A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence

Due to the advancements in cellular technologies and the dense deployment of cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the fifth-generation (5G) and beyond cellular networks is a promising solution to achieve safe UAV operation as well as enabling diversified applications with mission-specific payload data delivery. In particular, 5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in three-dimensional (3D) space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference. Besides the requirement of high-performance wireless communications, the ability to support effective and efficient sensing as well as network intelligence is also essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting aerial and ground users. In this paper, we provide a comprehensive overview of the latest research efforts on integrating UAVs into cellular networks, with an emphasis on how to exploit advanced techniques (e.g., intelligent reflecting surface, short packet transmission, energy harvesting, joint communication and radar sensing, and edge intelligence) to meet the diversified service requirements of next-generation wireless systems. Moreover, we highlight important directions for further investigation in future work.Comment: Accepted by IEEE JSA

Intelligent strategies for mobile robotics in laboratory automation

In this thesis a new intelligent framework is presented for the mobile robots in laboratory automation, which includes: a new multi-floor indoor navigation method is presented and an intelligent multi-floor path planning is proposed; a new signal filtering method is presented for the robots to forecast their indoor coordinates; a new human feature based strategy is proposed for the robot-human smart collision avoidance; a new robot power forecasting method is proposed to decide a distributed transportation task; a new blind approach is presented for the arm manipulations for the robots

Navigational control of multiple mobile robots in various environments

The thesis addresses the problem of mobile robots navigation in various cluttered environments and proposes methodologies based on a soft computing approach, concerning to three main techniques: Potential Field technique, Genetic Algorithm technique and Fuzzy Logic technique. The selected techniques along with their hybrid models, based on a mathematical support, solve the three main issues of path planning of robots such as environment representation, localization and navigation. The motivation of the thesis is based on some cutting edge issues for path planning and navigation capabilities, that retrieve the essential for various situations found in day-to-day life. For this purpose, complete algorithms are developed and analysed for standalone techniques and their hybrid models. In the potential field technique the local minima due to existence of dead cycle problem has been addressed and the possible solution for different situations has been carried out. In fuzzy logic technique the different controllers have been designed and their performance analysis has been done during their navigational control in various environments. Firstly, the fuzzy controller having all triangular members with five membership functions have been considered. Subsequently the membership functions are changed from Triangular to other functions, e.g. Trapezoidal, Gaussian functions and combinational form to have a more smooth and optimised control response. It has been found that the fuzzy controller with all Gaussian membership function works better compared to other chosen membership functions. Similarly the proposed Genetic algorithm is based on the suitable population size and fitness functions for finding out the robot steering angle in various cluttered field. At the end hybrid approaches e.g. Potential-Fuzzy, otential-Genetic, Fuzzy-Genetic and Potential-Fuzzy-Genetic are considered for navigation of multiple mobile robots. Initially the combination of two techniques has been selected in order to model the controllers and then all the techniques have been hybridized to get a better controller. These hybrid controllers are first designed and analysed for possible solutions for various situations provided by human intelligence. Then computer simulations have been executed extensively for various known and unknown environments. The proposed hybrid algorithms are embedded in the controllers of the real robots and tested in realistic scenarios to demonstrate the effectiveness of the developed controllers. Finally, the thesis concludes in a chapter describing the comparison of results acquired from various environments, showing that the developed algorithms achieve the main goals proposed by different approaches with a high level of simulations. The main contribution provided in the thesis is the definition and demonstration of the applicability of multiple mobile robots navigations with multiple targets in various environments based on the strategy of path optimisation