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As cobots become more popular in manufacturing, the close cooperation between the robot and the human operator becomes more complex. This paper proposes a method for optimizing the robot's work pace by interactive bacterial evolutionary algorithm. One of the main contributions is the interactivity of the optimization via the operator's feedback. Beside the operator's subjective evaluation, heart rate signals are also measured. The paper focuses mainly on the concept of this method tested with the help of some volunteers. Preliminary tests show that the algorithm is able to find comfortable configurations with less then 1% of them evaluated.

This paper proposes a novel supervised learning algorithm for spiking neural networks. The algorithm combines Hebbian learning and least mean squares method and it works well for small training datasets and short training cycles. The proposed method is applied in human-robot interaction for recognizing musical hand gestures based on the work of Zoltan Kodaly. The MNIST dataset is also used as a benchmark test to´ verify the proposed algorithm’s capability to outperform shallow ANN architectures. Experiments with the robot also provided promising results by recognizing the human hand signs correctly.

The number of gears in a transmission system has increased significantly in the last decades, in order to always keep the engine in the optimal operating point, whether the vehicle is accelerating, decelerating or traveling with constant velocity. Several transmission control algorithms have been developed for road vehicles equipped with Automated Manual Transmission (AMT) or Automatic Transmission (AT) in order to find out and serve the drivers will and choose gear according to the actual driving status. The aim of this work is to investigate different gear shifting strategies, and to compare them in various aspects in simulation environment and tests including a real vehicle. When selecting the appropriate gear in the transmission, a number of parameters can be taken into consideration, e.g. actual engine speed, actual engine torque, fuel consumption, acceleration pedal position and the speed of the position change (position derivative), etc. To consider all of these parameters four gear selection algorithms have been implemented and tested.