MEMS gyroscopes can provide useful information for dead-reckoning navigation systems if suitable error compensation algorithm is applied. If there is information from other sources available, usually the Kalman filter is used for this task. This work focuses on improving the performance of the sensor if no other information is available and the integration error should be kept low during periods of still (no movement) operation. A filtering algorithm is proposed to follow bias change during sensor operation to reduce integration error and extend time between successive sensor calibrations. The advantage of the proposed solution is its low computational complexity which allows implementing it directly in the micro-controller of controlling the MEMS gyroscope. An intelligent sensor can be build this way, suitable for use in control systems for mobile platforms. Presented results of a simple experiment show the improvement of the angle estimation. During the 12 hours experiment with a common MEMS sensor and no thermal compensation, the maximum orientation angle error was below 8 degrees.

The latest market research (Fanuc Polska 2019) shows that the robotization of the Polish industry is accelerating. More and more companies are investing in robotic production lines, which enable greater efficiency of implemented processes and reduce labour costs. The article presents the possibilities of using virtual reality (VR) for behavioural analysis in open robotic systems with a shared workspace. The aim of the article is to develop a method of verification of programmed movements of an industrial robot in terms of safety and efficiency in systems with a shared workspace. The method of the robot program verification on the digital model of the working cell made in VR will be checked. The obtained research results indicate a great potential of this method in industrial applications as well as for educational purposes.

Indoor navigation and vehicle tracking require special measurement techniques. The reference points and routes used by classic AGV (Automated Guided Vehicle) systems are usually buried under floor surface or painted directly on the floor, thus limiting the set of possible transportation paths. However, the indoor environment of an industrial warehouse is dynamic, the number and location of objects inside are subject to frequent changes and these changes might not be reflected in the map of the area. In such conditions, navigation according to the on-board instruments (dead-reckoning) could provide valuable information about the position and orientation of the vehicle. This paper reports test results from a smart sensor using a 6-axis MEMS IMU unit and a self-calibrating procedure for indoor vehicle orientation tracking. The smart sensor, integrated with information from wheel encoders can produce 2D position coordinates suitable for navigation. Original data processing algorithm, applied in the sensor, was developed by the authors as a part of the research project on mobile robotics.

The paper presents an attempt to construct a direct brain-to-machine interface (BCI) that could be used to control movements of a robotic arm. A series of experiments was performed to collect data from subjects, train and validate an effective neural network and search for a generalized solution. The ERD/ERS imagery paradigm was chosen to extract valid data from the EEG signal. Since categorizing between two opposite states (like Left-Right) proved to be the most reliable, a control structure containing multiple neural networks was proposed. New research concerns the method of development and the use of neural networks classifiers. An automated procedure was used to select the best bipolar classifiers from the set of machine-generated neural networks. The chosen classifiers were used in a hierarchical structure responsible for signal interpretation. Adoption of this method was motivated mainly by the complexity of arm movement. The movement consisted of several phases, such as the initiation, continuation, change of direction, change of speed. It was observed that simple bipolar classifiers produced better output than classifiers designed to recognize complex decisions.

A method of attitude estimation with a low-cost, strap-on MEMS sensor was proposed in the article. The method relies on dynamic gyroscope bias change estimation and updates during detected stops of the robot. The algorithm has been tested in laboratory with iRobot Roomba robot and should be improved to be useful in an industrial environment. The measurement of attitude of a mobile platform is necessary for correct navigation, especially in autonomous vehicles, which use dead reckoning between position updates from external sources. Since the system is intended to operate indoors, in industrial halls and shops, it cannot avail of GPS and Earth magnetic field sensors because of anomalies, which are common inside the steel constructions. Therefore, the accuracy of the gyroscope-based attitude estimation is significant. The proposed method aims to address the common problem of gyroscope bias drift, by dynamic update of sensor bias and simultaneous use of all gyroscope axes, to improve the quality of the measurements. A popular 3-axial gyroscope and 3-axial accelerometer sensors were used during the test runs. Obtained results suggest that it is possible to improve short-term accuracy of inertial dead reckoning, to get a system that could be of practical use in industrial AGV systems or intelligent vehicles.

The article presents the analytic method for real-time detection of the stationary state of a vehicle based on information retrieved from 6 DoF IMU sensor. Reliable detection of stillness is essential for the application of resetting the inertial sensor’s output bias, called Zero Velocity Update method. It is obvious that the signal from the strapped on inertial sensor differs while the vehicle is stationary or moving. Effort was then made to find a computational method that would automatically discriminate between both states with possibly small impact on the vehicle embedded controller. An algorithmic step-by-step method for building, optimizing, and implementing a diagnostic system that detects the vehicle’s stationary state was developed. The proposed method adopts the “Mahalanobis Distance” quantity widely used in industrial quality assurance systems. The method transforms (fuses) information from multiple diagnostic variables (including linear accelerations and angular velocities) into one scalar variable, expressing the degree of deviation in the robot’s current state from the stationary state. Then, the method was implemented and tested in the dead reckoning navigation system of an autonomous wheeled mobile robot. The method correctly classified nearly 93% of all stationary states of the robot and obtained only less than 0.3% wrong states.