This paper presents a robotized workstation for end-of-life treatment of electrical motors with an electrical effect of about 1 kW. These motors can. for example, be found in washing machines and in industry. There are two main steps in the work. The first step is an inspection whereby the functionality of the motor is checked and classification either for re-use or for disassembly is done. In the second step the motors classified for disassembly are disassembled in a robotized automatic station. In the initial step measurements art, performed during a start-up sequence of about 1 s. By measuring the rotation speed and the current and voltage of the three phases of the motor classification for either reuse or disassembly can be done. During the disassembly work, vision data are fused in order to classify the motors according to their type. The vision system also feeds the control system of the robot with various object co-ordinates, to facilitate correct operation of the robot. Finally, tests with a vision system and eddy-current equipment are performed to decide whether all copper has been removed from the stator.

Information from different sensors can be fused in various ways. It is often difficult to choose the most suitable method for solving a fusion problem. In a measurement situation, the measured signal is often corrupted by disturbances (noise, etc.). It is, therefore, meaningless to compare crisp values without the corresponding uncertainty intervals. This paper describes a toolbox including nine different fusing methods. All methods are applied on training data, and the most suitable method is then used for solving the real fusion problem. In the example, fusion is performed on data for classification in an industrial recycling operation. The data is from different vision systems and an eddy current system. The fusion methods included in the toolbox are fuzzy logic with triangular and Gaussian shaped membership functions, fuzzy measures with triangular and Gaussian shapes, Bayes' statistics, artificial neural networks, multivariate analysis (PCA), a knowledge-based system, and a neuro-fuzzy system.

Machines in industry, including industrial robots, have in many cases dramatically reduced the man-made work and improved the work environment. New machines introduce, however, new risk factors. Traditionally machines are safeguarded by means that more or less rigidly separates the machines from the personnel. This works well in many traditional areas, i.e., where industrial robots are involved. There is however a risk that the safety system limits the valuable flexibility of the robot, which can be considered as a quality that tends to become even more valuable in the progress of programming possibilities and sensor technology. This article shows an example how a safety system can be designed to achieve increased flexibility in co-operation between human and production safety strategy. The proposed safety system is totally based on sensor information that monitors the working area, calculate the safety level and improve the system dynamically, e.g., reduce the robot capability in conjunction to the system safety level. The safety system gain information from the sensors and calculates a risk level which controls the robot speed, i.e., the speed is reduced to achieve a sufficiently low risk level. The sensor data is combined with fuzzy-based sensor fusion and fuzzy rules. The safety system is based on sensor information, hence it automatically adjusts to changes in the guarded area as long as the functionality of the sensors is maintained. Finally, we present a system implementation in an industrial robot application.

This paper presents a new calibration method for industrial robots. The calibration method is based on a combination of inclinometer and LVDT measurements and is very simple to use. The inclinometers measure the deviation from the gravity direction of a measurement rod and the LVDT sensor measures the length of the rod. The inclinometers can be used without any pre-calibration and the whole equipment is inexpensive, portable and robust.