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Cooperative control strategy of wheel-legged robot based on attitude balance

Published online by Cambridge University Press:  12 October 2022

Yaojie Shen ,
Guangrong Chen Open the ORCID record for Guangrong Chen [Opens in a new window] ,
Zhaoyang Li ,
Ningze Wei ,
Huafeng Lu ,
Qingyu Meng  and
Sheng Guo
Yaojie Shen
Affiliation:
Robotics Research Center, Beijing Jiaotong University, Beijing, 100044, P.R. China
Guangrong Chen*
Affiliation:
Robotics Research Center, Beijing Jiaotong University, Beijing, 100044, P.R. China
Zhaoyang Li
Affiliation:
Robotics Research Center, Beijing Jiaotong University, Beijing, 100044, P.R. China
Ningze Wei
Affiliation:
Robotics Research Center, Beijing Jiaotong University, Beijing, 100044, P.R. China
Huafeng Lu
Affiliation:
Robotics Research Center, Beijing Jiaotong University, Beijing, 100044, P.R. China
Qingyu Meng
Affiliation:
Robotics Research Center, Beijing Jiaotong University, Beijing, 100044, P.R. China
Sheng Guo
Affiliation:
Robotics Research Center, Beijing Jiaotong University, Beijing, 100044, P.R. China
*
*Corresponding author. E-mail: grchen@bjtu.edu.cn
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Abstract

To integrate the uneven terrain adaptivity of legged robots and the fast capacity of wheeled robots on even terrains, a four wheel-legged robot is addressed and the cooperative control strategy of wheels and legs based on attitude balance is investigated. Firstly, the kinematics of wheel-legged robot is analyzed, which contains the legged and wheeled motion modal. Secondly, the cooperative control strategy of wheel-legged robot based on attitude balance is proposed. The attitude is calculated by using the quaternion method and complementary filtering, and the attitude stability control of the wheel-legged robot is studied. The trajectory planning of leg motion including walk and trot gait is implemented, and the differential control of wheeled motion is deduced. And then, the cooperative motion control of wheels and legs is achieved by keeping the attitude balance of robot body. Finally, a small prototype is set up to validate the feasibility and effectiveness of proposed method. The experimental results show that the established wheel-legged robot can do walk, trot, and wheel-leg compound motion to overcome many complex terrains and environments.

Keywords

wheel-legged robotcooperative controlcontrol strategyattitude balancetrajectory planning
Type
Research Article
Information
Robotica , Volume 41 , Issue 2 , February 2023 , pp. 566 - 586
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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References

