With the advent of artificial intelligence (AI) technology, there has been an increase in the utilisation of autonomous systems in difficult or dangerous environments. These systems are already under consideration in academia and industry for a variety of tasks, such as high-quality computer vision-based surveillance of different online and offline construction sites, package delivery with variable payload mass, and for photography purposes, such as the promotion of tourism.

Most of these autonomous vehicles are modelled in different ways and have appropriate observer designs to tackle unmodelled dynamic factors and disturbances. These autonomous vehicles can be underactuated to decrease power consumption but maintain a considerable degree of freedom (DOF). Moreover, these vehicles can be used in terrestrial, underwater, and aerial environments. The stable operation of an autonomous vehicle requires smart modelling of its dynamics and the proposition of a hybrid/intelligent control law. This allows a behaviour that is cooperative with the physical world, with maximum efficiency and precision. In recent years, due to the extensive use of machine learning and artificial intelligence to make real autonomous systems, applications have significantly reduced. Moreover, autonomous vehicles can be used for several purposes to resolve real-world issues with the addition of the internet, for example, the Internet of Robotic Things (IoRT).

The aim of this Special Issue is to investigate recent advances in unmanned autonomous systems (UAS), and the use of machine learning and artificial intelligence, along with bio-inspired and IoT trends, for the development of ground, air, and underwater vehicles in various applications. We are mainly looking for research with practical results of deployed systems with a brief overview of the challenges they faced, along with their achievements, measurements, and modern applications amalgamated with artificial intelligence.

Potential topics include but are not limited to the following:

• Robotic manipulation with unmodelled factors
• Artificial intelligence and machine learning based unmanned autonomous systems
• Nonlinear and under-actuated robotic systems
• Advanced system identification
• Computer vision and control applications
• Formation and fault tolerance hybrid control design
• Advanced bio-inspired robotics
• Field robotics
• Robotics software design and engineering
• Sensor based UAS or Internet of Robotic Things (IoRT)