This article compares three different algorithms used to compute Euler angles from data obtained by the angular rate sensor (e.g., MEMS gyroscope)—the algorithms based on a rotational matrix, on transforming angular velocity to time derivations of the Euler angles and on unit quaternion expressing rotation. Algorithms are compared by their computational efficiency and accuracy of Euler angles estimation. If attitude of the object is computed only from data obtained by the gyroscope, the quaternion-based algorithm seems to be most suitable (having similar accuracy as the matrix-based algorithm, but taking approx. 30% less clock cycles on the 8-bit microcomputer). Integration of the Euler angles’ time derivations has a singularity, therefore is not accurate at full range of object’s attitude. Since the error in every real gyroscope system tends to increase with time due to its offset and thermal drift, we also propose some measures based on compensation by additional sensors (a magnetic compass and accelerometer). Vector data of mentioned secondary sensors has to be transformed into the inertial frame of reference. While transformation of the vector by the matrix is slightly faster than doing the same by quaternion, the compensated sensor system utilizing a matrix-based algorithm can be approximately 10% faster than the system utilizing quaternions (depending on implementation and hardware).

The review discusses the possibilities of different driving mechanisms and sensors of spherical robots, and a special kind of mobile robots is introduced and discussed. The sensors discussed can expand robots’ sensing capabilities which are typically very limited. Most spherical robots have holonomic characteristics and protect the inner environment using a shell. Today, there are a diversity of driving mechanisms. Therefore, this article provides a review of all of them and identifies their basic properties. Accordingly, many spherical robots have only inner sensors for moving, balancing, driving, etc. However, a few of them are also equipped with sensors that can measure environmental properties. Therefore, in this paper, we propose the possibility of using such sensors as cameras, LiDARs, thermocouples, and gas sensors, which can be used for special purposes underground, for example, in mines, underground tunnels, or road tunnels. After combining all components are combined, it is possible to design a special type of spherical robot designed for underground exploration, such as accidents in mines or road tunnels.

This paper discusses an innovative adaptive heterogeneous fusion algorithm based on the estimation of the mean square error of all variables used in real-time processing. The algorithm is designed for a fusion between derivative and absolute sensors and is explained by the fusion of the three-axial gyroscope, three-axial accelerometer, and three-axial magnetometer into attitude and heading estimation. Our algorithm has a similar error performance in the steady state but much faster dynamic response compared with the fixed-gain fusion algorithm. In comparison with the extended Kalman filter, the proposed algorithm converges faster and takes less computational time. On the other hand, Kalman filter has smaller mean square output error in a steady state but becomes unstable if the estimated state changes too rapidly. In addition, the noisy fusion deviation can be used in the process of calibration. This paper proposes and explains a real-time calibration method based on machine learning working in the online mode during run time. This allows compensation of sensor thermal drift right in the sensor's working environment without need of recalibration in the laboratory.

Passenger detection and occupancy estimation are vital tasks in many fields. The existing literature emphasises that the increasing demand for such systems will continue to grow. This paper reviews the existing literature specializing in the field of transportation safety and efficiency concerning occupancy estimation in vehicles and passenger detection at public transport stations. A comparison between different approaches to passenger estimation is presented. Discussion on the advantages and disadvantages is highlighted. Hence, this paper provides an analysis of 146 papers on the current state of the field. This review paper concludes that invasive methods provide high accuracy with relatively cheap implementation, while noninvasive systems do not violate passenger privacy but lack state-of-the-art accuracy. Future work will include a systematic literature review and a comparative analysis of systems considering the existing window tinting and solar windshields heavily blocking certain parts of the electromagnetic spectrum. Moreover, future work will investigate the critical challenges of noninvasive passenger estimation in different types of vehicles: trucks, buses, or even motorcycles.

This article is focused on the search for potential collision of a moving object (as a mobile robot is) with the environment. Application of the proposed and presented method requires existence of the three-dimensional model of the environmental space. In this case, the models are generated automatically with the help of mobile measurement platform developed by authors and are available to other applications. Models of moving objects are loaded by the proposed application from external files. The method is based on data fusion, using data from a laser scanner, global positioning system, and inertial navigation system; however, it can also be used for data obtained by other ways. Its correctness was experimentally proved by several experimental tests. Output is in the form of calculated coordinates of the point in which a moving object would potentially collide with the environment if no avoiding action is taken. Such an object may represent an oversized load transferred by a truck, train, ship, the transport means itself or even a person.