A Comprehensive Survey of Push Recovery Techniques for Standing and Walking Bipedal Robots

Abstract

Bipedal robots, which mimic human or animal motion, are expected to perform numerous tasks, such as delivering healthcare, conducting search and rescue missions in dangerous environments and serving industrial applications. Bipedal robots are required to interact with objects or people in their surroundings while performing their planned tasks. The main challenge faced by these robots is their ability to maintain balance in the presence of disturbances, such as external pushing forces applied to them and uneven terrains. Therefore, a push recovery control system that enables these robots to preserve stability while executing their intended tasks must be developed. This study investigates several push recovery control algorithms for bipedal robots operating in static (standing) or dynamic (walking) modes when faced with disturbances. The study further assesses the literature based on three factors: 1) the dynamic model used to represent the robot’s behaviour, 2) the control methods and 3) the required sensors. Moreover, this review paper emphasises the challenges that must be tackled in future research. These issues include the ability of bipedal robots to adapt rapidly to changing conditions in dynamic scenarios, the substantial energy consumption they require and the delays that arise from the complex and nonlinear structure of their movements. Hence, this research suggests some recommendations for effectively tackling these difficulties. 1) Sensory feedback approaches with machine learning algorithms should be employed to develop adaptable balance control systems that quickly learn from and react to different disturbances in real time. 2) Control algorithms that optimally balance stability and energy efficiency, such as predictive control algorithms that emulate the natural reflexes of humans, should be developed. 3) Hierarchical control systems should be used to partition the balance control problem into smaller stages, thus reducing the latency issues related to solving complex nonlinear equations.