The field of autonomous Unmanned Aerial Vehicle (UAV) navigation is moving towards more efficient and robust control systems. Recent developments focus on leveraging edge-computing, vision-based gesture recognition, and hyper-efficient perception and planning to enable UAVs to fly swiftly and avoid obstacles in cluttered environments. Path planning is also being enhanced through the integration of vision language models and rapidly-exploring random trees, leading to improved sampling efficiency and path quality. Furthermore, the incorporation of cognitive guardrails in open-world decision-making is ensuring safe and sensible decision-making under uncertainty. Noteworthy papers include: HEPP, which proposes a hyper-efficient perception and planning system for high-speed obstacle avoidance. VLM-RRT, which integrates vision language models with rapidly-exploring random trees for enhanced path-planning efficiency.