Robot calibration and performance will degrade if proper maintenance isn't performed. There have been challenges for manufacturers to optimize the maintenance strategy and minimize unexpected shutdowns. Prognostics and health management (PHM) can be applied to industrial robots through the development of performance metrics, test methods, reference datasets, and supporting tools. A subset of this research involves developing a quick health assessment methodology emphasizing the identification of the positional health (position and orientation accuracy) changes. This methodology enables manufacturers to quickly assess the static/dynamic position and orientation accuracies of their robot systems. In this paper, the National Institute of Standards and Technology's (NIST) effort to develop the measurement science to support this development is presented, including the modeling and algorithm development for the test method, the advanced sensor development to measure 7-D information (time, X, Y, Z, roll, pitch, and yaw), algorithms to analyze the data, and a use case to present the results.

“After many false starts,” declared the Economist magazine in a June 2016 Special Report,1“artificial intelligence (AI) has taken off.” This special report examined “how AI, associated with hubris and disappointment since its earliest days, has suddenly become the hottest field in technology.” The report also heralded the return of themachinery question, first posed nearly 200 years ago in the context of industrial revolution, on the impact of such disruptive technologies on the different classes of society.

Unambiguous definition of spatial position and orientation has crucial importance for robotics. In this paper we propose an ontology about positioning. It is part of a more extensive core ontology being developed by the IEEE RAS Working Group on ontologies for robotics and automation. The core ontology should provide a common ground for further ontology development in the field. We give a brief overview of concepts in the core ontology and then describe an integrated approach for representing quantitative and qualitative position information.

Ubiquitous robotics, ambient Intelligence and cloud robotics are new research topics that start to gain popularity among the robotics community. The overlap that exists now between ubirobots and AmI makes their integration together within cloud computing valuable to create a hybrid physical-digital space rich with myriad of proactive intelligent services that enhance the quality and the way of our living and working. Indeed, there is no visible cooperation or synergies between ubiquitous computing and robots communities. This paper presents a workshop that is intended to facilitate discussions and build a bridge between these communities. Moreover we hope that satellite topics such as affective computing, semantic reasoning, and humancomputer interaction will enhance such discussions and foster the research in this area. We believe that both communities can learn and benefit from their mutual experiences.

Service robotics is becoming a leading application area of human-centered technologies, and the rise of household and personal assistance robots forecasts an utopist world of human--robot collaborative society. Along the road, one of the robotics community's major tasks is to work on the harmonization of trends, standardization of terms, interfaces and technologies. It is important to keep the scientific and social progress under control through sufficient public outreach and technology dissemination. Along those lines, the IEEE Robotics & Automation Society is sponsoring a working group entitled Ontologies for Robotics and Automation that will bridge cutting edge technology to users of the services---the general public. In this paper, the background of the project is presented, the definitions and examples of descriptive systems of relations are described, showing how ontologies can help to address the above challenges.

In this article, we present the ongoing efforts within the newly formed IEEE-RAS Working Group named Ontologies for Robotics and Automation. We focus in particular on one of the four subgroups that compose this working group, called the Upper Ontology/Methodology (UpOM) subgroup. As the name indicates, the aim of this subgroup is to develop an upper ontology and a methodology for ontology building and evaluation. This methodology will be used to coordinate the distributed development of domain specific ontologies by the other three subgroups, and to generate a global ontology that we hope will contribute to the standardization process of robotics and automation. This paper presents the composition, the envisioned methodology, and the future work of the UpOM subgroup. It also discusses some general aspects related to ontologies for the robotics and automation field, and to the efforts in standardizing them.

In this paper, we describe a novel approach for representing state information for the purpose of intention recognition in cooperative human-robot environments. States are represented by a combination of spatial relationships in a Cartesian frame along with cardinal direction information. This approach is applied to a manufacturing kitting operation, where humans and robots are working together to develop kits. Based upon a set of predefined high-level states relationships that must be true for future actions to occur, a robot can use the detailed state information presented in this paper to infer the probability of subsequent actions occurring. This would enable the robot to better help the human with the operation or, at a minimum, better stay out of his or her way.

