Traditional sports and esports benefit from the development of Information and Communications Technologies (ICT), including gaming, 4D image/video processing, augmented reality (AR), virtual reality (VR), machine learning (ML), artificial intelligence (AI), big data, high-performance computing (HPC), and cloud computing. On the fuzzy border between the areas of physical and modified reality, both types of sports can coexist. The hardware layer of esports includes PC, consoles, smartphones, and peripherals used to interface with computers, including sensors and feedback devices. The IT layer of esports includes algorithms required in the development of games, online platforms, and virtual reality. The esports community includes amateur and professional players, spectators, esports organizers, sponsors, and other stakeholders. Esports and gaming research spans throughout law (intellectual rights, insurance, safety, and age restrictions), administration (teams, clubs, organizations, league regulations, and tournaments) biology (medicine, psychology, addiction, training and education) Olympic and non- Olympic disciplines, ethical issues, game producers, finance, gambling, data acquisition and analysis. Our article aims to presents selected research issues of esports in the ICT virtualization layer.

Quality evaluation is very important for haptic rendering. In this paper, an objective evaluation method for a haptic rendering system based on haptic perception features is proposed. In the method, the haptic rendering process is compared to the real world perception process in a simple standardized procedure based on feature extraction and data analysis. A complete evaluation process for a simple haptic rendering task of pressing a virtual spring is presented as an example to explain the method in detail. Compared with the traditional objective method based on error statistics, the method is more concerned about the consistency of human subjective feelings rather than physical parameters, which makes the evaluation process more consistent with the haptic perception mechanism. The results of comparative analysis show that the method presented in this paper is simple, gives reliable results reflecting the consistency with subjective feeling and has a better discrimination ability for different kinds of devices and algorithms compared with the traditional evaluation methods.

Robots that can comprehend and navigate their surroundings independently on their own are considered intelligent mobile robots (MR). Using a sophisticated set of controllers, artificial intelligence (AI), deep learning (DL), machine learning (ML), sensors, and computation for navigation, MR's can understand and navigate around their environments without even being connected to a cabled source of power. Mobility and intelligence are fundamental drivers of autonomous robots that are intended for their planned operations. They are becoming popular in a variety of fields, including business, industry, healthcare, education, government, agriculture, military operations, and even domestic settings, to optimize everyday activities. We describe different controllers, including proportional integral derivative (PID) controllers, model predictive controllers (MPCs), fuzzy logic controllers (FLCs), and reinforcement learning controllers used in robotics science. The main objective of this article is to demonstrate a comprehensive idea and basic working principle of controllers utilized by mobile robots (MR) for navigation. This work thoroughly investigates several available books and literature to provide a better understanding of the navigation strategies taken by MR. Future research trends and possible challenges to optimizing the MR navigation system are also discussed.