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Online Gait Generation Method Based on Neural Network for Humanoid Robot Fast Walking on Uneven Terrain

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  • Robot and Applications
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Abstract

Advanced humanoid robots highlight the ability of fast walking and adaptability to uneven terrain. However, owing to the complexity in walking dynamics, disturbances introduced by terrain height variations can adversely affect the bipedal walking performance. Moreover, to generate periodic gaits, most methods require to solve the gait generation problem by using nonlinear optimization approaches, resulting in difficulties for online control. To solve this problem, this paper proposes an online gait generation method to find periodic gaits for fast walking on uneven terrain by using a pre-trained neural network. First, to enhance the terrain adaptability, this paper proposes an improved walking pattern that allows the robots to skip the last single support phase. Such improvement enlarges the feasible step region when stepping down. A compensation strategy is also proposed to reduce the velocity tracking error. Then the improved whale swarm algorithm (IWSA) is applied to generate various datasets that cover the ranges of target velocities and terrain height variations. A back-propagation (BP) network is employed to train these datasets offline to learn the gait dynamics, which is further used to generate the optimal trajectories. Simulation results suggest that, compared with the current methods, the proposed method can solve the walking return map in a short time, with improvements in both maximum walking speed and terrain adaptability.

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Funding

This work was supported by the National Natural Science Foundation of China (grant number 51721092), and the program for the HUST Academic Frontier Youth Team (grant number 2017QYTD04).

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Authors and Affiliations

  1. State Key Laboratory of Digital Manufacturing Equipment and Technology, the School of Mechanical Science and Engineering, Huazhong University of Science and Technology (HUST), 1037, Luoyu Road, Hubei, Wuhan, China

    Haoran Zhong, Sicheng Xie, Xinyu Li, Liang Gao & Shengyu Lu

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  1. Haoran Zhong
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  2. Sicheng Xie
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  3. Xinyu Li
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  4. Liang Gao
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Correspondence to Liang Gao.

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Haoran Zhong received his B.S. degree from East China University of Science and Technology (ECUST), Shanghai, China, in 2014. He is now a Doctoral Candidate with the Department of Industrial and Manufacturing Systems Engineering, State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology (HUST). His current research interests include humanoid robot, series elastic actuator (SEA), and intelligent algorithms.

Sicheng Xie received his B.S. degree in mechanical engineering from the Huazhong University of Science and Technology (HUST), Wuhan, China, in 2019. He is now a Doctoral Candidate with the Department of Industrial and Manufacturing Systems Engineering, State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology (HUST). His current research interests include humanoid robot and gait planning.

Xinyu Li received his Ph.D. degree in industrial engineering from the Huazhong University of Science and Technology (HUST), Wuhan, China, in 2009. He is currently a Professor with the Department of Industrial and Manufacturing Systems Engineering, State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, HUST. He has authored more than 80 refereed articles. His research interests include intelligent algorithm, big data, and machine learning.

Liang Gao received his Ph.D. degree in mechatronic engineering from the Huazhong University of Science and Technology (HUST), Wuhan, China, in 2002. He is currently a Professor with the Department of Industrial and Manufacturing System Engineering, State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, HUST. He has authored more than 170 refereed articles. His research interests include operations research and optimization, big data, and machine learning.

Shengyu Lu received his M.Eng. degree in mechanical engineering from the Huazhong University of Science and Technology (HUST), Wuhan, China, in 2019. He is now a Doctoral Candidate with the Department of Industrial and Manufacturing Systems Engineering, State Key Laboratory of Digital Manufacturing Equipment and Technology, the School of Mechanical Science and Engineering, Huazhong University of Science and Technology (HUST). His current research interests include humanoid robot design and gait planning.

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Zhong, H., Xie, S., Li, X. et al. Online Gait Generation Method Based on Neural Network for Humanoid Robot Fast Walking on Uneven Terrain. Int. J. Control Autom. Syst. 20, 941–955 (2022). https://doi.org/10.1007/s12555-021-0099-8

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