The IEEE RAS Ontologies for Robotics and Automation Working Group is dedicated to developing a methodology for knowledge representation and reasoning in robotics and automation. As part of this working group, the Industrial Robots sub-group is tasked with studying industrial applications of the ontology. One of the first areas of interest for this subgroup is the area of kit building or kitting. This is a process that brings parts that will be used in assembly operations together in a kit and then moves the kit to the assembly area where the parts are used in the final assembly. This paper examines the knowledge representations that have been developed and implemented for the kitting problem.

This article discusses a newly formed IEEE-RAS working group entitled Ontologies for Robotics and Automation (ORA). The goal of this working group is to develop a standard ontology and associated methodology for knowledge representation and reasoning in robotics and automation, together with the representation of concepts in an initial set of application domains. The standard provides a unified way of representing knowledge and provides a common set of terms and definitions, allowing for unambiguous knowledge transfer among any group of humans, robots, and other artificial systems. In addition to describing the goal and structure of the group, this article gives some examples of how the ontology, once developed, can be used by applications such as industrial kitting.

In this paper, we describe our recent efforts on symbolic representations enabling autonomous driving in dynamic environments such as on-road driving. In contrast to traditional bottom-up approaches where temporal accumulation of locally insignificant inaccuracies causes eventual failures in high-level scene interpretation, our proposed knowledge-driven top-down approach combined with vehicle's intentions can provide valuable information to guide low-level bottom-up tasks and vice-versa. We contend that such rich symbolic representations can reduce the burden on sensory processing thereby dynamically directing it to look for particular features in expected locations and subsequently facilitating the vehicle to better react to potentially dangerous situations, such as the appearance of pedestrians in the road. We demonstrate the proposed approaches in various scenarios pertaining to vehicle perception and control using field data obtained from a military unmanned ground vehicle (UGV) traversing urban environments

We have developed PRIDE (prediction in dynamic environments), a hierarchical multi-resolutional framework for moving object prediction that incorporates multiple prediction algorithms into a single, unifying framework. PRIDE incorporates a long-term (LT) prediction approach based on situation recognition and a short-term (ST) prediction approach based on vehicle models. These two approaches provide for the prediction of the future location of moving objects at various levels of resolution at the frequency and level of abstraction necessary for planners at different levels within the hierarchy using sensory data. In this paper, we demonstrate the ability to use the results of the short-term prediction algorithms to strengthen/weaken the estimates of the long-term prediction algorithms via experimental results. We provide experimental results in an autonomous on-road driving scenario using AutoSim, a high-fidelity simulation tool that models details about road networks, including individual lanes, lane markings, intersections, legal intersection traversability, etc

In this paper, we present PRIDE (prediction in dynamic environments), a hierarchical multi-resolutional framework for moving object prediction that incorporates multiple prediction algorithms into a single, unifying framework. PRIDE is based upon the 4D/RCS (real-time control system) architecture and provides information to planners at the level of granularity that is appropriate for their planning horizon. The primary contribution of this paper is the approach that is used to provide a more synergistic framework, in which the results from one prediction algorithm are used to strengthen/weaken the results of another prediction algorithm. In particular, we describe how the results of an estimation-theoretic short-term (ST) prediction algorithm are used to validate the results of a situation-based long-term (LT) prediction algorithm. We show the results of the long-term prediction both before and after the results of the short-term predictions are integrated, and demonstrate how the latter provide better predictions of vehicle future positions using the AutoSim simulation package

In this paper, we present a hierarchical multi-resolutional approach for moving object prediction via estimation-theoretic and situation-based probabilistic techniques. The results of the prediction are made available to a planner to allow it to make accurate plans in the presence of a dynamic environment. We have applied this approach to an on-road driving control hierarchy being developed as part of the DARPA Mobile Autonomous Robotic Systems (MARS) effort. Experimental results are shown in two simulation environments.

For an autonomous vehicle to drive in a dynamic environment, it must be able to detect moving objects, predict their future location, represent this information in its internal knowledge bases, and make appropriate plans based upon all of this information. The estimation of what the object is and where it is expected to be in the future must be continuously refined as time progresses. This paper discusses a multi-representational approach to capturing important characteristics about moving objects in a dynamic environment. Specifically we look at applying a combination of symbolic, equation-based, and grid-based representations to fully represent the information that is needed for different types of planners to develop appropriate plans in the presence of moving objects.