데이터셋 상세
미국
Process and robot data from a two robot workcell representative performing representative manufacturing operations.
This data set is captured from a robot workcell that is performing activities representative of several manufacturing operations. The workcell contains two, 6-degree-of-freedom robot manipulators where one robot is performing material handling operations (e.g., transport parts into and out of a specific work space) while the other robot is performing a simulated precision operation (e.g., the robot touching the center of a part with a tool tip that leaves a mark on the part). This precision operation is intended to represent a precise manufacturing operation (e.g., welding, machining). The goal of this data set is to provide robot level and process level measurements of the workcell operating in nominal parameters. There are no known equipment or process degradations in the workcell. The material handling robot will perform pick and place operations, including moving simulated parts from an input area to in-process work fixtures. Once parts are placed in/on the work fixtures, the second robot will interact with the part in a specified precise manner. In this specific instance, the second robot has a pen mounted to its tool flange and is drawing the NIST logo on a surface of the part. When the precision operation is completed, the material handling robot will then move the completed part to an output. This suite of data includes process data and performance data, including timestamps. Timestamps are recorded at predefined state changes and events on the PLC and robot controllers, respectively. Each robot controller and the PLC have their own internal clocks and, due to hardware limitations, the timestamps recorded on each device are relative to their own internal clocks. All timestamp data collected on the PLC is available for real-time calculations and is recorded. The timestamps collected on the robots are only available as recorded data for post-processing and analysis. The timestamps collected on the PLC correspond to 14 part state changes throughout the processing of a part. Timestamps are recorded when PLC-monitored triggers are activated by internal processing (PLC trigger origin) or after the PLC receives an input from a robot controller (robot trigger origin). Records generated from PLC-originated triggers include parts entering the work cell, assignment of robot tasks, and parts leaving the work cell. PLC-originating triggers are activated by either internal algorithms or sensors which are monitored directly in the PLC Inputs/Outputs (I/O). Records generated from a robot-originated trigger include when a robot begins operating on a part, when the task operation is complete, and when the robot has physically cleared the fixture area and is ready for a new task assignment. Robot-originating triggers are activated by PLC I/O. Process data collected in the workcell are the variable pieces of process information. This includes the input location (single option in the initial configuration presented in this paper), the output location (single option in the initial configuration presented in this paper), the work fixture location, the part number counted from startup, and the part type (task number for drawing robot). Additional information on the context of the workcell operations and the captured data can be found in the attached files, which includes a README.txt, along with several noted publications. Disclaimer: Certain commercial entities, equipment, or materials may be identified or referenced in this data, or its supporting materials, in order to illustrate a point or concept. Such identification or reference is not intended to imply recommendation or endorsement by NIST; nor does it imply that the entities, materials, equipment or data are necessarily the best available for the purpose. The user assumes any and all risk arising from use of this dataset.
연관 데이터
Measurement and Processed Data From A Graph Database Approach to Wireless IIoT Work-cell Performance Evaluation
공공데이터포털
The work-cell is an essential industrial environment for testing wireless communication techniques in factory automation processes. A graph database approach to storing and analyzing network performance data from a manufacturing factory work-cell is introduced. A robotic testbed performs a pick-and-place task using two collaborative grade robot arms, machine emulators, and wireless communication devices. A graph database is implemented to capture network data and operational event data among the actors within the testbed. Using a proposed schema, the database is then populated with events from the testbed and the resulting graph is constructed. Query commands are then presented to examine and analyze network performance and relationships within the actors of the network. The resulting data from the experiments conducted are included in this dataset.
