Autonomous Robot Navigation

Autonomous robot navigation focuses on enabling robots to move safely and efficiently through various environments, often without explicit human guidance. Current research emphasizes robust perception using sensor fusion (LiDAR, cameras, IMUs) and advanced planning algorithms, including model predictive control (MPC), reinforcement learning (RL), and large language models (LLMs) for both path planning and behavioral adaptation to complex, dynamic scenarios (e.g., crowded environments, unstructured terrain). These advancements are crucial for deploying robots in diverse real-world applications, such as search and rescue, delivery services, and industrial automation, improving efficiency and safety in human-robot interaction.