The recent trend of amalgamating different technologies has undoubtedly changed the dynamics of the modern world. Researchers have been developing hybrid algorithms, integrating and evaluating mathematics, and measuring the efficiency and computational cost of several physical systems. This type of emerging trend is very common when optimizing various real-world applications, for example, in the field of unmanned vehicles (UVs), including unmanned ground, aerial, underwater, and surface vehicles. These approaches have been introduced to ensure efficiency and their utilization in dull, dirty, difficult, and dangerous environments.

These systems are currently researched in both academia and industry for several high-tech operations, such as high-quality computer vision-based surveillance of specific areas, package manipulation from one place to another with a variable payload, and photography, among others. Most of these unmanned autonomous vehicles have been modeled, controlled, and stabilized in different ways to acquire the best and most robust performance and can be used in terrestrial, underwater, and aerial environments. However, there are many challenges facing the use of these vehicles at present, such as the effect of unmodeled dynamic factors and external disturbances on trajectory tracking, non-holonomic and chattering-like issues, instability due to bounded conditions, the sluggish convergence rate of UVs, nonlinear dynamics and singularity issues, and fault tolerance issues.

This Special issue aims to investigate the recent trends of integrated engineering, computing, and mathematical methods proposed for improving the performance of unmanned autonomous ground, aerial, underwater, and surface vehicles. We invite research based on real-time measurement and experimentation along with the integration and hybridization of different computing techniques. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Nonlinear hybrid control and observer design for unmanned autonomous vehicles
• Estimation techniques for unmanned autonomous vehicles
• Emerging global linearization-based control methods
• Multiple local models-based control designs
• Lyapunov theory-based control algorithms and approaches
• Effect of unmodeled dynamic factors on unmanned autonomous vehicles
• Integrated algorithms to address issues such as the Zeno effect, under-actuation, and noise
• Bio-inspired based formation control and tracking for UVs
• Mathematical and less computational algorithms for system identification