Unstructured Environment

Unstructured environment research focuses on enabling robots to navigate and operate effectively in complex, unpredictable settings lacking pre-defined structures or maps, such as agricultural fields, disaster zones, or natural terrains. Current research emphasizes robust perception using diverse sensor fusion (e.g., lidar, cameras, IMUs) and advanced planning algorithms (e.g., reinforcement learning, graph neural networks, model predictive control) to handle uncertainty and dynamic obstacles. This field is crucial for advancing autonomous systems in various applications, including robotics, autonomous driving, and environmental monitoring, by improving safety, efficiency, and adaptability in challenging real-world scenarios.