The field of autonomous vehicles is witnessing significant advancements in navigation and control systems. Researchers are exploring innovative approaches to improve the precision, robustness, and safety of these systems. One notable direction is the integration of advanced mathematical techniques, such as trident quaternions and control barrier functions, to enhance navigation and control algorithms. Another area of focus is the development of fault-tolerant control systems that can adapt to failures and maintain stability in extreme conditions. The use of machine learning and neural networks is also becoming increasingly popular in the design of safe and stable control systems. Noteworthy papers in this area include:

• A novel unmanned surface vehicle (USV)-autonomous underwater vehicle (AUV) collaborative system that enhances underwater task performance in extreme sea conditions.
• A cascade IPG observer for underwater robot state estimation that outperforms existing methods in terms of positional accuracy and variance.
• An embedded safe reactive navigation system for multirotor systems using control barrier functions that ensures safe control actions in dynamic environments.
• A neural ODE framework with differentiable QP layers for safe and stable control design that eliminates the reliance on nominal controllers or large datasets.