Recent Advances in Legged Robot Locomotion and Control

Abstract

 In recent years, legged robots have emerged as powerful mobile robots capable of navigating complex and uncontrolled environments where wheeled robots often fail. This review provides a comprehensive overview of recent studies and advances in legged robot locomotion and control, focusing on integrating fundamental control strategies. The paper begins by examining the evolution of legged robot designs, from early prototypes to quadrupeds and humanoids. It then discusses critical locomotion modeling, including simplified spring-mass systems and full-body dynamic simulations, and highlights how they utilize control strategies. Recent developments in model-based control, such as whole-body and model predictive control, are compared with emerging learning-based methods like reinforcement learning and hybrid control architectures. This paper additionally focuses on addressing the challenges of sensing, terrain adaptation, and the integration of perception for real-time gait adjustment. Finally, the paper identifies ongoing challenges such as energy efficiency, robustness, and sim-to-real transfer, offering perspectives on future research directions. This review aims to serve as a valuable resource for researchers and practitioners seeking to advance the capabilities of legged robotic systems.