Paulo J. S. Gonçalves

An IEEE standard Ontology for Robotics and Automation

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.

Applied ontologies and standards for service robots

Service robotics is an emerging application area for human-centered technologies. The rise of household and personal assistance robots forecasts a human-robot collaborative society. One of the robotics communitys major task is to streamline development trends, work on the harmonization of taxonomies and ontologies, along with the standardization of terms, interfaces and technologies. It is important to keep the scientific progress and public understanding synchronous, through efficient outreach and education. These efforts support the collaboration among research groups, and lead to widely accepted standards, beneficial for both manufacturers and users. This article describes the necessity of developing robotics ontologies and standards focusing on the past and current research efforts. In addition, the paper proposes a roadmap for service robotics ontology development. The IEEE Robotics & Automation Society is sponsoring the working group Ontologies for Robotics and Automation. The efforts of the Working group are presented here, aiming to connect the cutting edge technology with the users of these services-the general public.

Uncalibrated Eye-to-Hand Visual Servoing Using Inverse Fuzzy Models

A new uncalibrated eye-to-hand visual servoing based on inverse fuzzy modeling is proposed in this paper. In classical visual servoing, the Jacobian plays a decisive role in the convergence of the controller, as its analytical model depends on the selected image features. This Jacobian must also be inverted online. Fuzzy modeling is applied to obtain an inverse model of the mapping between image feature variations and joint velocities. This approach is independent from the robots kinematic model or camera calibration and also avoids the necessity of inverting the Jacobian online. An inverse model is identified for the robot workspace, using measurement data of a robotic manipulator. This inverse model is directly used as a controller. The inverse fuzzy control scheme is applied to a robotic manipulator performing visual servoing for random positioning in the robot workspace. The obtained experimental results show the effectiveness of the proposed control scheme. The fuzzy controller can position the robotic manipulator at any point in the workspace with better accuracy than the classic visual servoing approach.

Requirements for building an ontology for autonomous robots

IEEE Ontologies for Robotics and Automation Working Group were divided into subgroups that were in charge of studying industrial robotics, service robotics and autonomous robotics. This paper aims to present the work in-progress developed by the autonomous robotics (AuR) subgroup. This group aims to extend the core ontology for robotics and automation to represent more specific concepts and axioms that are commonly used in autonomous robots.,For autonomous robots, various concepts for aerial robots, underwater robots and ground robots are described. Components of an autonomous system are defined, such as robotic platforms, actuators, sensors, control, state estimation, path planning, perception and decision-making.,AuR has identified the core concepts and domains needed to create an ontology for autonomous robots.,AuR targets to create a standard ontology to represent the knowledge and reasoning needed to create autonomous systems that comprise robots that can operate in the air, ground and underwater environments. The concepts in the developed ontology will endow a robot with autonomy, that is, endow robots with the ability to perform desired tasks in unstructured environments without continuous explicit human guidance.,Creating a standard for knowledge representation and reasoning in autonomous robotics will have a significant impact on all R&A domains, such as on the knowledge transmission among agents, including autonomous robots and humans. This tends to facilitate the communication among them and also provide reasoning capabilities involving the knowledge of all elements using the ontology. This will result in improved autonomy of autonomous systems. The autonomy will have considerable impact on how robots interact with humans. As a result, the use of robots will further benefit our society. Many tedious tasks that currently can only be performed by humans will be performed by robots, which will further improve the quality of life. To the best of the authors’knowledge, AuR is the first group that adopts a systematic approach to develop ontologies consisting of specific concepts and axioms that are commonly used in autonomous robots.

Camera configurations of a visual servoing setup, for a 2 dof planar robot

Abstract This paper presents a study of three different camera configurations, for a 2 dof planar robot, comparing their behavior based on the singularities of the jacobian used in the control law. The camera configurations used were: eye-to-hand; eye-in-hand with the camera looking respectively in front and down. Two image features, coordinates of a point, from the target were used. Plots representing jacobian singularities, within the joint limits, are presented for a known target position. Some conclusions are also drawn when the target position is unknown. Simulation results for 20 Visual Servoing are presented, to show the behavior of the servoing in the three camera configurations.

A Vision System for Robotic Ultrasound Guided Orthopaedic Surgery

Surgical navigation is a crucial concept in computer assisted surgery. The use of robots in the surgery room largely benefits from this concept’s recent developments, namely with the use of non-invasive and radiation free imaging systems, like ultrasound (US). This paper presents the algorithms and software tools developed for US based orthopaedic surgery navigation. In the second part of the paper are presented the algorithms and software tools developed for controlling a robotic manipulator, that can be used both in simulation and in a real scenario. The paper also presents the developed simulation scenario, for a surgical procedure in Hip Resurfacing, e.g., the first drill of the femur head. For navigation, the system, during surgery, acquires a 3D US bone surface from a sequence of US images. This bone surface will then be registered to the pre-operative bone model, for a precise knowledge of the bone position and orientation. This registration is performed in two steps. The first, a global registration before the surgical procedure, to exactly register the bone. The second is to locally register the femur, which is faster and more suitable for tracking the bone movements. The measured bone movement is used by the robot manipulator to update its drilling position and orientation.

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.

Intelligent real-time fabric defect detection

This paper presents real-time fabric defect detection based in intelligent techniques. Neural networks (NN), fuzzy modeling (FM) based on productspace fuzzy clustering and adaptive network based fuzzy inference system (ANFIS) were used to obtain a clearly classification for defect detection. Their implementation requires thresholding its output, and based in previous studies a confusion matrix based optimization is used to obtain the threshold. Experimental results for real fabric defect detection were obtained from the experimental apparatus presented in the paper, that showed the usefulness of the three intelligent techniques, although the NN has a faster performance. Online implementation of the algorithms showed they can be easily implemented with commonly available resources and may be adapted to industrial applications without great effort.

A Suite of Ontologies for Robotics and Automation [Industrial Activities]

Reports on the formation and activities undertaken by the Ontologies for Robotics and Automation (ORA) Working Group. ORA was established in 2011 by the IEEE Standard Associations Robotics Society. The goal of the group is to develop a standard to provide an overall 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 human, robots, and other artificial systems.

Uncalibrated visual servoing in 3d workspace

In this paper, inverse fuzzy models for uncalibrated visual servoing, in 3D Workspace, are developed and validated in a six degrees of freedom robotic manipulator. This approach does not require calibrated kinematic and camera models, as needed in classical visual servoing to obtain the Jacobian. Fuzzy modeling is used to identify the inverse Jacobian in the robot workspace. Robot control is achieved by means of using the inverse fuzzy models directly as the controller. Experimental results obtained in a PUMA robot performing eye-to-hand visual servoing demonstrate the validity of the approach.