Navigation systems are developing rapidly; nevertheless, tasks are becoming more complex, significantly increasing the number of challenges for robotic systems. Navigation can be separated into global and local navigation. While global navigation works according to predefined data about the environment, local navigation uses sensory data to dynamically react and adjust the trajectory. Tasks are becoming more complex with the addition of dynamic obstacles, multiple robots, or, in some cases, inspection of places that are not physically reachable by humans. Cognitive tasks require not only detecting an object but also evaluating it without direct recognition. For this purpose, sensor fusion methods are employed. However, sensors of different physical nature sometimes cannot directly extract required information. As a result, AI methods are becoming increasingly popular for evaluating acquired information and for controlling and generating robot trajectories. In this work, a review of sensors for mobile robot localization is presented by comparing them and listing advantages and disadvantages of their combinations. Also, integration with path-planning methods is looked into. Moreover, sensor fusion methods are analyzed and evaluated. Furthermore, a concept for channel robot navigation, designed based on the research literature, is presented. Lastly, discussion and conclusions are drawn.

Autonomous systems are becoming increasingly relevant in our everyday life. The transportation field is no exception and the smart cities concept raises new tasks and challenges for the development of autonomous systems development which has been progressively researched in literature. One of the main challenges is communication between different traffic objects. For instance, a mobile robot system can work as a standalone autonomous system reacting to a static environment and avoiding obstacles to reach a target. Nevertheless, more intensive communication and decision making is needed when additional dynamic objects and other autonomous systems are present in the same working environment. Traffic is a complicated environment consisting of vehicles, pedestrians, and various infrastructure elements. To apply autonomous systems in this kind of environment it is important to integrate object localization and to guarantee functional and trustworthy communication between each element. To achieve this, various sensors, communication standards, and equipment are integrated via the application of sensor fusion and AI machine learning methods. In this work review of vehicular communication systems is presented. The main focus is the researched sensors, communication standards, devices, machine learning methods, and vehicular-related data to find existing gaps for future vehicular communication system development. In the end, discussion and conclusions are presented.

The key request for a vehicle suspension system is vibration control and decreasing the actual inertia forces. This ensures ride comfort for the crew and influences the fatigue level of the driver and overall driving safety. Implementing semi-active damping control in the vehicle suspension allows for adjusting the damping process in the vehicle for minimum acceleration applied to the seats, driver, and passengers. In order to implement theoretical analysis, we used a mathematical full-car model in Simulink/MATLAB. As the load, we added simulations of various artificially generated road profiles. The damping coefficient of the semi-active suspension system was optimized for maximum comfort level for a driver only. Results from the full-car simulation process deliver a graph of the output accelerations showing kinematic excitation from road deformities under various locations of vehicle load positions.

Navigation technologies are becoming more advanced, helping to solve complicated problems in various fields. Navigation can be classified as global, in which predefined data and reference points from the working environment are used to generate a path, and local, where the map is generated momentarily by acquiring data from outside using sensors. As navigation tasks become more demanding, working environments can become very complicated with an increasing number of dynamic obstacles or, in some cases, a lack of global references, which may have particularly notable impacts on communication. Inspection robots that are required to work in underground sewers are often required to work completely locally, relying on sensor data. For this reason, in this study, a cost-efficient laser marker-based obstacle detection and measurement system is designed and tested for future use in autonomous sewer robot local path generation. Our experiments show the convenience of applying four linear laser markers with an RGB camera to fully inspect upcoming obstacles in the region covering the front robot dimensions. The results show distance measurement accuracy of up to ±1.33 mm and obstacle width accuracy of up to ±0.28 mm measured from a 130 mm range. Nevertheless, accuracy is strongly dependent on the segmentation of image resolution, lighting, and reflection of the inspected surfaces. Also, it depends on the configuration of laser markers and the RGB camera position.

Ever-growing field of structural health monitoring (SHM) applications requires automatization of the structure monitoring process. Automatization of SHM processes is mainly limited by the lack of suitable sensors, such situation is especially noticeable in industrial solid-fuel boilers applications. There is huge lack of cheap and reliable sensors for internal concrete stress measurements in high-temperature. This research proposes a resistive pressure sensor prototype for measuring internal concrete stresses in a high temperature range. During the experiment sensor was integrated into concrete sample which was heated up to 600°C. During experiments sensor resistance varied from $\mathbf{700\ k}\Omega$ to $\mathbf{24}\ \Omega$ , obtained results proves functionality of developed sensors and its sensitivity for internal stress variation.