Safe Robot Navigation

Safe robot navigation focuses on enabling robots to move reliably and safely in diverse, often unpredictable environments, prioritizing collision avoidance and task completion. Current research emphasizes robust perception (e.g., using LiDAR, cameras, and neural networks for scene understanding), safe planning algorithms (including model predictive control, control barrier functions, and sampling-based methods like RRT*), and uncertainty management (through probabilistic models and distributionally robust optimization). These advancements are crucial for deploying robots in real-world applications like autonomous driving, warehouse logistics, and planetary exploration, improving efficiency and safety in human-robot interaction.