Bjørn Solvang

Vibro-tactile feedback for VR systems

Today, in production engineering we have already taken into usage complex virtual representations of the manufacturing environment were we can program, simulate, analyse and optimise key performances. Such systems are mainly working through keyboard/ mouse input while user feedback normally are direct visual through computer screen and text files. Such communication only relays on a limited spectre of our senses and the keyboard/mouse/screen systems cannot be said to be very human friendly, especially when the operator is expected to operate “out of office” in a unstructured, maybe dangerous or dirty, environment. In the near future we expect to see more advanced input/output devices like motion capturing and speech input systems while feedback will not be visual alone but include several other human senses. Combinatorial sensory information, interpreted by the human brain, becomes very handy when a stand alone sense is not enough to interpret the actual situation. Imitation of senses and feedback from virtual reality environments always meant a great problem. Realization of two (sight and hearing) out of the five human senses is indispensable. Simulation of these two senses is not complex issue; on the contrary the other three are quite challenging. This paper describes the development of a vibro-tactile glove which can provide sensory feedback from a virtual environment, either as a stand alone system but most important in combination with sight and audio feedback systems. Instead of implementing real force feedback, the focus is on tactile sensing, as an alternative way of achieving the same feedback. The glove contains six vibration motors on different locations on the hand. These locations include all five fingers, and the palm. Communication with the glove is wireless, enabling free movement for the user. The system is low cost and very small sized which allows for combining it with advanced input devices like a motion capturing suit.

Multilevel control of flexible manufacturing systems

Typical manufacturing equipment for small and medium scale production is arranged in a setup of a flexible manufacturing cell (FMC). Normally, advanced manufacturing machinery in a FMC are individually well capable and equipped for offline programming. However, in terms of coordinated control between these machines there is a lack of capability. Especially in smaller production facilities, where the FMC components often are a mixture of older (poorly documented) and newer machines, there exist no ready-to-use (standard) solution for communication between the members of the cell. This paper presents an architecture for inter-machine communication and control based on an existing middleware framework. Further, and in detail, a vision system for rapid/simple/secure and low cost data retrieval from old computerized numerical control (CNC) machines is presented as a system key component. The architecture and vision system is general in its layout and can be utilized on a large majority of the components in flexible manufacturing systems.

STEP-NC based industrial robot CAM system

Abstract In this paper a computer aided manufacturing (CAM) system for industrial robot machining operations is introduced. The system is based on the new machining standard STEP-NC which is rapidly making its way into the world of numerical controlled (NC-) machines (e.g. milling and turning machines). Existing research, related to STEP-NC development, focus mainly on the typical implementation of the standard into these traditional production equipments. However, industrial robots are getting more and more capable of taking onto machining operations and it is necessary to couple the industrial robot towards the new standard.

Robot Vision for RT-Middleware Framework

In conventional robot system development the different robot parts (sensors, processing elements, actuators) are combined together in a compact, self contained system. The need for faster development, system reconfigurability and flexibility required the introduction of middleware frameworks for robot systems. One promising middleware framework is the RT-middleware technology (and its implementation named OpenRTM-aist), which is getting more and more popular in robot system design. Vision sensors are one of the most important sensors in robot systems. When constructing a new robot system, it is desirable that vision and image processing components are just as easily integrated as any other robot components. Unfortunately, RT-middleware is not yet equipped with such RT-components. This need can be satisfied by introducing a new image processing toolbox, directly obtained from DIMAN, a existing software previously developed by the authors. DIMAN is a distributed framework for image processing, with a modular architecture very similar to RT-middleware. The modules of DIMAN satisfy the needs of robot systems with respect to vision related tasks. Through experimental results usability of DIMAN in robot vision will be shown in this paper. Furthermore DIMAN module deployment in RT-middleware based robot systems will be introduced also.

