arXiv subjects

Björn Hein

Publications and source records attributed to Björn Hein.

23 records · Page 2Linked to original sources

arXiv2019

Interpreting OWL Complex Classes in AutomationML based on Bidirectional Translation

The World Wide Web Consortium (W3C) has published several recommendations for building and storing ontologies, including the most recent OWL 2 Web Ontology Language (OWL). These initiatives have been followed by practical implementations that popularize OWL in various domains. For example, OWL has been used for conceptual modeling in industrial engineering, and its reasoning facilities are used to provide a wealth of services, e.g. model diagnosis, automated code generation, and semantic integration. More specifically, recent studies have shown that OWL is well suited for harmonizing information of engineering tools stored as AutomationML (AML) files. However, OWL and its tools can be cumbersome for direct use by engineers such that an ontology expert is often required in practice. Although much attention has been paid in the literature to overcome this issue by transforming OWL ontologies from/to AML models automatically, dealing with OWL complex classes remains an open research question. In this paper, we introduce the AML concept models for representing OWL complex classes in AutomationML, and present algorithms for the bidirectional translation between OWL complex classes and their corresponding AML concept models. We show that this approach provides an efficient and intuitive interface for nonexperts to visualize, modify, and create OWL complex classes.

cs.AI↗

arXiv2019

Referencing between a Head-Mounted Device and Robotic Manipulators

Having a precise and robust transformation between the robot coordinate system and the AR-device coordinate system is paramount during human-robot interaction (HRI) based on augmented reality using Head mounted displays (HMD), both for intuitive information display and for the tracking of human motions. Most current solutions in this area rely either on the tracking of visual markers, e.g. QR codes, or on manual referencing, both of which provide unsatisfying results. Meanwhile a plethora of object detection and referencing methods exist in the wider robotic and machine vision communities. The precision of the referencing is likewise almost never measured. Here we would like to address this issue by firstly presenting an overview of currently used referencing methods between robots and HMDs. This is followed by a brief overview of object detection and referencing methods used in the field of robotics. Based on these methods we suggest three classes of referencing algorithms we intend to pursue - semi-automatic, on-shot; automatic, one-shot; and automatic continuous. We describe the general workflows of these three classes as well as describing our proposed algorithms in each of these classes. Finally we present the first experimental results of a semi-automatic referencing algorithm, tested on an industrial KUKA KR-5 manipulator.

cs.RO↗

arXiv2019

Sensorless Hand Guidance using Microsoft Hololens

Hand guidance of robots has proven to be a useful tool both for programming trajectories and in kinesthetic teaching. However hand guidance is usually relegated to robots possessing joint-torque sensors (JTS). Here we propose to extend hand guidance to robots lacking those sensors through the use of an Augmented Reality (AR) device, namely Microsoft's Hololens. Augmented reality devices have been envisioned as a helpful addition to ease both robot programming and increase situational awareness of humans working in close proximity to robots. We reference the robot by using a registration algorithm to match a robot model to the spatial mesh. The in-built hand tracking capabilities are then used to calculate the position of the hands relative to the robot. By decomposing the hand movements into orthogonal rotations we achieve a completely sensorless hand guidance without any need to build a dynamic model of the robot itself. We did the first tests our approach on a commonly used industrial manipulator, the KUKA KR-5.

cs.RO↗

arXiv2018

Human Intention Estimation based on Hidden Markov Model Motion Validation for Safe Flexible Robotized Warehouses

With the substantial growth of logistics businesses the need for larger warehouses and their automation arises, thus using robots as assistants to human workers is becoming a priority. In order to operate efficiently and safely, robot assistants or the supervising system should recognize human intentions in real-time. Theory of mind (ToM) is an intuitive human conception of other humans' mental state, i.e., beliefs and desires, and how they cause behavior. In this paper we propose a ToM based human intention estimation algorithm for flexible robotized warehouses. We observe human's, i.e., worker's motion and validate it with respect to the goal locations using generalized Voronoi diagram based path planning. These observations are then processed by the proposed hidden Markov model framework which estimates worker intentions in an online manner, capable of handling changing environments. To test the proposed intention estimation we ran experiments in a real-world laboratory warehouse with a worker wearing Microsoft Hololens augmented reality glasses. Furthermore, in order to demonstrate the scalability of the approach to larger warehouses, we propose to use virtual reality digital warehouse twins in order to realistically simulate worker behavior. We conducted intention estimation experiments in the larger warehouse digital twin with up to 24 running robots. We demonstrate that the proposed framework estimates warehouse worker intentions precisely and in the end we discuss the experimental results.

cs.RO↗

arXiv2018

Implementation of Augmented Reality in Autonomous Warehouses: Challenges and Opportunities

Autonomous warehouses with mobile, rack-carrying robots are starting to become commonplace, with systems such as Amazon's Kiva and Swisslog's CarryPick already implemented in functional warehouses. Such warehouses however still require human intervention for object picking and maintenance. In the European project SafeLog we are developing a safety-vest, used for safety-critical ranging and stopping of mobile robots, an improved planner that can handle large fleets of heterogeneous agents as well as an AR interaction system to navigate and support human workers in such automated environments. Here we present the AR interaction modalities, namely navigation, pick-by-AR and general system interactions that were developed at the moment of writing, as well as the overall system concept and planned future work.

cs.RO↗