Heterogeneous Terrain

Heterogeneous terrain research focuses on enabling robots and autonomous systems to navigate and operate effectively in environments with unpredictable and varied surfaces, such as uneven ground, deformable soil, and obstacles. Current research emphasizes developing robust perception models (e.g., using deep learning architectures like DeepLab v3+ and incorporating visual attention mechanisms) and control strategies (e.g., reinforcement learning, variable impedance control, and probabilistic path planning) to handle uncertainties in terrain properties and achieve reliable locomotion and manipulation. This work is crucial for advancing robotics in diverse fields, including planetary exploration, agriculture, and search and rescue, where reliable autonomous operation in complex environments is essential.