In this paper, we address the most pressing challenges faced by the manufacturing sector, particularly the manufacturing of small and medium-sized enterprises (SMEs), where the transition towards high-mix low-volume production and the availability of cost-effective solutions are crucial. To overcome these challenges, this paper presents 14 innovative solutions that can be utilized to support the introduction of agile manufacturing processes in SMEs. These solutions encompass a wide range of key technologies, including reconfigurable fixtures, low-cost automation for printed circuit board (PCB) assembly, computer-vision-based control, wireless sensor networks (WSNs) simulations, predictive maintenance based on Internet of Things (IoT), virtualization for operator training, intuitive robot programming using virtual reality (VR), autonomous trajectory generation, programming by demonstration for force-based tasks, on-line task allocation in human–robot collaboration (HRC), projector-based graphical user interface (GUI) for HRC, human safety in collaborative work cells, and integration of automated ground vehicles for intralogistics. All of these solutions were designed with the purpose of increasing agility in the manufacturing sector. They are designed to enable flexible and modular manufacturing systems that are easy to integrate and use while remaining cost-effective for SMEs. As such, they have a high potential to be implemented in the manufacturing industry. They can be used as standalone modules or combined to solve a more complicated task, and contribute to enhancing the agility, efficiency, and competitiveness of manufacturing companies. With their application tested in industrially relevant environments, the proposed solutions strive to ensure practical implementation and real-world impact. While this paper presents these solutions and gives an overview of their methodologies and evaluations, it does not go into their details. It provides summaries of comprehensive and multifaceted solutions to tackle the evolving needs and demands of the manufacturing sector, empowering SMEs to thrive in a dynamic and competitive market landscape.

In this paper a novel fusion algorithm for a setup of redundant MEMS gyroscopes is proposed. Six cost-efficient, tri-axial, consumer-type sensors are combined with three singleaxis vibration-robust automotive-type devices. The proposed algorithm provides a virtual sensor output that has both low noise due to redundancy averaging, as well as good mitigation of external, high-frequency vibration disturbances. Thereby the advantages of both sensor types, consumer and automotive, are combined. If one sensor is compromised by vibration influences, the conflicting sensor signals are challenging to conventional fusion methods. To overcome this, a two-stage fusion algorithm, called topological conflict measure (TCM), is proposed. It is based on weighted sums, where the weights for each sensor diminish as the signal reliability decreases. The method can be utilized to (1) assess a measure of multi-sensor conflict, (2) to improve fusion of redundant devices by lessening weighting for sensors with high conflict, and (3) to combine the fused signals calculated from sensors with inherently different characteristics, like vibration robustness in this case. Furthermore, a selection of other possible methods is implemented and compared to TCM: simple Bayesian averaging (BA), Dempster-Shafer Evidence Theory (DSET), Kalman filtering (KF) and fuzzy inference system (FIS). All methods offer similar, improved noise levels while the TCM, KF and FIS methods achieve the best vibration rejection. Among those, TCM has the best computational efficiency and adaptability to other types of redundant sensor setups.

The main contribution of this paper is to present the state-space model of an omnidirectional drivetrain (Kiwi drive) for mobile robots. Holonomy or omnidirectional movement of mobile robots is important in applications where navigation through tight space is required. Besides the already available literature regarding either dynamic or kinematic models, this paper presents the complex analysis of a mechatronic system and shows a coherent model that takes into account both the kinematics and the dynamics of a coupled electromechanical system. The model was developed based on two holonomic mobile robots: the Festo Robotino® and the MOGI Ethon. A simulation software developed by the authors is also presented for numerical evaluation of the model. The simulation results qualitatively confirm the model by fulfilling the empirical requirements. Both the model and the simulation software are expandable in order to improve the accuracy of the model describing actual mobile robots. Compared to other models which are based only on the kinematics of the Kiwi drivetrain, in accordance to one's technical instinct, the presented model shows that there is a cross effect between the motors of the drivetrain, where the motors affect each other.

Engineers encounter the problem of friction in any mechanical system. Friction force is strongly nonlinear and varies considerably while the system is working. In the case of high-precision applications friction makes the situation even more complex, as the stick-slip effect occurs near the target position. This paper introduces a pneumatic servo-system for investigation of the behavior of friction near the target position. A new model is proposed which takes the hysteresis loop of the friction also into consideration emphasizing the importance of hysteresis. This paper presents the tensorproduct (TP) based modeling of friction which is suitable for control design. The main advantage of the TP model transformation is that due to its polytopic model form Linear Matrix Inequality (LMI) can be immediately applied to the resulting model to yield controllers with guaranteed performance. The main contribution of this paper is the application of TP model transformation making the identification of friction parameters unnecessary by utilizing directly the measured data itself.

Construction vehicle automation for high accuracy applications require information about the state of the machine, resulting in a fully sensitized system with precise kinematic parameters. Since the measurement of these parameters contains uncertainties, accurate measurement of them is an expensive task. Automatic calibration of link parameters makes the task of kinematic parameter determination easier. This study reports a method for forward kinematic chain estimation of an excavator by bacterial programming (BP) based on randomly placed inertial navigation systems (INS) per segments with microelectromechanical sensors (MEMS) within. MEMS INS with fusion techniques provide increasing accuracy with outstanding resilience against harsh environment in a rigid housing. With known robot kinematic the tool orientation estimation can be made more accurate also the path can be planned. The unknown model structure and parameters are established and identified by BP without any a priori or given information about the device according to Denavit-Hartenberg (DH) transformation conventions. Fundamentals of this approach are described in detail and shown on simulated measurement results.

The identification of a nonlinear system is quite challenging for engineers. This paper presents the automatic identification of a servo-pneumatic cylinder based on a framework implemented in MATLAB. The introduced application shortens the process length of identification and gives areference model, important for controlling.