An overview on bipedal gait control methods

Hu, Chenghao; Xie, Sicheng; Gao, Liang; Lu, Shengyu; Li, Jingyuan

doi:10.1049/cim2.12080

Cited by 5 publications

(1 citation statement)

References 109 publications

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“…Reinforcement learning-based strategies for bipedal robot gait control aim to enable robots to autonomously learn and optimize their walking methods to suit varied ground and environmental conditions. To enhance the adaptability of these learning strategies while reducing their computational demands, current research trends towards integrating reinforcement learning with conventional control methods [ 17 ]. For example, Li and colleagues developed a hierarchical framework for quadruped robots’ gait planning that combines the DDPG algorithm with Model Predictive Control (MPC), achieving optimal action control [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Biped Robots Control in Gusty Environments with Adaptive Exploration Based DDPG

Zhang,

Sun,

Sun

et al. 2024

Biomimetics

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As technology rapidly evolves, the application of bipedal robots in various environments has widely expanded. These robots, compared to their wheeled counterparts, exhibit a greater degree of freedom and a higher complexity in control, making the challenge of maintaining balance and stability under changing wind speeds particularly intricate. Overcoming this challenge is critical as it enables bipedal robots to sustain more stable gaits during outdoor tasks, thereby increasing safety and enhancing operational efficiency in outdoor settings. To transcend the constraints of existing methodologies, this research introduces an adaptive bio-inspired exploration framework for bipedal robots facing wind disturbances, which is based on the Deep Deterministic Policy Gradient (DDPG) approach. This framework allows the robots to perceive their bodily states through wind force inputs and adaptively modify their exploration coefficients. Additionally, to address the convergence challenges posed by sparse rewards, this study incorporates Hindsight Experience Replay (HER) and a reward-reshaping strategy to provide safer and more effective training guidance for the agents. Simulation outcomes reveal that robots utilizing this advanced method can more swiftly explore behaviors that contribute to stability in complex conditions, and demonstrate improvements in training speed and walking distance over traditional DDPG algorithms.

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Section: Introductionmentioning

confidence: 99%

Biped Robots Control in Gusty Environments with Adaptive Exploration Based DDPG

Zhang,

Sun,

Sun

et al. 2024

Biomimetics

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Dynamic Modelling and Optimal Sliding Mode Control of the Wearable Rehabilitative Bipedal Cable Robot with 7 Degrees of Freedom

Sajedifar,

Korayem,

Allahverdi

2024

J Intell Robot Syst

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Although robot-assisted physiotherapy has gained increasing attention in recent years, the use of wearable rehabilitation robots for lower limbs has shown reduced efficiency due to additional equipment and motors located at the center of the joint, increasing complexity and load on disabled patients. This paper proposes a novel rehabilitation approach by eliminating motors and equipment from the center of joints and placing them on a fixed platform using cable-based power transmission. A proposed model of a 14 cable-driven bipedal robot with 7 degrees of freedom has been used to model a lower limb rehabilitation robot corresponding to it. The dynamic equations of the robot are derived using the Euler-Lagrange method. The sliding mode control technique is utilized to offer accurate control for tracking desired trajectories, ensuring smoothness despite disturbances, and reducing tracking errors. This approach is employed to help prevent patients from falling and support them in maintaining balance during rehabilitative exercises. To ensure that cables exert positive tension, the sliding mode controller was combined with quadratic programming optimization, minimizing path error while constraining the controller input torque to be non-negative. The performance of the proposed controller was assessed by considering several control gains resulting in K = 10 identified as the most effective one. The feasibility of this approach to rehabilitation is demonstrated by the numerical results in MATLAB simulation, which show that the RMSE amount of the right and left hip and thigh angles are 0.29, 0.37, 0.31, and 0.44, respectively which verified an improved rehabilitation process. Also, the correlation coefficient between the Adams and MATLAB simulation results for motor torque was found to be 0.98, indicating a high degree of correlation between the two simulation results.

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