Terrain Navigation

Terrain navigation research focuses on enabling autonomous robots to safely and efficiently traverse uneven and unpredictable environments. Current efforts concentrate on developing robust perception systems using sensors like cameras, LiDAR, and side-scan sonar, coupled with advanced planning algorithms such as RRT*, model predictive control, and Gaussian processes to generate traversable paths. These methods are often enhanced by machine learning techniques, including neural networks and self-supervised learning, to improve terrain classification and adapt to varying surface properties. This work is crucial for advancing robotics in diverse fields, including search and rescue, agriculture, and planetary exploration.