Efficient Navigation

Efficient navigation research aims to develop algorithms enabling robots and autonomous vehicles to reach goals safely and quickly, especially in complex or dynamic environments. Current work focuses on integrating various techniques, including model predictive control (e.g., MPPI), deep reinforcement learning, and Gaussian processes, often within frameworks incorporating semantic understanding, multi-agent collaboration, and efficient map representations (e.g., scene graphs, tomograms). These advancements are crucial for improving the robustness and reliability of autonomous systems in diverse applications, from warehouse logistics and search-and-rescue to assistive robotics and exploration of challenging terrains.