Proceedings of the 1st Standardized Knowledge Representation and Ontologies for Robotics and Automation Workshop

Welcome to IEEE-ORA (Ontologies for Robotics and Automation) IROS workshop. This is the 1st edition of the workshop on! Standardized Knowledge Representation and Ontologies for Robotics and Automation. The IEEE-ORA 2014 workshop was held on the 18th September, 2014 in Chicago, Illinois, USA. In!the IEEE-ORA IROS workshop, 10 contributions were presented from 7 countries in North and South America, Asia and Europe. The presentations took place in the afternoon, from 1:30 PM to 5:00 PM. The first session was dedicated to “Standards for Knowledge Representation in Robotics”, where presentations were made from the IEEE working group standards for robotics and automation, and also from the ISO TC 184/SC2/WH7. The second session was dedicated to “Core and Application Ontologies”, where presentations were made for core robotics ontologies, and also for industrial and robot assisted surgery ontologies. Three posters were presented in emergent applications of ontologies in robotics. We would like to express our thanks to all participants. First of all to the authors, whose quality work is the essence of this workshop. Next, to all the members of the international program committee, who helped us with their expertise and valuable time. We would also like to deeply thank the IEEE-IROS 2014 organizers for hosting this workshop. Our deep gratitude goes to the IEEE Robotics and Automation Society, that sponsors! the IEEE-ORA group activities, and also to the scientific organizations that kindly agreed to sponsor all the workshop authors work

Using a BDI Agent to Represent a Human on the Factory Floor of the ARIAC 2023 Industrial Automation Competition

Postprin

Towards a robot task ontology standard

Ontologies serve robotics in many ways, particularly in de- scribing and driving autonomous functions. These functions are built around robot tasks. In this paper, we introduce the IEEE Robot Task Representation Study Group, including its work plan, initial development efforts, and proposed use cases. This effort aims to develop a standard that provides a comprehensive on- tology encompassing robot task structures and reasoning across robotic domains, addressing both the relationships between tasks and platforms and the relationships between tasks and users. Its goal is to develop a knowledge representation that addresses task structure, with decomposition into subclasses, categories, and/or relations. It includes attributes, both common across tasks and specific to particular tasks and task types

Defining positioning in a core ontology for robotics

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.3-7 November 201

A Robot Ontology for Urban Search and Rescue

The goal of this Robot Ontology effort is to develop and begin to populate a neutral knowledge representation (the data structures) capturing relevant information about robots and their capabilities to assist in the development, testing, and certification of effective technologies for sensing, mobility, navigation, planning, integration and operator interaction within search and rescue robot systems. This knowledge representation must be flexible enough to adapt as the robot requirements evolve. As such, we have chosen to use an ontological approach to representing these requirements. This paper describes the Robot Ontology, how it fits in to the overall Urban Search and Rescue effort, how we will be proceeding in the future

Déduction d’intentions au travers de la représentation d’états au sein des milieux coopératifs entre homme et robot

Humans and robots working safely and seamlessly together in a cooperative environment is one of the future goals of the robotics community. When humans and robots can work together in the same space, a whole class of tasks becomes amenable to automation, ranging from collaborative assembly to parts and material handling to delivery. Proposed standards exist for collaborative human-robot safety, but they focus on limiting the approach distances and contact forces between the human and the robot. These standards focus on reactive processes based only on current sensor readings. They do not consider future states or task-relevant information. A key enabler for human-robot safety in cooperative environments involves the field of intention recognition, in which the robot attempts to understand the intention of an agent (the human) by recognizing some or all of their actions to help predict the human’s future actions.We present an approach to inferring the intention of an agent in the environment via the recognition and representation of state information. This approach to intention recognition is different than many ontology-based intention recognition approaches in the literature as they primarily focus on activity (as opposed to state) recognition and then use a form of abduction to provide explanations for observations. We infer detailed state relationships using observations based on Region Connection Calculus 8 (RCC-8) and then infer the overall state relationships that are true at a given time. Once a sequence of state relationships has been determined, we use a Bayesian approach to associate those states with likely overall intentions to determine the next possible action (and associated state) that is likely to occur. We compare the output of the Intention Recognition Algorithm to those of an experiment involving human subjects attempting to recognize the same intentions in a manufacturing kitting domain. The results show that the Intention Recognition Algorithm, in almost every case, performed as good, if not better, than a human performing the same activity.Les humains et les robots travaillant en toute sécurité et en parfaite harmonie dans un environnement est l'un des objectifs futurs de la communauté robotique. Quand les humains et les robots peuvent travailler ensemble dans le même espace, toute une catégorie de tâches devient prête à l'automatisation, allant de la collaboration pour l'assemblage de pièces, à la manutention de pièces et de materiels ainsi qu'à leur livraison. Garantir la sûreté des humains nécessite que le robot puisse être capable de surveiller la zone de travail, déduire l'intention humaine, et être conscient suffisamment tôt des dangers potentiels afin de les éviter.Des normes existent sur la collaboration entre robots et humains, cependant elles se focalisent à limiter les distances d'approche et les forces de contact entre l'humain et le robot. Ces approches s'appuient sur des processus qui se basent uniquement sur la lecture des capteurs, et ne tiennent pas compte des états futurs ou des informations sur les tâches en question. Un outil clé pour la sécurité entre des robots et des humains travaillant dans un environnement inclut la reconnaissance de l'intention dans lequel le robot tente de comprendre l'intention d'un agent (l'humain) en reconnaissant tout ou partie des actions de l'agent pour l'aider à prévoir les actions futures de cet agent. La connaissance de ces actions futures permettra au robot de planifier sa contribution aux tâches que l'humain doit exécuter ou au minimum, à ne pas se mettre dans une position dangereuse.Dans cette thèse, nous présentons une approche qui est capable de déduire l'intention d'un agent grâce à la reconnaissance et à la représentation des informations de l'état. Cette approche est différente des nombreuses approches présentes dans la littérature qui se concentrent principalement sur la reconnaissance de l'activité (par opposition à la reconnaissance de l'état) et qui « devinent » des raisons pour expliquer les observations. Nous déduisons les relations détaillées de l'état à partir d'observations en utilisant Region Connection Calculus 8 (RCC-8) et ensuite nous déduisons les relations globales de l'état qui sont vraies à un moment donné. L'utilisation des informations sur l'état sert à apporter une contribution plus précise aux algorithmes de reconnaissance de l'intention et à générer des résultats qui sont equivalents, et dans certains cas, meilleurs qu'un être humain qui a accès aux mêmes informations