Gait Transition

Gait transition research focuses on enabling robots to smoothly and efficiently switch between different locomotion patterns, optimizing performance for varied terrains and speeds. Current efforts leverage diverse approaches, including optimal control methods with geometric representations, deep learning for enhanced stability and reachability analysis, and biologically-inspired neural networks (CPGs) to generate and control gait transitions. This research is crucial for developing more robust and adaptable legged robots for applications ranging from search and rescue to exploration in challenging environments, improving both energy efficiency and fault tolerance.