The field of autonomous navigation and control is moving towards more sophisticated and robust methods for ensuring safety and efficiency in complex environments. Researchers are exploring innovative approaches to motion planning, obstacle avoidance, and control allocation, with a focus on real-time performance and adaptability to changing conditions. Notable developments include the integration of control barrier functions with model predictive control, the use of factor graphs for efficient optimization, and the application of geometric control frameworks for 3D source seeking. These advancements have the potential to significantly improve the reliability and autonomy of robotic systems in various applications. Noteworthy papers include: Point Cloud-Based Control Barrier Functions for Model Predictive Control, which proposes a novel motion planning algorithm for safety-critical navigation. Distributed Connectivity Maintenance and Recovery for Quadrotor Motion Planning, which presents a real-time distributed framework for multi-robot navigation certified by high-order control barrier functions. Integrated Planning and Control on Manifolds, which introduces a factor-graph based MPC toolkit for unified system dynamics, constraints, and objectives. A Formal gatekeeper Framework for Safe Dual Control with Active Exploration, which integrates robust planning with active exploration under formal guarantees. Distributed 3D Source Seeking via SO(3) Geometric Control of Robot Swarms, which presents a geometric control framework on the Lie group SO(3) for 3D source-seeking by robots.