The progress observed in ‘soft robotics’ brought some promising research in flexible tactile, pressure and force sensors, which can be based on polymeric composite materials. Therefore, in this paper, we intend to evaluate the characteristics of a force-sensitive material—polyethylene-carbon composite (Velostat®) by implementing this material into the design of the flexible tactile sensor. We have explored several possibilities to measure the electrical signal and assessed the mechanical and time-dependent properties of this tactile sensor. The response of the sensor was evaluated by performing tests in static, long-term load and cyclic modes. Experimental results of loading cycle measurements revealed the hysteresis and nonlinear properties of the sensor. The transverse resolution of the sensor was defined by measuring the response of the sensor at different distances from the loaded point. Obtained dependencies of the sensor’s sensitivity, hysteresis, response time, transversal resolution and deformation on applied compressive force promise a practical possibility to use the polyethylene-carbon composite as a sensitive material for sensors with a single electrode pair or its matrix. The results received from experimental research have defined the area of the possible implementation of the sensor based on a composite material—Velostat®.

This review is dedicated to the advanced applications of robotic technologies in the industrial field. Robotic solutions in areas with non-intensive applications are presented, and their implementations are analysed. We also provide an overview of survey publications and technical reports, classified by application criteria, and the development of the structure of existing solutions, and identify recent research gaps. The analysis results reveal the background to the existing obstacles and problems. These issues relate to the areas of psychology, human nature, special artificial intelligence (AI) implementation, and the robot-oriented object design paradigm. Analysis of robot applications shows that the existing emerging applications in robotics face technical and psychological obstacles. The results of this review revealed four directions of required advancement in robotics: development of intelligent companions; improved implementation of AI-based solutions; robot-oriented design of objects; and psychological solutions for robot–human collaboration.

Positioning accuracy in robotics is a key issue for the manufacturing process. One of the possible ways to achieve high accuracy is the implementation of machine learning (ML), which allows robots to learn from their own practical experience and find the best way to perform the prescribed operation. Usually, accuracy improvement methods cover the generation of a positioning error map for the whole robot workspace, providing corresponding correction models. However, most practical cases require extremely high positioning accuracy only at a few essential points on the trajectory. This paper provides a methodology for the online deep Q-learning-based approach intended to increase positioning accuracy at key points by analyzing experimentally predetermined robot properties and their impact on overall accuracy. Using the KUKA-YouBot robot as a test system, we perform accuracy measurement experiments in the following three axes: (i) after a long operational break, (ii) using different loads, and (iii) at different speeds. To use this data for ML, the relationships between the robot’s operating time from switching on, load, and positioning accuracy are defined. In addition, the gripper vibrations are evaluated when the robot arm moves at various speeds in vertical and horizontal planes. It is found that the robot’s degrees of freedom (DOFs) clearances are significantly influenced by operational heat, which affects its static and dynamic accuracy. Implementation of the proposed ML-based compensation method resulted in a positioning error decrease at the trajectory key points by more than 30%.

Steel ropes are complex flexible structures used in many technical applications, such as elevators, cable cars, and funicular cabs. Due to the specific design and critical safety requirements, diagnostics of ropes remains an important issue. Broken wire number in the steel ropes is limited by safety standards when they are used in the human lifting and carrying installations. There are some practical issues on loose wires-firstly, it shows end of lifetime of the entire rope, independently of wear, lubrication or wrong winding on the drums or through pulleys; and, secondly, it can stick in the tight pulley-support gaps and cause deterioration of rope structure up to birdcage formations. Normal rope operation should not generate broken wires, so increasing of their number shows a need for rope installation maintenance. This paper presents a methodology of steel rope diagnostics and the results of analysis using multi-criteria analysis methods. The experimental part of the research was performed using an original test bench to detect broken wires on the rope surface by its vibrations. Diagnostics was performed in the range of frequencies from 60 to 560 Hz with a pitch of 50 Hz. The obtained amplitudes of the broken rope wire vibrations, different from the entire rope surface vibration parameters, was the significant outcome. Later analysis of the obtained experimental results revealed the most significant values of the diagnostic parameters. The evaluation of the power of the diagnostics was implemented by using multi-criteria decision-making (MCDM) methods. Various decision-making methods are necessary due to unknown efficiencies with respect to the physical phenomena of the evaluated processes. The significance of the methods was evaluated using objective methods from the structure of the presented data. Some of these methods were proposed by authors of this paper. Implementation of MCDM in diagnostic data analysis and definition of the diagnostic parameters significance offers meaningful results.

Increasing the imaging rate of atomic force microscopy (AFM) without impairing of the imaging quality is a challenging task, since the increase in the scanning speed leads to a number of artifacts related to the limited mechanical bandwidth of the AFM components. One of these artifacts is the loss of contact between the probe tip and the sample. We propose to apply an additional nonlinear force on the upper surface of a cantilever, which will help to keep the tip and surface in contact. In practice, this force can be produced by the precisely regulated airflow. Such an improvement affects the AFM system dynamics, which were evaluated using a mathematical model that is presented in this paper. The model defines the relationships between the additional nonlinear force, the pressure of the applied air stream, and the initial air gap between the upper surface of the cantilever and the end of the air duct. It was found that the nonlinear force created by the stream of compressed air (aerodynamic force) prevents the contact loss caused by the high scanning speed or the higher surface roughness, thus maintaining stable contact between the probe and the surface. This improvement allows us to effectively increase the scanning speed by at least 10 times using a soft (spring constant of 0.2 N/m) cantilever by applying the air pressure of 40 Pa. If a stiff cantilever (spring constant of 40 N/m) is used, the potential of vertical deviation improvement is twice is large. This method is suitable for use with different types of AFM sensors and it can be implemented practically without essential changes in AFM sensor design.