Robotics

This document covers the general description of benchmarking test piece geometries along with quantitative and qualitative measurements to be taken on the benchmarking test piece(s) to assess the performance of additive manufacturing (AM) systems.This performance assessment may serve the two following purposes:– AM system capability evaluation. – AM system calibration.The benchmarking test piece(s) is (are) primarily used to quantitatively assess the geometric performance of an AM system. This document describes a suite of test geometries, each designed to investigate one or more specific performance metrics and several example configurations of these geometries into test piece(s). It prescribes quantities and qualities of the test geometries to be measured but does not dictate specific measurement methods. Various user applications can require various grades of performance. This document discusses examples of feature configurations, as well as measurement uncertainty requirements, to demonstrate low and high grade examination and performance. This document does not discuss a specific procedure or machine settings for manufacturing a test piece, which are covered by ASTM F2971 and other relevant process specific specifications.

ISO 9409-1:2004 defines the main dimensions, designation and marking for a circular plate as mechanical interface. It is intended to ensure the exchangeability and to keep the orientation of hand-mounted end effectors.It does not define other requirements of the end effector coupling device.It does not contain any correlation of load-carrying ranges, as it is expected that the appropriate interface is selected depending on the application and the load-carrying capacity of the robot.

This document defines terms used in relation to robotics.

As part of Autonomic/Autonomous Networks (ANs) paradigm, the document presents a Framework for intelligent and automated O&M for Access & Home networks, whichincludes: 1) Automatically identify typical anomalies and faults in Access & Home network based on Machine Learning (ML), Artificial Intelligence (AI) algorithms and expert experience, realize rapid troubleshooting, and thus reduce invalid on-site and sporadic issues. 2) Perform predictive analysis based on ML and AI to achieve preventive O&M and self-healing in case of failure.

As part of the growing area of Autonomic/Autonomous Networks (ANs), the document covers the subject of Federated GANA Knowledge Planes (KPs) Platforms for E2E Multi-Domain Federated Autonomic Management and Control (AMC) of 5G Network Slices in NGMN® E2E 5G Architecture

Describes the work of the A3 Robotics Association, Landscape, and how A3 provides leadership, coordination, administration, communication, and education for the development of standards in the robotics industry. It also provides Information on Global Robotic Standards Committees and Standards Developmenet Activities in the Area of Industrial Robots. It is formerly the Robotic Industries Association (RIA).

Outlines Requirements for Autonomic Networking and Autonomic Management & Control in 5G Networks

Requirements and Architectural Framework for Autonomic Management and Control of IMT-2020 Networks, as part of the the paradigm of Autonomic/Autonomous Networking (ANs) for Networks that exhibit features such as self-* operations such as self-adaptation, self-optimization, self-monitoring, self-protection and self-defense objectives for the network and services

Provides information on Study to identify and close gaps in Standards for Autonomous Networks (ANs) to complement Standards on ANs developed elsewhere such as the ETSI GANA related Standards