Zhang, C., Chen, G., Li, Z., Qiu, X. and Guo, S., “Design and control of a foldable and reconfigurable multi-terrain vehicle with variable wheelbase,” J. Mech. Robot. 15(2), 024501 (9 pages) (2022).CrossRefGoogle Scholar
Chen, G., Wei, N., Li, J. and Lu, H., “Design and Simulation Analysis of A Bionic Ostrich Robot,” In: Biomechanics and Modeling in Mechanobiology (2022) pp. 1–21.Google Scholar
Ba, K.-X., Song, Y.-H., Shi, Y.-P., Wang, C.-Y., Ma, G.-L., Wang, Y., Yu, B. and Yuan, L.-P., “A novel one-dimensional force sensor calibration method to improve the contact force solution accuracy for legged robot,” Mech. Mach. Theory 169(8), 104685 (2022).CrossRefGoogle Scholar
Rubio, F., Valero, F. and Llopis-Albert, C., “A review of mobile robots: Concepts, methods, theoretical framework, and applications,” Int. J. Adv. Robot. Syst. 16(2), 1729881419839596 (2019).CrossRefGoogle Scholar
Li, J., Wang, J., Peng, H., Zhang, L., Hu, Y. and Su, H., “Neural fuzzy approximation enhanced autonomous tracking control of the wheel-legged robot under uncertain physical interaction,” Neurocomputing 410, 342–353 (2020).CrossRefGoogle Scholar
Chen, Z., Wang, S., Wang, J., Xu, K., Lei, T., Zhang, H., Wang, X., Liu, D. and Si, J., “Control strategy of stable walking for a hexapod wheel-legged robot,” ISA Trans. 108(03), 367–380 (2021).CrossRefGoogle ScholarPubMed
Wang, S., Chen, Z., Li, J., Wang, J., Li, J. and Zhao, J., “Flexible motion framework of the six wheel-legged robot: Experimental results,” IEEE/ASME Trans. Mechatron. 27(4), 2246–2257 (2022).CrossRefGoogle Scholar
Li, X., Zhou, H., Feng, H., Zhang, S. and Fu, Y., “Design and Experiments of A Novel Hydraulic Wheel-Legged Robot (WLR),” In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2018) pp. 3292–3297.CrossRefGoogle Scholar
Peng, H., Wang, J., Shen, W. and Shi, D., “Cooperative attitude control for a wheel-legged robot,” Peer Peer Netw. Appl. 12(6), 1741–1752 (2019).CrossRefGoogle Scholar
Xu, K., Wang, S., Yue, B., Wang, J., Peng, H., Liu, D., Chen, Z. and Shi, M., “Adaptive impedance control with variable target stiffness for wheel-legged robot on complex unknown terrain,” Mechatronics 69(7), 102388 (2020).CrossRefGoogle Scholar
Ni, L., Wu, L. and Zhang, H., “Parameters uncertainty analysis of posture control of a four-wheel-legged robot with series slow active suspension system,” Mech. Mach. Theory 175(1), 104966 (2022).CrossRefGoogle Scholar
Raza, F., Zhu, W. and Hayashibe, M., “Balance stability augmentation for wheel-legged biped robot through arm acceleration control,” IEEE Access 9, 54022–54031 (2021).CrossRefGoogle Scholar
Wang, S., Cui, L., Zhang, J., Lai, J., Zhang, D., Chen, K., Zheng, Y., Zhang, Z. and Jiang, Z.-P., “Balance Control of A Novel Wheel-Legged Robot: Design and Experiments,” In: 2021 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2021) pp. 6782–6788.CrossRefGoogle Scholar
Cui, L., Wang, S., Zhang, J., Zhang, D., Lai, J., Zheng, Y., Zhang, Z. and Jiang, Z.-P., “Learning-based balance control of wheel-legged robots,” IEEE Robot. Automat. Lett. 6(4), 7667–7674 (2021).CrossRefGoogle Scholar
Chen, X., Ghadirzadeh, A., Folkesson, J., Björkman, M. and Jensfelt, P., “Deep Reinforcement Learning to Acquire Navigation Skills for Wheel-Legged Robots in Complex Environments,” In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE, 2018) pp. 3110–3116.CrossRefGoogle Scholar
Ba, K.-X., Song, Y.-H., Wang, C.-Y., Shi, Y.-P., Yu, B., Chen, X., Ma, G.-L. and Kong, X.-D., “A novel kinematics and statics correction algorithm of semi-cylindrical foot end structure for 3-DoF LHDS of legged robots,” Complex Intell. Syst. 108(4), 1–21 (2022).Google Scholar
Hwangbo, J., Lee, J., Dosovitskiy, A., Bellicoso, D., Tsounis, V., Koltun, V. and Hutter, M., “Learning agile and dynamic motor skills for legged robots,” Sci. Robot. 4(26), eaau5872 (2019).CrossRefGoogle ScholarPubMed
Lee, J., Hwangbo, J., Wellhausen, L., Koltun, V. and Hutter, M., “Learning quadrupedal locomotion over challenging terrain,” Sci. Robot. 5(47), eabc5986 (2020).CrossRefGoogle ScholarPubMed
Miki, T., Lee, J., Hwangbo, J., Wellhausen, L., Koltun, V. and Hutter, M., “Learning robust perceptive locomotion for quadrupedal robots in the wild,” Sci. Robot. 7(62), eabk2822 (2022).CrossRefGoogle ScholarPubMed
Ackerman, E., “A robot for the worst job in the warehouse: Boston dynamics’ stretch can move 800 heavy boxes per hour,” IEEE Spectr. 59(1), 50–51 (2022).CrossRefGoogle Scholar
Klemm, V., Morra, A., Salzmann, C., Tschopp, F., Bodie, K., Gulich, L., Küng, N., Mannhart, D., Pfister, C., Vierneisel, M., Weber, F., Deuber, R. and Siegwart, R, “Ascento: A Two-Wheeled Jumping Robot,” In: 2019 International Conference on Robotics and Automation (ICRA) (IEEE, 2019) pp. 7515–7521.CrossRefGoogle Scholar