Abstract
Locomotion stability is essential for controlling quadruped robots and adapting them to unstructured terrain. We propose a control strategy with center-of-mass (CoM) dynamic planning for the stable locomotion of these robots. The motion trajectories of the swing legs are synchronized with the CoM of the robot. To implement the synchronous control scheme, we adjusted the swing legs to form a support triangle. The strategy is applicable to both static walk gait and dynamic trot gait. In the motion control processes of the robot legs, the distribution of the ground reaction forces is optimized to minimize joint torque and locomotion energy consumption. We also used an improved joint-torque controller with varied controller coefficients in the stance and swing phases. The simulation and experimental results demonstrate that the robot can complete omnidirectional locomotion in both walk and trot gaits. At a given locomotion speed, the stability margins for the robot during walking and trotting were 27.25% and 37.25% higher, respectively, than in the scheme without CoM planning. The control strategy with energy consumption optimization (ECO) reduced the energy consumption of the robot in walk and trot gaits by 11.25% and 13.83%, respectively, from those of the control scheme without ECO.
摘要
目的
运动稳定性对于四足机器人至关重要,是其适应非结构化地形的前提。为了提高机器人在运动过程中的机体稳定性,文本提出一种基于质心动态规划的四足机器人稳定控制策略。
创新点
1. 在期望速度一定的情况下,同时考虑机器人运动的稳定性和能耗两个问题;2. 考虑到机器人机身与各条腿之间的运动协调性问题,设计质心移动与摆动相动作的同步配合方案,并对质心进行实时轨迹规划。
方法
1. 为了实现同步控制方案,用摆动腿和支撑腿共同构成支撑三角形,并在静步态基础上对小跑步态做出扩展;2. 结合机器人腿在站立和摆动阶段受力情况的不同,设计主力矩由优化的足端反力映射和关节比例微分控制器组成的变权重控制策略。
结论
1. 仿真和实验结果表明,采用本文提出的控制策略,机器人可以完成行走和小跑两种步态的全向运动;2. 在一定的运动速度下,机器人行走和小跑的稳定裕度分别比未进行质心规划的方案提高了27.25%和37.25%;3. 与未进行能耗优化控制的方案相比,采用所提策略的机器人的能耗分别降低了11.25%(行走)和13.83%(小跑)。
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References
Arena P, Patanè L, Sueri P, et al., 2021. A data-driven neural network model predictive steering controller for a bio-inspired quadruped robot. IFAC-PapersOnLine, 54(17):93–98. https://doi.org/10.1016/j.ifacol.2021.11.031
Boaventura T, Semini C, Buchli J, et al., 2012. Dynamic torque control of a hydraulic quadruped robot. Proceedings of the IEEE International Conference on Robotics and Automation, p.1889–1894. https://doi.org/10.1109/ICRA.2012.6224628
Chignoli M, Wensing PM, 2020. Variational-based optimal control of underactuated balancing for dynamic quadrupeds. IEEE Access, 8:49785–49797. https://doi.org/10.1109/ACCESS.2020.2980446
di Carlo J, Wensing PM, Katz B, et al., 2018. Dynamic locomotion in the MIT Cheetah 3 through convex model-predictive control. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, p.1–9. https://doi.org/10.1109/IROS.2018.8594448
Ding YR, Pandala A, Li CZ, et al., 2021. Representation-free model predictive control for dynamic motions in quadrupeds. IEEE Transactions on Robotics, 37(4):1154–1171. https://doi.org/10.1109/TRO.2020.3046415
Dudzik T, Chignoli M, Bledt G, et al., 2020. Robust autonomous navigation of a small-scale quadruped robot in real-world environments. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, p.3664–3671. https://doi.org/10.1109/IROS45743.2020.9340701
Focchi M, del Prete A, Havoutis I, et al., 2017. High-slope terrain locomotion for torque-controlled quadruped robots. Autonomous Robots, 41(1):259–272. https://doi.org/10.1007/s10514-016-9573-1
Fukui T, Fujisawa H, Otaka K, et al., 2019. Autonomous gait transition and galloping over unperceived obstacles of a quadruped robot with CPG modulated by vestibular feedback. Robotics and Autonomous Systems, 111:1–19. https://doi.org/10.1016/j.robot.2018.10.002
Gonzalez de Santos P, Jimenez MA, Armada MA, 1998. Dynamic effects in statically stable walking machines. Journal of Intelligent and Robotic Systems, 23(1):71–85. https://doi.org/10.1023/A:1007993923530
Gonzalez C, Barasuol V, Frigerio M, et al., 2020. Line walking and balancing for legged robots with point feet. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, p.3649–3656. https://doi.org/10.1109/IROS45743.2020.9341743
Hao Q, Wang ZB, Wang JZ, et al., 2020. Stability-guaranteed and high terrain adaptability static gait for quadruped robots. Sensors, 20(17):4911. https://doi.org/10.3390/s20174911
Hutter M, Gehring C, Jud D, et al., 2016. ANYmal–a highly mobile and dynamic quadrupedal robot. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, p.38–44. https://doi.org/10.1109/IROS.2016.7758092
Hutter M, Gehring C, Lauber A, et al., 2017. ANYmal–toward legged robots for harsh environments. Advanced Robotics, 31(17):918–931. https://doi.org/10.1080/01691864.2017.1378591
Hyun DJ, Seok S, Lee J, et al., 2014. High speed trot-running: implementation of a hierarchical controller using proprioceptive impedance control on the MIT Cheetah. The International Journal of Robotics Research, 33(11):1417–1445. https://doi.org/10.1177/0278364914532150
