Deformable Terrain

Deformable terrain modeling focuses on accurately simulating the interaction between vehicles or robots and soft, uneven surfaces, primarily to improve autonomous navigation and robotic manipulation in challenging environments like lunar regolith or off-road settings. Current research emphasizes developing physics-based simulators incorporating techniques like the Discrete Element Method and integrating them with machine learning models (e.g., deep reinforcement learning, autoencoders) for tasks such as slip prediction, traversability assessment, and path planning. These advancements are crucial for enhancing the safety and efficiency of autonomous systems in diverse and unpredictable terrains, with applications ranging from space exploration to agriculture and construction.