Force-based Active Compliance Control of Hydraulic Quadruped Robot
Keywords:Hydraulic quadruped robot, compliance control, multi-rigid body dynamics, hybrid dynamics, co-simulation
The hydraulically driven quadruped robot has received extensive attention from many scholars due to its high power density and adaptability to unstructured terrain. However, the research on hydraulic quadruped robots based on torque control is not mature enough, especially in the aspect of multi-rigid body dynamics. In this paper, the most commonly used gait trot is selected as the research object. First, the multi-rigid motion equation of the quadruped robot is established by the spin recursion method based on Lie groups. Next, the Lagrange multiplier is used to represent the constraint force to establish the 12-degree-of-freedom inverse dynamics model of the quadruped robot’s stance phase. And the hybrid dynamics method is used to reduce the dimension of the inversion matrix, which simplifies the solution process of the dynamics model. Then, the trajectory of the foot is planned. Through the analysis of the simplified model, it is concluded that the gait cycle and the initial position of the stance phase are important factors affecting the stability of the trot gait. Finally, the controller framework of the quadruped robot is introduced, and the effectiveness of the algorithm designed in this paper is verified through the co-simulation of the trot gait. The co-simulation results show that the inverse dynamics algorithm can be used as the feedforward of the control system, which can greatly reduce the gains of the PD controller; the robot has good compliance and can achieve stable trotting.
Raibert M, Blankespoor K, Nelson G, et al. “Bigdog, the rough-terrain quadruped robot,” Proceedings of the 17th World Congress of the International. Seoul, Korea, 2008, pp. 10822–10825.
Wooden D, Malchano M, Blankespoor K, et al. “Autonomous Navigation for BigDog,” International Conference on Robotics and Automation. Anchorage, USA, 2010, pp. 4736–4741.
Hogan N, “Impedance control: An approach to manipulation: Part I – Theory,” ASME Journal of Dynamic Systems, Measurement, and control. 1985, 107, pp. 1–7.
Burdet E, Osu R, Franklin DW, et al. “The central nervous system stabilizes unstable dynamics by learning optimal impedance,” Nature. 2001, 414(6862), pp. 446–449.
Selen L, Franklin D and Wolpert D, “Impedance control reduces instability that arises from motor noise,” Journal of Neuroscience. 2009, 29, pp. 12606.
Geyer H and Herr H, “A muscle-reflex model that encodes principles of legged mechanics produces human walking dynamics and muscle activities,” IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2010, 18, pp. 263–273.
Gehring C, Bellicoso C D, Coros S, et al. “Dynamic trotting on slopes for quadrupedal robots,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Hamburg, Germany, 2015.
Gehring C, Coros S, Hutter M, et al. “Towards Automatic Discovery of Agile Gaits for Quadrupedal Robots,” IEEE International Conference on Robotics & Automation. Hong Kong, China, 2014.
Boaventura T, Medrano-Cerda GA, Semini C, et al. “Stability and performance of the compliance controller of the quadruped robot hyq,” Proceedings of IEEE/RSJ international conference on intelligent robots and systems (IROS). Tokyo, Japan, 2013, pp. 1458–1464.
Claudio S, Victor B, Thiago B, et al. “Towards versatile legged robots through active impedance control,” The International Journal of Robotics Research. 2015, 34(7), pp. 1003–1020.
Havoutis I, Semini C, Buchli J, et al. “Quadrupedal trotting with active compliance,” IEEE International Conference on Mechatronics (ICM). Vicenza, Italy, 2013.
Christian G, Stelian C, Marco H, et al. “An Optimization-Based Approach to Controlling Agile Motions for a Quadruped Robot,” IEEE Robotics & Automation. 2016, 23, pp. 34–43.
Qingjun Y, Rui Z, Zhenguo N, et al. “Natural Frequency Analysis of Hydraulic Quadruped Robot and Structural Optimization of the Leg,” ASME Journal of Dynamic Systems, Measurement, and control. 2020, 142(1), pp. 011009.
R Featherstone, “Rigid Body Dynamics Algorithms,” Springer, 2008.
K.M. Lynch and F.C. Park, “Modern Robotics: Mechanics, Planning, and Control,” Cambridge University Press, 2017.
Michael M, Jonas B and Stefan S. “Inverse Dynamics Control of Floating Base Systems Using Orthogonal Decomposition,” IEEE International Conference on Robotics and Automation. Anchorage, Alaska, USA, 2010.
Barasuol V, Buchli J and Semini C. “A reactive controller framework for quadrupedal locomotion on challenging terrain,” Proc. IEEE Conf. on Robotics and Automation. Karlsruhe, Germany, 2013, pp. 2539–2546.
Rutishauser S, Sprowitz A and Righetti L. “Passive compliant quadruped robot using central pattern generators for locomotion control,” Proc. IEEE Conf. on Biomedical Robotics and Biomechatronics. Scottadale, US, 2008, pp. 710–715.
Maufroy C, Kimura H and Takase K. “Integration of posture and rythmic motion controls in quadrupedal dynamic walking using phase modulations based on leg loading/unloading,” Autonomous Robots, 2010, 28(3), pp. 331–353.
Barkan U, Loannis H and Claudio S. “Dynamic trot-walking with the hydraulic quadruped robot – HyQ: Analytical trajectory generation and active compliance control,” 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. Tokyo, Japan, 2014.