The field of autonomous systems is moving towards more robust and resilient control methods, with a focus on trajectory tracking and localization in dynamic environments. Researchers are exploring new approaches to guidance laws, such as bifurcation-based methods, and developing more efficient controllers for autonomous surface vehicles. Additionally, there is a growing interest in multi-drone systems for marine robot tracking and localization. The use of Lie algebraic methods and equivariant tracking control is also becoming more prevalent. Furthermore, the development of jammer-resilient synchronization ASICs and model-free control design methods is improving the overall robustness of autonomous systems. Noteworthy papers include: Online Learning-Enhanced Lie Algebraic MPC for Robust Trajectory Tracking of Autonomous Surface Vehicles, which demonstrates superior tracking accuracy in dynamic marine conditions. Stable Multi-Drone GNSS Tracking System for Marine Robots, which presents a scalable and robust tracking system for surface and near-surface marine robots. Equivariant Tracking Control for Fully Actuated Mechanical Systems on Matrix Lie Groups, which provides a new insight into the representation of Lie-Poisson systems for tracking control. Multi-Hypotheses Ego-Tracking for Resilient Navigation, which proposes a resilient navigation architecture for autonomous robots under spoofing attacks. A Jammer-Resilient 2.87 mm^2 1.28 MS/s 310 mW Multi-Antenna Synchronization ASIC in 65 nm, which presents the first ASIC implementation of jammer-resilient multi-antenna time synchronization.