorithm.Development of improving Video quality and object detection and object tracking algorithm.Key Researches- Data Preprocessing and Multi Modal Embedding- Awareness Situation and Inference Intention- Maritime Object Detection based Optical ImageProject ExplainationDevelop the multi-modal embedding and deduction situational awareness technology through design the system of integrate various types of database which can be obtained at sea. Various types of information such as camera, radar, andlidardata are acquired at sea. It is necessary to create a database for inference of intention by reorganizing these information into situational awareness information.Develop the algorithm that inference the overall intention of the swarm situation and the range of short-term maneuver and position of opponent ship using multi-modal embedded data. Inferencing COA(Course Of Action) which is the enemys intention in the maritime battlefield situation is important to win with minimal damage. Inferencing the intentions of the swarm of ships, it would be possible to give appropriate orders to allies in a maritime battlefield.Develop the algorithm that improve the quality of marine optical video information and detecting ships with tracking the same object. The optical video information acquired at sea can obtain low-quality information because of ship movement, water droplets, fog, and rain. To detect object such as ships, there is a need to improve the quality of the optical video information. In addition, object tracking technology will be studied because it is necessary to know whether the detected object is the same object as the previously detected object.Project DetailDonation: ADD(Agency for Defense Development)Term :2020. 03. 01 ~ 2021. 11. 10.Expenses :527,000,000\Consortium : KRISO, Hanwhasystems, Seadronix Inc., Chosun Univ., Kongju Univ, GISTContact- name : Seongju Lee, Ph.D Course- E-mail : lsj2121@gm.gist.ac.kr
- 등록일2020-06-25 09:24:11
Development of Intelligent Meal Assistant Robot with Easy Installation for the elderly and disabled
- 작성자Artificial Intelligence Laboratory
- 조회수22
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OverviewDevelopment of a Caring Robot with AI-based Dietary Assistance and Meal History Management Functions for the Elderly and the DisabledMulti-joint Single-arm Robot Arm based Meal Assistance RobotFood Recognition and AI-based Meal History Management Suitable for Korean-style MealsKey Researches- Building Korean Food Tray Image Dataset-Development of Food Segmentation Algorithm-Development of Meal History Management ServiceProject ExplainationThis task is to develop meal-assisted robot that provides Korean food tray and meal.It provides dietary assistance through multi-joint single-arm robotic arm and provides various types of UI (eyes, buttons, joystick, voice recognition) and AI-based meal history management.GIST artificial intelligence laboratory develops deep learning-based Korean food recognition algorithm to recognize food on the food tray and develop meal history management service.While traditional food recognition models often extract food tray features, this study develops deep learning models that utilize food tray-independent food textures.We have developed a food tray photo data collection system for Korean food recognition and are building datasets.We develop a deep learning-based food segmentation model for food recognition to recognize the type and location of food, and use the deep learning model to recognize the relative position on the food tray.We also develop a meal history management system based on food recognition results and develop applications suitable for mobile devices.Project Detail- Donation: Ministry of Trade, Industry and Energy, Korea Evaluation Institute of Industrial Technology(KETI)- Term: 2019. 04. 01 ~ 2021. 12. 31 (33 months)-Expenses: 3,680,000,000-Consortium:Cymechs, Korea Electronics Technology Institute(KETI), Korea Institute of Industrial Technology(KITECH), University Industry Liaison office of CNU, Soul National University Bundang HospitalContact- Name: JooSoon Lee, Integrated Ph.D. Course-E-mail:joosoon1111@gist.ac.kr
- 등록일2020-06-15 14:24:03