Lee C, An D, 2021. Reinforcement learning and neural network-based artificial intelligence control algorithm for self-balancing quadruped robot. Journal of Mechanical Science and Technology, 35(1):307–322. https://doi.org/10.1007/s12206-020-1230-0
Lin PC, Komsuoglu H, Koditschek DE, 2005. A leg configuration measurement system for full-body pose estimates in a hexapod robot. IEEE Transactions on Robotics, 21(3):411–422. https://doi.org/10.1109/TRO.2004.840898
Liu LQ, Zhang CR, 2020. Dynamic properties of VDP-CPG model in rhythmic movement with delay. Mathematical Biosciences and Engineering, 17(4):3190–3202. https://doi.org/10.3934/mbe.2020181
McClain EW, Meek S, 2018. Determining optimal gait parameters for a statically stable walking human assistive quadruped robot. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, p.1751–1756. https://doi.org/10.1109/IROS.2018.8593979
Park HW, Wensing PM, Kim S, 2017. High-speed bounding with the MIT Cheetah 2: control design and experiments. The International Journal of Robotics Research, 36(2):167–192. https://doi.org/10.1177/0278364917694244
Pepe G, Laurenza M, Belfiore NP, et al., 2021. Quadrupedal robots’ gaits identification via contact forces optimization. Applied Sciences, 11(5):2102. https://doi.org/10.3390/app11052102
Raibert MH, 1986. Legged Robots That Balance. MIT Press, Cambridge, USA, p.44–56.
Shao YC, Jin YB, Liu XW, et al., 2022. Learning free gait transition for quadruped robots via phase-guided controller. IEEE Robotics and Automation Letters, 7(2):1230–1237. https://doi.org/10.1109/LRA.2021.3136645
Srinivas T, Madhusudhan AKK, Manohar L, et al., 2021. Valkyrie-design and development of gaits for quadruped robot using particle swarm optimization. Applied Sciences, 11(16):7458. https://doi.org/10.3390/app11167458
Tian J, Ma C, Wei C, et al., 2019. A smooth gait planning framework for quadruped robot based on virtual model control. Proceedings of the 12th International Conference on Intelligent Robotics and Applications, p.398–410. https://doi.org/10.1007/978-3-030-27538-9_34
Wang YQ, Ye LQ, Wang XQ, et al., 2020. A static gait generation for quadruped robots with optimized walking speed. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, p.1899–1906. https://doi.org/10.1109/SMC42975.2020.9282997
Yeom H, Bae J, 2021. A dynamic gait stabilization algorithm for quadrupedal locomotion through contact time modulation. Nonlinear Dynamics, 104(3):2275–2289. https://doi.org/10.1007/s11071-021-06376-5
Zhang ML, Zhang YJ, He XL, et al., 2021. Adaptive pid control and its application based on a double-layer BP neural network. Processes, 9(8):1475. https://doi.org/10.3390/pr9081475
Zhang SS, Liu M, Yin YF, et al., 2019. Static gait planning method for quadruped robot walking on unknown rough terrain. IEEE Access, 7:177651–177660. https://doi.org/10.1109/ACCESS.2019.2958320
Zhang Y, Wang H, Ding Y, et al., 2021. Adaptive walking control for a quadruped robot on irregular terrain using the complex-valued CPG network. Symmetry, 13(11):2090. https://doi.org/10.3390/sym13112090
Zhou LL, Li TF, Liu ZY, et al., 2021. An efficient gait-generating method for electrical quadruped robot based on humanoid power planning approach. Journal of Bionic Engineering, 18(6):1463–1474. https://doi.org/10.1007/s42235-021-00089-6
Acknowledgments
This work is supported by the National Natural Science Foundation of China (Nos. 52175050 and 52205059), the Outstanding Youth Science Foundation (No. 51922093), the Scientific Research Fund of Zhejiang Provincial Education Department (No. Y202148352), and the Graduate Innovation Special Fund Project of Jiangxi Province (No. YC2021-B031), China.
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Yangyang HAN designed the research. Liyu GAO and Huaizhi ZONG processed the corresponding data. Yangyang HAN and Zhenyu LU wrote the first draft of the manuscript. Feifei ZHONG helped to organize the manuscript. Guoping LIU and Junhui ZHANG revised and edited the final version.
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Yangyang HAN, Guoping LIU, Zhenyu LU, Huaizhi ZONG, Junhui ZHANG, Feifei ZHONG, and Liyu GAO declare that they have no conflict of interest.
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Han, Y., Liu, G., Lu, Z. et al. A stability locomotion-control strategy for quadruped robots with center-of-mass dynamic planning. J. Zhejiang Univ. Sci. A 24, 516–530 (2023). https://doi.org/10.1631/jzus.A2200310
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DOI: https://doi.org/10.1631/jzus.A2200310
Key words
- Center-of-mass (CoM) planning
- Quadruped robot
- Cooperative scheme
- Ground reaction forces
- Stability margin