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.

基于质心动态规划的四足机器人稳定运动控制策略

[1]ArenaP, PatanèL, SueriP, et al., 2021. A data-driven neural network model predictive steering controller for a bio-inspired quadruped robot. IFAC-PapersOnLine, 54(17):93-98.

[2]BoaventuraT, SeminiC, BuchliJ, et al., 2012. Dynamic torque control of a hydraulic quadruped robot. Proceedings of the IEEE International Conference on Robotics and Automation, p.1889-1894.

[3]ChignoliM, WensingPM, 2020. Variational-based optimal control of underactuated balancing for dynamic quadrupeds. IEEE Access, 8:49785-49797.

[4]di CarloJ, WensingPM, KatzB, 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.

[5]DingYR, PandalaA, LiCZ, et al., 2021. Representation-free model predictive control for dynamic motions in quadrupeds. IEEE Transactions on Robotics, 37(4):1154-1171.

[6]DudzikT, ChignoliM, BledtG, 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.

[7]FocchiM, del PreteA, HavoutisI, et al., 2017. High-slope terrain locomotion for torque-controlled quadruped robots. Autonomous Robots, 41(1):259-272.

[8]FukuiT, FujisawaH, OtakaK, 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.

[9]Gonzalez de SantosP, JimenezMA, ArmadaMA, 1998. Dynamic effects in statically stable walking machines. Journal of Intelligent and Robotic Systems, 23(1):71-85.

[10]GonzalezC, BarasuolV, FrigerioM, 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.

[11]HaoQ, WangZB, WangJZ, et al., 2020. Stability-guaranteed and high terrain adaptability static gait for quadruped robots. Sensors, 20(17):4911.

[12]HutterM, GehringC, JudD, 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.

[13]HutterM, GehringC, LauberA, et al., 2017. ANYmal–toward legged robots for harsh environments. Advanced Robotics, 31(17):918-931.

[14]HyunDJ, SeokS, LeeJ, 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.

[15]LeeC, AnD, 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.

[16]LinPC, KomsuogluH, KoditschekDE, 2005. A leg configuration measurement system for full-body pose estimates in a hexapod robot. IEEE Transactions on Robotics, 21(3):411-422.

[17]LiuLQ, ZhangCR, 2020. Dynamic properties of VDP-CPG model in rhythmic movement with delay. Mathematical Biosciences and Engineering, 17(4):3190-3202.

[18]McClainEW, MeekS, 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.

[19]ParkHW, WensingPM, KimS, 2017. High-speed bounding with the MIT Cheetah 2: control design and experiments. The International Journal of Robotics Research, 36(2):167-192.

[20]PepeG, LaurenzaM, BelfioreNP, et al., 2021. Quadrupedal robots’ gaits identification via contact forces optimization. Applied Sciences, 11(5):2102.

[21]RaibertMH, 1986. Legged Robots That Balance. MIT Press, Cambridge, USA, p.44-56.

[22]ShaoYC, JinYB, LiuXW, et al., 2022. Learning free gait transition for quadruped robots via phase-guided controller. IEEE Robotics and Automation Letters, 7(2):1230-1237.

[23]SrinivasT, MadhusudhanAKK, ManoharL, et al., 2021. Valkyrie-design and development of gaits for quadruped robot using particle swarm optimization. Applied Sciences, 11(16):7458.

[24]TianJ, MaC, WeiC, 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.

[25]WangYQ, YeLQ, WangXQ, 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.

[26]YeomH, BaeJ, 2021. A dynamic gait stabilization algorithm for quadrupedal locomotion through contact time modulation. Nonlinear Dynamics, 104(3):2275-2289.

[27]ZhangML, ZhangYJ, HeXL, et al., 2021. Adaptive pid control and its application based on a double-layer BP neural network. Processes, 9(8):1475.

[28]ZhangSS, LiuM, YinYF, et al., 2019. Static gait planning method for quadruped robot walking on unknown rough terrain. IEEE Access, 7:177651-177660.

[29]ZhangY, WangH, DingY, et al., 2021. Adaptive walking control for a quadruped robot on irregular terrain using the complex-valued CPG network. Symmetry, 13(11):2090.

[30]ZhouLL, LiTF, LiuZY, 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.