Högskolan i Skövde

The University of Skövde and Gothia Science Park have over the last 13 years developed a strong environment for education, research and business, aligned with the profile of the University. The University and Science Park is located in a Swedish region with the majority of industry working in manufacturing with limited R&D and a with lower percentage of the population with higher education compared to Sweden in average. Explicit strategies and the operation of these have led to the development of an integrated innovation environment enabling new forms of university industry interactions, research, innovation processes and business development. The cooperation between the University and the Science Park organization take place on the strategic level, on the tactical level and on the operational level. The success of the case is characterized by strong commitment, the courage to select, openness and trust, organic growth and enthusiasm. The development has resulted in the University multiplying its research funding for projects with industry, The Gothia Business Incubator becoming one of the leading Swedish incubators and the science park growing into the regions third biggest workplace in trade and industry and one of the 10 largest science parks in Sweden.

A Network-Centric approach enables systems to be interconnected in a dynamic and flexible architecture to support multi-lateral, civilian and military missions. Constantly changing environments require commanders to plan for more flexible missions that allow organizations from various nations and agencies to join or separate from the teams performing the missions, depending on the situation. The uncertainty inherent in an actual mission, and the variety of potential organizations that support the mission after it is underway, makes Command Intent (CI) a critical concept for the mission team. Both humans and computerized decision support services need to have the ability to communicate and interpret a shared CI. This paper presents the Operations Intent and Effects Model (OIEM) - a model that relates CI to Effects, and supports both traditional military planning and Effects Based Operation. In the provided example the suggested Command and Control Language is used to express Operations Intent and Effects. © 2009 IEEE.

The Network-Centric approach envisioned in the Global Information Grid enables the interconnection of systems in a dynamic and flexible architecture to support multi-lateral, civilian and military missions. Constantly changing environments require commanders to plan for missions that allow organizations from various nations and agencies to join or separate from the teams performing the missions, depending on the situation, as missions unfold. The uncertainty within an actual mission, and the variety of potential organizations that support the mission after it is underway, makes Command Intent (CI) a critical concept for the mission team. With new and innovative information technologies, CI can now be made available to the team of organizations in a coalition environment. Using a flexible and linguistically based approach for representing CI allows Intent to be interpreted and processed by all participants – both humans and machines. CI representations need to be able to express mission team’s purpose, the anticipated End-State of the mission and desired key tasks. In this work, the expression of CI is developed to enable the structure and dynamics of collaboration support.

Simulation of sensor systems for mobile robots are described in this paper. By simulation of smart sensor systems, the performance of semi-autonomous vehicles / mobile robots can be enhanced. Smart sensor systems used in the field of mobile robotics can utilise adaptive algorithms. e. g. artificial neural nets, fuzzy logic or hybrid variants of these systems. The development, training and evaluation of adaptive algorithms for sensor systems can be done within a virtual environment in which graphical models are built to simulate an intelligent vehicle, its sensors, and its environment. The virtual sensors are validated by comparing the characteristics of the virtual sensors with those of the real devices.

Virtual robotics can be used to dramatically improve the capabilities and performance of industrial robotic systems. Virtual robotics encapsulates graphical off-line programming systems and Computer Aided Robotics (CAR). However current virtual robotic tools suffer from a number of major limitations which severely restrict the ways in which they can be deployed and the performance advantages they offer to the industrial user. The research study focuses on simulation of sensors, programming of event based robotic systerns and demonstrates how intelligent robots can be trained adaptive behaviours in virtual environments. Contemporary graphical programming systems for robots can only be used to program limited sections of a robot program, since i) they do not support methods for the simulation of sensors and event detection; ii) they normally use a post-processor to translate programs from a general language to a controller specific language; iii) conternporary robots can not easily adapt to changes in their environments; and iv) robot programs created off-line must be calibrated to adjust to differences between the virtual and real robotic workcells.

The thesis introduces a generic sensor model which can be used to model a variety of sensor types. This model allows virtual sensors to work as independent devices. It is demonstrated that using simulated sensors, event-based robot programs can be created and debugged entirely off-line. Off-line programming of event-based robotic systems demands methods for realistic handling of the communication between independent devices and process. The system must also possess the ability to manage and store information describing status and events in the environment. A blackboard architecture has been used in this research study to store environmental conditions and manage inter-process communication.

Self-learning robots is a possible strategy to allow robots to adapt to environmental changes and to learn from their experience. If suitable learning regimes are developed robots can learn to detect changes between virtual and real environments thus minimising the need for calibration. Most learning is based on experience and this requires experimental data to be fed to the learning system. This thesis demonstrates that robot controllers using artificial neural networks for knowledge acquisition and storage can be 'pre-emptively learnt' in virtual robotic environments using virtual robots and simulated sensors. The controllers are able to generalise from the information acquired by the virtual sensors operating in the virtual environment. Arguably the biggest obstacle to the use of self learning robotic systems in real applications has been the need to train the 'real robots' extensively in the 'real environment'. 'Pre-emptive learning' removes this problem. Furthermore, it is therefore possible to develop and evaluate new learning regimes using virtual robotic systems. This approach provides an opportunity to create a variety of environments and conditions which would be impractical to create in a real environment (due to constraints of time, cost and availability). 

Sensor simulation in Computer Aided Robotics (CAR) can enhance the capabilities of such systems to enable off-line generation of programmes for sensor driven robots. However, such sensor simulation is not commonly supported in current computer aided robotic environments. A generic sensor object model for the simulation of sensors in graphical environments is described in this paper. Such a model can be used to simulate a variety of sensors, for example photoelectric, proximity and ultrasonic sensors. Tests results presented here show that this generic sensor model can be customised to emulate the characteristics of the real sensors. The preliminary findings from the first off-line trained mobile robot are presented. The results indicate that sensor simulation within CARs can be used to train robots to adapt to changing environments.