Traversability Analysis

Traversability analysis focuses on determining whether a robot can safely navigate a given terrain, crucial for autonomous navigation in unstructured environments. Current research emphasizes integrating diverse sensor data (LiDAR, cameras, force sensors) with various machine learning models (e.g., neural networks, SVMs, Gaussian Processes) and classical planning algorithms (e.g., A*, RRT*) to create robust traversability maps. This field is vital for advancing robotics in diverse applications, including search and rescue, agriculture, and planetary exploration, by enabling safer and more efficient autonomous navigation in complex and unpredictable terrains. The development of computationally efficient and generalizable methods remains a key focus.