Björn Hein

Intuitive and model-based on-line programming of industrial robots: A modular on-line programming environment

This paper focuses on the simplification of the on-line programming process of industrial robots. It presents a modular on-line programming environment (software and hardware), which supports an intuitive way of moving and teaching robots, while supporting the user with assisting algorithms like collision avoidance and automatic path planning. Main idea is the combination of different approaches from tele-operation, programming by demonstration and off-line programming, and reuse them in a new fashion on-line on the shopfloor. The proposed programming environment is designed to evaluate the usability of different combination of assisting techniques.

Methods for safe human-robot-interaction using capacitive tactile proximity sensors

In this paper we base upon capacitive tactile proximity sensor modules developed in a previous work to demonstrate applications for safe human-robot-interaction. Arranged as a matrix, the modules can be used to model events in the near proximity of the robot surface, closing the near field perception gap in robotics. The central application investigated here is object tracking. Several results are shown: the tracking of two human hands as well as the handling of occlusions and the prediction of collision for object trajectories. These results are important for novel pretouch- and touch-based humanrobot interaction strategies and for assessing and implementing safety capabilities with these sensor systems.

Intuitive and model-based on-line programming of industrial robots: New input devices

This paper focuses on the simplification of the on-line programming process of industrial robots. It presents in detail the input part of a modular on-line programming environment presented as overview in [1]. Main concept of this programming environment is an intuitive way of moving and teaching robots, while supporting the user with assisting algorithms like collision avoidance and automatic path planning. Goal is the combination of different approaches from tele-operation, programming by demonstration, Virtual Reality and off-line programming, and to reuse them in a new fashion on-line on the shop-floor. This paper presents some ideas and concepts, how input devices and strategies for robot programming could look like and how to use them to set up an intuitive manual motion control of the robot.

Plug & produce by modelling skills and service-oriented orchestration of reconfigurable manufacturing systems

Abstract Shortening product lifecycles and small lot sizes require manufacturing systems to adapt increasingly fast. Many existing machine tools, handling and logistics systems provide a generic functionality that is not bound to a specific product. But this flexibility and reconfigurability on the level of individual resources is lost in automated systems that are limited to the production of a fixed set of product variants. We propose a unified abstraction for the skills provided by the available resources and the product-specific manufacturing requirements. From these high-level descriptions, executable manufacturing procedures are derived, exposed as services and dynamically orchestrated at runtime in order to achieve the manufacturing goals.

Modelling and orchestration of service-based manufacturing systems via skills

Shortening product lifecycles and small lot sizes require manufacturing systems to adapt increasingly fast. Many existing machine tools, handling and logistics systems are already generic and not bound to a specific product a-priori. Yet this flexibility and reconfigurability on the asset level is lost in automated systems that are limited to executing a small set of predefined actions in a fixed sequence. The SkillPro1 project aims to develop a holistic service-oriented framework for modelling and orchestration of modern adaptable manufacturing systems. The core concept is a unified abstraction for manufacturing tasks: skills provided by the available assets and the requirements of the different production steps. The skill-based system model enables the transition from generic high-level descriptions to low-level formats that can be directly executed. Self-describing assets can be added, changed and removed at runtime, taking into account technical and economic conditions to best achieve the manufacturing goals.

6D proximity servoing for preshaping and haptic exploration using capacitive tactile proximity sensors

In this paper we present applications for a robot system whose gripper is equipped with distributed capacitive tactile proximity sensors (CTPS). Firstly, we introduce and evaluate a closed loop control scheme by which it is possible to align the gripper to objects or features of the environment using proximity values alone. We call this control method proximity servoing. It is implemented by equilibrating the sensor signals, resulting in a robust preshape in all 6DOF of the object pose. Objects can then be grasped with virtually no displacement. Secondly, also based on proximity servoing, we demonstrate and evaluate novel ideas for combined haptic and proximity-based exploration. Without any cues from an external camera the system is capable of detecting and exploring features such as curvatures, edges or corners of objects. It is also shown that tactile and proximity exploration steps can be used complementarily to increase efficiency in exploration while delivering accurate object measurements.

Capacitive Tactile Proximity Sensing: From Signal Processing to Applications in Manipulation and Safe Human-Robot Interaction

Recently we have shown developments on capacitive tactile proximity sensors (CTPS) and their applications. In this work we give an overview of these developments and put them into a more general perspective, emphasizing what the common grounds are for the different applications, i.e., preshaping and grasping, haptic exploration as well as collision avoidance and safe human-robot interaction. We discuss issues related to signal processing and the design of a smart skin for the robot arm and its end-effector. On a higher level we discuss the concept of proximity servoing and its use for the above mentioned applications.

Automated generated collision-free time optimized robot movements in industrial environments based on rounding

This paper presents a new approach to optimized notion planning for industrial robot arms with six degrees of freedom in an online given 3D environment. The method is based on the A*-search algorithm and needs no essential off-line computations. The algorithm works in an implicitly discrete configuration space. Collisions are detected in the Cartesian workspace by hierarchical distance computation based on the given CAD model. The collision-free result path is then optimized in execution time using rounding, a common command of every standard robot controller in the industry.

Telemanipulation with force-based display of proximity fields

In this paper we show and evaluate the design of a novel telemanipulation system that maps proximity values, acquired inside of a gripper, to forces a user can feel through a haptic input device. The command console is complemented by input-devices that give the user an intuitive control over parameters relevant to the system. Furthermore, proximity sensors enable the autonomous alignment/centering of the gripper to objects in user-selected DoFs with the potential of aiding the user and lowering the workload. We evaluate our approach in a user study that shows that the telemanipulation system benefits from the supplementary proximity information and that the workload can indeed be reduced when the system operates with partial autonomy.

From AutomationML to ROS: A model-driven approach for software engineering of industrial robotics using ontological reasoning

One of the major investment for applying industrial robots in production resides in the software development, which is an interdisciplinary and heterogeneous engineering process. This paper presents a novel model-driven approach that uses AutomationML as modeling framework and ontological reasoning as inference framework for constructing robotic application using Robot Operating System (ROS). We show how different robotic components can be classified and modeled with AutomationML, how these components can be composed together to a production system, and how the AutomationML models can be processed semantically by utilizing Semantic Web technologies and ontological reasoning. By applying model-to-text transformation techniques, executable ROS code can be generated from the models that foster fast prototyping and the reuse of robotic software.