Gait Planning

Gait planning focuses on generating efficient and stable locomotion patterns for robots, particularly in challenging environments. Current research emphasizes integrating path planning with gait generation, often using model predictive control (MPC) and reinforcement learning (RL) algorithms, sometimes enhanced by techniques like Monte Carlo Tree Search and deep learning for improved robustness and adaptability. This field is crucial for advancing robotics, enabling more agile and versatile robots capable of navigating complex terrains and performing intricate tasks, with applications ranging from search and rescue to manufacturing.