㈜씨유박스 - 로봇 행동 데이터 (3D 물건 파지)
공공데이터포털
- 물류 산업 현장에서 다양한 사물을 로봇을 이용하여 파지할 수 있는 인공지능 모델 연구 및 개발을 위하여 핑거타입 그리퍼 및 흡착식 그리퍼로 파지할 수 있는 상품에 대한 로봇 파지 행동 데이터셋 구축
㈜모핑아이 - AI탑재 생체모방로봇을 활용한 상수도관 내외부 데이터
공공데이터포털
- 상수도관로의 이상을 손상 없이 탐지하기 위해, 소프트 스킨의 생체모방 주행 로봇을 내부 투입하고, 각종 센서 및 장비를 통한 영상/음향 정보를 수집 후, AI 기반 빅데이터 분석 통해 이상유무 판단 및 예측 수행할 데이터 구축함 <데이터의 한계> 외부 음향데이터가 기존에는 상수도관 내의 이상부분에서의 음향의 차이가 있을 것으로 예측하고 수집하였으나 이상징후의 종류에 따른 차이가 크지 않았음
Exoskeleton Performance Data
공공데이터포털
The National Institute of Standards and Technology, Intelligent Systems Division has collected data measuring human subjects, while performing common, simulated industrial manufacturing tasks with and without wearing an exoskeleton. Five tests were completed as part of a research study to develop measurement science towards standard test methods. For simulated industrial manufacturing tasks were performed using a novel, now standardized apparatus, called the Position and Load Test Apparatus for Exoskeletons (PoLoTAE). In addition, a set of novel optical tracking marker artifacts were worn by the subject for synchronous tracking of exoskeleton and human leg position and orientation. The standard test artifacts were intended to address the challenges of measurement uncertainty variation between different marker clusters and marker movement on soft tissue and marker occlusion when using traditional bio-mechanical marker models while wearing an exoskeleton. The PoLoTAE tests simulated generic industrial tasks (load positioning, load alignment, peg-in-hole, applied force). The knee bend tests were performed to synchronously track the exoskeleton and human lower limb position and orientation for analysis such as comparing the exoskeleton fit to the subject’s leg.Overall, the tests included 116 subjects of which 68 subjects (59% of total subjects) consented to publication of their raw test data described in this paper. While some subjects performed more than one test, at least 30 subjects performed each of the five tests totaling 158 tests performed. To date, aggregate data for the load positioning and knee bend tests have been analyzed and are referenced in this paper. Sensor data was collected from each subject, which included: repetition number, heart rate, videos, skeletal joint pose estimation, and survey data.
Measurement Data for a Wireless Force Seeking Apparatus
공공데이터포털
Cyber-physical systems are systems governed by the laws of physics that are tightly controlled by computer-based algorithms and network-based sensing and actuation. Wireless communication technology is envisioned to play a primary role in conducting the information flows within such systems. A practical industrial wireless use case involving a robot manipulator control system, an integrated wireless force-torque sensor, and a remote vision-based observer is constructed and the performance of the cyber-physical system is examined. The resulting data from the experiments conducted are included in the dataset.
중소기업기술정보진흥원 제조AI 솔루션 공급기업 현황
공공데이터포털
중소기업기술정보진흥원에서 중소중견기업의 인공지능(AI) 및 데이터 기반 제조혁신을 지원하기 위해 보유하고 있는 AI 기반 제조데이터 솔루션 공급기업 정보
Mobile Manipulator Performance Measurement Data
공공데이터포털
An advanced approach to flexible manufacturing is to move robotic manipulators (also referred to as industrial arms), using an AGV or mobile robot, between workstations. This integrated system is referred to as a mobile manipulator. Prior to industrial acceptance and standards development for mobile manipulators, users of these new systems will expect manufacturers to provide real performance data to guide their procurement and assure suitability for given application tasks. A test method that uses an artifact, called the Reconfigurable Mobile Manipulator Artifact (RMMA), is described in [Bostelman RV, Li-Baboud Y, Legowik S, Hong TH, Foufou S., "Mobile Manipulator Performance Measurement Data". 2017 Jun 27] and compared to an optical tracking system that was used as ground truth for the RMMA and mobile manipulator. Measurement data of an AGV, an onboard robot arm, and an optical tracking system were recorded and are described in the paper and are available for download using the link available in this record. The data needed to make these three measurements was collected during two tests; both tests have corresponding timestamps relative to global positioning system (GPS) time, where the computer clocks are synchronized using the Network Time Protocol. It is expected that the user of the information within the paper and the data files will have sufficient knowledge to implement mobile manipulation testing and evaluation.