The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, and developing efficient collaborative decision-making algorithms for a swarm of UAVs limit their autonomy, robustness, and reliability. Thus, a growing focus has been witnessed on collaborative communication to allow a swarm of UAVs to coordinate and communicate autonomously for the cooperative completion of tasks in a short time with improved efficiency and reliability. This work presents a comprehensive review of collaborative communication in a multi-UAV system. We thoroughly discuss the characteristics of intelligent UAVs and their communication and control requirements for autonomous collaboration and coordination. Moreover, we review various UAV collaboration tasks, summarize the applications of UAV swarm networks for dense urban environments and present the use case scenarios to highlight the current developments of UAV-based applications in various domains. Finally, we identify several exciting future research direction that needs attention for advancing the research in collaborative UAVs.

Unmanned Aerial Vehicles (UAVs) have emerged as a transformative technology across diverse sectors, offering adaptable solutions to complex challenges in both military and civilian domains. Their expanding capabilities present a platform for further advancement by integrating cutting-edge computational tools like Artificial Intelligence (AI) and Machine Learning (ML) algorithms. These advancements have significantly impacted various facets of human life, fostering an era of unparalleled efficiency and convenience. Large Language Models (LLMs), a key component of AI, exhibit remarkable learning and adaptation capabilities within deployed environments, demonstrating an evolving form of intelligence with the potential to approach human-level proficiency. This work explores the significant potential of integrating UAVs and LLMs to propel the development of autonomous systems. We comprehensively review LLM architectures, evaluating their suitability for UAV integration. Additionally, we summarize the state-of-the-art LLM-based UAV architectures and identify novel opportunities for LLM embedding within UAV frameworks. Notably, we focus on leveraging LLMs to refine data analysis and decision-making processes, specifically for enhanced spectral sensing and sharing in UAV applications. Furthermore, we investigate how LLM integration expands the scope of existing UAV applications, enabling autonomous data processing, improved decision-making, and faster response times in emergency scenarios like disaster response and network restoration. Finally, we highlight crucial areas for future research that are critical for facilitating the effective integration of LLMs and UAVs.

Continuum robots have been the subject of extensive research due to their potential use in a wide range of applications. In this paper, we propose a new continuum robot with three backbones, and provide a unified analytic method for the kinematics and dynamics of a multiple-backbone continuum robot. The robot achieves actuation by independently pulling three backbones to carry out a bending motion of two-degrees-of-freedom (DoF). A three-dimensional CAD model of the robot is built and the kinematical equation is established on the basis of the Euler-Bernoulli beam. The dynamical model of the continuum robot is constructed by using the Lagrange method. The simulation and the experiment's validation results show the continuum robot can exactly bend into pre-set angles in the two-dimensional space (the maximum error is less than 5% of the robot length) and can make a circular motion in three-dimensional space. The results demonstrate that the proposed analytic method for the kinematics and dynamics of a continuum robot is feasible.

Sensing technology has long been an integral part of modern developments that have brought several benefits to various domains ranging from civil and military to commercial and healthcare applications. However, the sensor’s inadequate energy is the primary concern that affects their long-term sustainable operations and remote location deployment. Therefore, self-powered sensors with the ability to scavenge energy from the environment to self-drive the sensor’s operation have received significant attention in recent years. Self-powered sensors with energy harvesting technology can convert the ambient energy available in the environment, such as mechanical, thermal, wave, and solar energy, into electric energy to self-power the sensors for long-term sustainable operations. Self-powered sensors have been a vital part of technological advances since the 21st century and the existing literature has thoroughly studied the fabrication material of self-powered sensors and their energy harvestingmechanisms. However, the current literature lacks a comprehensive review on state-of-the-art design solutions for self-powered sensors and their implementation challenges. To address the limitations of past and current reviews, we have reviewed state-of-the-art architectures of self-powered sensors and their applications in various domains (such as civil, automotive systems, environmental, robotic, human-machine interactions, healthcare and fitness applications). Moreover, following the study of existing designs of self-powered sensors, we provide a general architecture for a self-powered sensor design, and we discuss the implementation challenges of self-powered sensors that affect their long-term operations. These challenges include energy, size, cost, robustness, stability, and Artificial Intelligence (AI). We also discuss possible solutions to address the implementation challenges we have identified to enable the design of cost-effective self-powered sensor-based monitoring systems for diverse applications.

The introduction of sensor technology in our daily lives has brought comfort, convenience, and improved health over the past few decades. Technological advances further expanded the use of medical sensors by reducing their size and costs. Medical sensors improve the intelligence and capabilities of healthcare services including, remote health monitoring, surgical procedures, therapy, and rehabilitation. We present a comprehensive review of medical sensors in the last 50 years focusing on their deployment in healthcare applications. The review also discusses the role of Internet of Things (IoT) technology in enhancing the capabilities of sensor technologies for the healthcare domain. Moreover, we also investigate the benefits and challenges of various integrated architectures which have been proposed recently to seamlessly integrate heterogeneous medical sensors with emerging technologies and paradigms that include edge, Mobile Cloud Computing (MCC), fog, and cloud computing technologies. Finally, we identify future challenges that must be addressed to achieve the maximum potential and benefits of medical sensor technologies and ultimately provide robust, scalable, reliable, and cost-effective healthcare delivery.