Enhancing Quadruped Robot Walking on Unstructured Terrains: A Combination of Stable Blind Gait and Deep Reinforcement Learning

Legged robots, designed for high adaptability, are poised for deployment in hazardous tasks traditionally undertaken by humans, particularly in unstructured terrains where their wheeled counterparts cannot operate. Nevertheless, using them in unstructured settings necessitates advanced control techniques to maneuver without depending entirely on visual signals or pre-programmed instructions. To address these challenges, this research proposes a novel walking algorithm for quadruped robots that blends a stable blind gait without needing any visual cues with Deep Reinforcement Learning to enhance mobility across diverse terrains. The algorithm’s effectiveness was evaluated virtually, emphasizing the ability to regulate the robot’s leg movements and posture when reaching obstacles. Our results demonstrated a success rate of over 90% in the stair-climbing task, suggesting that the algorithm improved the robot’s mobility and stability. Although emphasizing a steady blind gait reduces reliance on visual cues, incorporating the algorithm with further sensory inputs and environmental awareness may improve the robot’s functionality and versatility in practical situations. More dynamic gaits and a wider variety of static and dynamic obstacles will be the focus of future algorithm development. Furthermore, validation in the real world will aid in detecting any shortcomings or potential areas for enhancement in the algorithm, thereby improving its adaptability and resilience in diverse settings and assignments.