The increasing demand for automation has led to the complexity of the design and operation of robotic systems. This paper presents a systematic literature review (SLR) focused on the applications and challenges of Data Distribution Service (DDS)-based middleware in robotics from 2006 to 2024. We explore the pivotal role of DDS in facilitating efficient communication across heterogeneous robotic systems, enabling seamless integration of actuators, sensors, and computational elements. Our review identifies key applications of DDS in various robotic domains, including multi-robot coordination, real-time data processing, and cloud–edge–end fusion architectures, which collectively enhance the performance and scalability of robotic operations. Furthermore, we identify several challenges associated with implementing DDS in robotic systems, such as security vulnerabilities, performance and scalability requirements, and the complexities of real-time data transmission. By analyzing recent advancements and case studies, we provide insights into the potential of DDS to overcome these challenges while ensuring robust and reliable communication in dynamic environments. This paper aims to contribute to the transformative impact of DDS-based middleware in robotics, offering a comprehensive overview of its benefits, applications, and security implications. Our findings underscore the necessity for continued research and development in this area, paving the way for more resilient and intelligent robotic systems that operate effectively in real-world scenarios. This review not only fills existing gaps in the literature but also serves as a foundational resource for researchers and practitioners seeking to leverage DDS in the design and implementation of next-generation robotic solutions.

Quadrotor Unmanned Aerial Vehicles (UAVs) are generally underactuated systems and when load is attached for transportation purposes, the system complexity increases. Therefore, the need to appropriately control such systems becomes paramount as they usually navigate in cluttered environments. In this work, we conceptualize the problem of cooperative tracking control for a Multi-agent UAV load system (MUAVLs) whereby each UAV is divided into global position and local attitude subsystems. To ensure that formation is maintained in a desired path, Neural Network Graph-theoretic Distributed Adaptive Control (NNGDAC) is used for the position subsystem with a modified virtual force artificial potential field for obstacle avoidance. Another Adaptive Feedback Linearization (AFBL) controller is also designed for the attitude subsystem which is verified by simulation results.

Quadrotor Unmanned Aerial Vehicles (UAVs) are highly nonlinear and underactuated in nature as they possess fewer control inputs than control variables. A combination multiple UAVs conveying a single load is more complex but offers some advantages due to it’s complimentary nature, as such, heavier loads can be conveyed compared to using single UAVs. Consequently, the need to avoid inter-agent collision necessitates the use of multi-agent control strategies which takes the presence of load disturbances into account. Therefore, in this work a Multi-agent UAV-Load system comprising dual-UAV with a single load is broken into altitude/position and attitude subsystem dynamics. To ensure that formation is maintained in a desired path, using rigid graph theory, an Adaptive Backstepping Multi-agent Control, based on Lyapunov theory is designed for the position subsystem in the presence of load disturbances. Furthermore, another Backstepping Finite-time Sliding Mode Control is designed for the attitude subsystem which is verified by simulation.

The United Nations predicted that 68% of the world’s population will live in urban areas by 2050. Currently, the resources in urban areas are barely enough to cater to the inhabitants’ needs. Scientists believe that automation is the solution. Hence, they believe that Smart Cities could offer a sustainable solution for the increasing rural-to-urban migration because they improve the quality of service by efficiently managing the limited resources the citizens share. However, community laws stipulate when and who governs the cities. These officials are responsible for decision making, which limits the quality of automation and smartness of the city. Integrated Operation Centers (IOCs) help to minimize this limitation. They gather information, process it, and visualize it for the managers. Thus, IOCs enable them to make informed and quick decisions on critical issues. This paper processed 64 conferences and journals on IOCs using the PRISMA method. The systematic literature review investigates the applications of IOCs, and we present a taxonomy for them. Also, we looked at how they impact humanity and environmental sustainability. We found that IOCs help to coordinate automation, disaster response, and security. They also help to conserve natural resources. Finally, we uncover some challenges of implementing IOCs and possible research directions.