Scale Navigation

Scale navigation research focuses on enabling autonomous agents, from robots to virtual entities, to effectively traverse large-scale environments, whether physical or simulated. Current efforts concentrate on developing robust and efficient navigation algorithms, often employing hierarchical reinforcement learning, deep learning for perception (e.g., object detection and mapping), and optimization techniques like quadratic programming with control barrier functions to ensure safety and feasibility. These advancements are crucial for improving the capabilities of autonomous systems in diverse applications, including agricultural robotics, indoor navigation, and urban transportation planning, by providing more accurate, reliable, and computationally efficient navigation solutions.