인프라엣지에서 동적 객체를 2차원 Bounding Box 형태로 검출하기 위한 인공지능 학습 데이터셋입니다.아래 링크에서 세부 정보를 확인하실 수 있으며, 협약서 작성 후 전체 데이터를 다운로드 받을 수 있습니다.https://nanum.etri.re.kr/share/jwlee0121/DataStitchingCameraObjectDetection?lang=ko_KR상기 데이터는 한국전자통신연구원, 카카오 모빌리티, 테슬라 시스템, 한국전자기술연구원, 한국과학기술원 등이 공동으로 협력하여 수행하는 자율주행혁신사업을 통해 구축한 데이터로 한국전자통신연구원에서 운영하는 ETRI AI 나눔 사이트를 통해 전체 데이터를 공개함

고해상도, 저해상도 이미지 데이터 및 로봇 핸드를 활용한 객체 데이터

,Zbiór danych zawiera wyniki uzyskane w ramach realizacji projektu badawczo-rozwojowego pt "Opracowanie urządzenia recyklingowego do rozpoznawania, segregowania i wstępnej utylizacji odpadów wielu rodzajów oraz ich wyceny i wypłaty odpowiedniego wynagrodzenia za recykling" nr POIR.01.01.01-00-0331/17, w ramach poddziałania 1.1.1: badania przemysłowe i prace rozwojowe realizowane przez przedsiębiorstwa; w ramach POIR 2014-2020.,

인프라엣지에서 동적 객체를 3차원 Bounding Box 형태로 검출하기 위한 라이다 센서 기반 인공지능 학습 데이터셋입니다.아래 링크에서 세부 정보를 확인하실 수 있으며 협약서 작성 후 전체 데이터를 다운로드 받을 수 있습니다.https://nanum.etri.re.kr/share/jwlee0121/DataStitchingLidarObjectDetection?lang=ko_KR상기 데이터는 한국전자통신연구원, 카카오 모빌리티, 테슬라 시스템, 한국전자기술연구원, 한국과학기술원 등이 공동으로 협력하여 수행하는 자율주행혁신사업을 통해 구축한 데이터로 한국전자통신연구원에서 운영하는 ETRI AI 나눔 사이트를 통해 전체 데이터를 공개함

자율주행 로봇의 실내외 통합 주행을 위한 주행 데이터 구축날씨 (맑음, 흐림, 비)와 아침/낮/저녁 등의 다양한 환경에서 실내 및 실외에서 로봇이 주행할 때 보이는 여러 객체들의 종류들과 객체 위치들의 데이터를 제공하여 실내 및/혹은 실외공간, 통합 공간 기반의 다양한 연구를 할 수 있는 데이터 제공

The dataset contains both the robot's high-level tool center position (TCP) health data and controller-level components' information (i.e., joint positions, velocities, currents, temperatures, currents). The datasets can be used by users (e.g., software developers, data scientists) who work on robot health management (including accuracy) but have limited or no access to robots that can capture real data. The datasets can support the: - Development of robot health monitoring algorithms and tools - Research of technologies and tools to support robot monitoring, diagnostics, prognostics, and health management (collectively called PHM) - Validation and verification of the industrial PHM implementation. For example, the verification of a robot's TCP accuracy after the work cell has been reconfigured, or whenever a manufacturer wants to determine if the robot arm has experienced a degradation. For data collection, a trajectory is programmed for the Universal Robot (UR5) approaching and stopping at randomly-selected locations in its workspace. The robot moves along this preprogrammed trajectory during different conditions of temperature, payload, and speed. The TCP (x,y,z) of the robot are measured by a 7-D measurement system developed at NIST. Differences are calculated between the measured positions from the 7-D measurement system and the nominal positions calculated by the nominal robot kinematic parameters. The results are recorded within the dataset. Controller level sensing data are also collected from each joint (direct output from the controller of the UR5), to understand the influences of position degradation from temperature, payload, and speed. Controller-level data can be used for the root cause analysis of the robot performance degradation, by providing joint positions, velocities, currents, accelerations, torques, and temperatures. For example, the cold-start temperatures of the six joints were approximately 25 degrees Celsius. After two hours of operation, the joint temperatures increased to approximately 35 degrees Celsius. Control variables are listed in the header file in the data set (UR5TestResult_header.xlsx). If you'd like to comment on this data and/or offer recommendations on future datasets, please email guixiu.qiao@nist.gov.

NIST is developing metrics and test methods to benchmark the performance of robotic systems when performing manufacturing tasks. The ability to perform simple insertions is critical for robotic systems in manufacturing. A simple peg-in-hole test was designed to measure a robotic system's capability for performing these simple insertions. The dataset captures the performance metrics of a robotic system outfitted with a robotic hand and a robotic gripper to study the effect of next-generation robotic hand technology versus conventional parallel gripper technologies.

NIST is developing metrics and test methods to benchmark the performance of robotic systems when performing manufacturing tasks. The ability to perform simple insertions is critical for robotic systems in manufacturing. A simple peg-in-hole test was designed to measure a robotic system's capability for performing these simple insertions. The dataset captures the performance metrics of a robotic system outfitted with a robotic hand and a robotic gripper to study the effect of next-generation robotic hand technology versus conventional parallel gripper technologies.