The field of autonomous systems and robotics is moving towards developing more efficient, resilient, and collaborative systems. Recent developments have focused on improving planning and navigation capabilities in complex and dynamic environments. Researchers are exploring new frameworks and algorithms that enable autonomous systems to adapt to uncertain conditions, avoid failures, and optimize their performance. Notable advancements include the development of massively parallel solvers, edge-accelerated UAV frameworks, and meta-cognitive swarm intelligence frameworks. These innovations have the potential to significantly improve the autonomy and effectiveness of robotic systems in various applications, including wildfire response, search and rescue, and collaborative manipulation. Noteworthy papers include: Vectorized Online POMDP Planning, which proposes a novel parallel online solver that achieves significant efficiency gains. AeroResQ, an edge-accelerated UAV framework designed for scalable and resilient escape route planning in wildfire scenarios, demonstrates impressive performance in realistic emulated setups. A Meta-Cognitive Swarm Intelligence Framework, which introduces a novel swarm intelligence framework that enables UAVs to autonomously sense, adapt, and recover from planning failures in real-time, showing superior performance in simulations.