Footstep Planning

Footstep planning for bipedal robots focuses on generating stable and efficient walking gaits, addressing the complex interplay of robot dynamics, terrain characteristics, and computational constraints. Current research emphasizes real-time performance through techniques like model predictive control (MPC), often augmented with reinforcement learning (RL) to handle model inaccuracies and adapt to unforeseen circumstances; algorithms such as A* and novel approaches based on deep learning (e.g., LSTMs) are being explored to improve efficiency and robustness. These advancements are crucial for enabling more agile and adaptable legged robots capable of navigating complex and unstructured environments, with implications for robotics, automation, and assistive technologies.