Intelligent systems emulate the human ability to perceive, reason, make decisions, and act. Examples of intelligent systems of direct interest to academia and industry can be described as follows.
Sensors are integral components of many systems, and support (among others) robotics, process control, vision and other intelligent systems. To illustrate, proximity sensors are used in collision avoidance and to facilitate close-proximity operations of robot manipulators. Tactile sensors, on the other hand, facilitate the manipulative capability of robot arms and end-effectors. A variety of sensors (e.g., moisture, pressure, flow and temperature) is widely applied in the manufacturing sector, to both monitor and control processes. Machine-Vision Systems integrate electronic components with software systems to imitate a variety of human vision-based functions.
Process control and automation systems
Basic technologies here are: knowledge-based systems (KBS) that allow human expertise to be captured and used to aid the decision-making process; pattern recognition technologies such as statistical pattern recognition, neural networks, case-based reasoning, fuzzy logic, genetic algorithms, and data mining; distributed databases and agents, reasoning systems and intelligent agents. The industries benefiting from these systems are as diverse as their applications and cover agriculture, manufacturing, transportation, communications, financial analysis, electric power generation, and health, among others.
Robotic systems perform physical manipulations loosely based on human abilities. The technologies involved in developing these systems depend on machine vision, sensors, and KBS. As technologies evolve, robots have become more useful in environments that are unpredictable and dangerous for humans (e.g. high temperature, radiation areas, munitions sites, environmental cleanup) and, ironically, in ultra-clean environments (e.g. microchip and pharmaceutical plants) where humans represent the undesirable element. Emerging robotic systems also include mobile robots, and their component sub-systems, and service robots.
The RAP framework
To meet the competitive challenges posed by the ever increasing use of intelligent systems in North America, Asia, and most of the other EU member countries, Sweden needs: 1) a larger volume of highly educated people who have both broad and deep knowledge in intelligent systems science and technology; 2) who are able to see the significance of these systems for Swedish industry; and 3) that can take lead roles in industrial research and development activities. RAP is only a first step in satisfying these needs by targeting three application areas of strong industrial relevance:
Intelligent service robots
The focus here is on autonomous vehicles for use in underground mining and security monitoring/surveillance. Examples of specific industrial output are an autonomous navigational system for next generation of unmanned mining trucks by Atlas Copco and surveillance robots by Rotundus AB.
The focus here is on SME-friendly industrial manipulators that can be easily deployed programmed and flexibly used in small-volume manufacturing. One specific industrial output is the next generation Friction Stir Welding robots by ESAB.
Intelligent information processing
The focus here is on data mining for large and heterogeneous data volumes and data/information intelligent processing and fusion.