Robot Programming in Machining Operations

Manual work is the key source of skill and ingenuity in industrial manufacturing. At the same time, it is the most expensive resource factor. Often machines can replace humans for more effective manufacturing because machines outperform humans in terms of strength, precision and endurance. Humans, however, perform better than machines when flexibility and intelligence is required. Flexible Manufacturing Systems are capable of manufacturing wide variety of products with low volume size and minimal lead time. These manufacturing cells are usually equipped with high level computer based control and include highly flexible industrial robots also. Robots can partially resolve contradiction of flexibility and intelligence due to their more humanlike structure and programmability. To a large extent, robots have already relieved workers of many of the tedious, hard and unhealthy parts of industrial work. But the universal application of robots in small-batch highly customized production is hindered by the time consuming robot programming. This thesis shows efficient methods and processes for teaching and optimizing complex robot tasks by introducing cognitive robot programming, flexible robotics, and middleware. Intelligent user interfaces combining information from several sensors in the manufacturing system will provide the operator with direct knowledge on the state of the manufacturing operation. Thus, the operator will be able to determine the system state quicker. Sensory system calculates and proposes the optimal process settings. The key element is the new cognitive human-machine communication channels helping the operator to comprehend the information from the sensory systems. Novel middleware technology is facilitated for integration of elements from different system platforms into a coherent robot system and scaling it for different hardware complexity levels. The contributions of the thesis could be summarized as follows: 1. Introduction of a new scientific concept of task dependent, software component based controller for flexible manufacturing cells. 2. Development of a new paradigm of robot teaching and supervising, which opens a new dimension of robotization in the area of small and medium sized enterprises. 3. Introduction of a new concept for Coginitve Telemanipualtion.

A Framework for Holistic Greening of Value Chains

Protecting and sustaining nature environment are two of the most important conditions regarding economic and social development. While the resources are extracted from and wastes are disposed to the same mother Earth, managing wastes of a value chain has been segmented and treated isolated. This segmentation leads to the partial optimization in waste minimization. This paper proposes a framework that facilitating holistic greening of a value chain so that total waste minimization can be achieved from entire chain’s perspective.

SAPIR: Supervised and Adaptive Programming of Industrial Robots

Cast parts have inconsistent geometry and grinding and deburring operations is to be carried out based on individual observation of every single workpiece. Normally, these operations are carried out manually by humans. However, due to the health risk associated with the grinding process, there is a strong incentive to explore new automated solutions. The industrial robot is viewed as a strong component for this job. Programming industrial robots is traditionally done by teach or offline programming methodologies. Both methods encounter problems in grinding/deburring operations. In traditional offline programming the robot path is generated from a CAD model of the workpiece. This CAD model holds no information on the irregularities (burrs) and then the necessary path cannot be created. This paper presents a new approach for supervised robot programming, which allows new field of application of industrial robots. In the near future, automatization of manufacturing processes with industrial robots in small and medium sized enterprise would be common. Instead of a costly, but fully automated solution, which works only from CAD model of workpiece and does not provide 100% satisfying result, an operator is involved in robot programming. The result is a 90% automated solution with the expertise of the worker. This interactive vision-based robot programming adds the required information into the offline programming environment. Thus, location and shape of any irregularities can be identified and the necessary deburring path created.

Tensor Product Transformation Based Friction Model

Friction is a very old and universal issue in all mechanical systems. Since friction is non-linear, it is an ever challenging problem. Several empirical nonlinear friction models have been proposed in the technical literature. This paper does not propose any new model but it presents a new, tensor product (TP) based representation of the existing friction models which is suitable for control design. The TP model transformation is a relatively new method for transforming certain nonlinear models into polytopic model form. The main advantage of the TP model transformation is that the most of the linear state feedback design methods including Linear Matrix Inequality (LMI) can immediately applied to the resulting polytopic models to yield controllers with guaranteed performance.

3D-Based Internet Communication and Control

Nowadays the main center of Research and Development are universities and academic institutions. The development of the Internet and network technologies made possible for research institutes to improve their cooperation and communication. The aim of this paper is to present the foundations of a uniform system for international collaboration, based on 3D Internet. The aim of the network is to create a cooperative and testable connection between mechatronical and industrial robot systems even in continental distances. In this paper we introduce a virtual laboratory which provides a suitable environment for distant institutions’ laboratories for a close collaboration.

A cognitive robot supervision system

This paper applies new cognitive infocommunication channels in human-machine interaction to develop a new paradigm of robot teaching and supervision. The robot is considered as an unskilled worker who is strong and capable for precise manufacturing. It has a special kind of intelligence but it is handicapped in a sense, which requires it to be supervised. If people can learn how to communicate to this “new worker” they can get a new, capable “colleague”. The goal is that the boss is able to give the daily task to a robot in a similar way as he/she gives the jobs to the human workers, for example using CAD documentations, gestures and some verbal explanation. This paper presents an industrial robot supervision system inspired by research results of cognitive infocommunication. The operator can steer the remote manipulator by certain gestures using a motion capture suit as input device. Every gesture has its own meaning, which corresponds to a specific movement of the robot. The manipulator interprets and executes the instructions invoking its on-board artificial intelligence, while feedback through a 3D visualization unit closes the supervisory loop. The system was designed to be independent of the geographical distance between the user and the manipulated environment, allowing to establish control loops spanning through countries and continents. Successful results have been achieved between Norway, France and Hungary.