Abstract：Walking control is the key issue to motion control of humanoid robots. To achieve fast and stable gait, a humanoid robot spiral model algorithm based on the covariance matrix adaptation evolution strategy(CMA-ES) is proposed in this paper. In the walking optimization process, the optimization task is divided into three sub tasks, the parameters are selected according to the optimization goal to join the corresponding optimization group, and the CMA-ES optimizer is constructed. Each CMA-ES algorithm optimizer is designed according to different learning objectives. Based on the optimization results of the previous optimization group, the spiral iterative optimization is combined with new requirements, and finally the established learning objectives are achieved to obtain the optimal parameter values. The proposed algorithm is applied in the HfutEngine simulation 3D team. The relevant gait test data of the robot show the validity of the proposed algorithm.
[1]XIAO H Z, LI Z J, YANG C G, et al.Robust Stabilization of a Wheeled Mobile Robot Using Model Predictive Control Based on Neurodynamics Optimization. IEEE Transactions on Industrial Electronics, 2017, 64(1): 505-516. [2]WERMELINGER M, FANKHAUSER P, DIETHELM R, et al. Navigation Planning for Legged Robots in Challenging Terrain // Proc of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Washington, USA: IEEE, 2016: 1184-1189. [3]URAKUBO T, MONNO M, MAEKAWA S, et al.Dynamic Mode-ling]and Controller Design for a Spherical Rolling Robot Equipped with a Gyro. IEEE Transactions on Control Systems Technology, 2016, 24(5): 1669-1679. [4]FALLON M F, MARION P, DEITS R, et al.Continuous Humanoid Locomotion over Uneven Terrain Using Stereo Fusion // Proc of the 15th IEEE-RAS International Conference on Humanoid Robots. Washington, USA: IEEE, 2015: 881-888. [5]KUBO R, OHNISHI K. Mechanical Recognition of Unknown Environment Using Active/Passive Contact Motion. IEEE Transactions on Industrial Electronics, 2009, 56(5): 1364-1374. [6]HOPKINS M A, GRIFFIN R J, LEONESSA A, et al.Design of a Compliant Bipedal Walking Controller for the DARPA Robotics Challenge // Proc of the 15th IEEE-RAS International Conference on Humanoid Robots. Washington, USA: IEEE, 2015: 831-837. [7]PLESTAN F, GRIZZLE J W, WESTERVELT E R, et al. Stable Walking of a 7-DOF Biped Robot. IEEE Transactions on Robotics and Automation, 2003, 19(4): 653-668. [8]LUO D S, QIU J W, WANG Y, et al.A New Method on Foot Rolling Trajectory Generation for Human-Like Bipedal Walking // Proc of the IEEE International Conference on Mechatronics and Automation. Washington, USA: IEEE, 2013: 837-842. [9]HONG Y D, PARK C S, KIM J H. Human-Like Stable Bipedal Walking with a Large Stride by the Height Variation of the Center of Mass Using an Evolutionary Optimized Central Pattern Generator // Proc of the 37th Annual Conference of the IEEE Industrial Electronics Society. Washington, USA: IEEE, 2011: 177-182. [10]MORO F L, TSAGARAKIS N G, CALDWELL D G. A Human-Like Walking for the COmpliant HuMANoid COMAN Based on CoM Trajectory Reconstruction from Kinematic Motion Primitives // Proc of the 11th IEEE-RAS International Conference on Humanoid Robots. Washington, USA: IEEE, 2011: 364-370. [11]TLALOLINI D, CHEVALLEREAU C, AOUSTIN Y. Human-Like Walking: Optimal Motion of a Bipedal Robot with Toe-Rotation Motion. IEEE/ASME Transactions on Mechatronics, 2011, 16(2): 310-320. [12]MIURA K, MORISAWA M, KANEHIRO F, et al.Human-Like Walking with TOE Supporting for Humanoids // Proc of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Washington, USA: IEEE, 2011: 4428-4435. [13]GALDEANO D, CHEMORI A, KRUT S, et al.Optimal Pattern Generator for Dynamic Walking in Humanoid Robotics // Proc of the 10th International Multi-conference on Systems, Signals and Devices. Washington, USA: IEEE, 2013. DOI: 10.1109/SSD.2013.6564107. [14]MACALPINE P, BARRETT S, URIELI D, et al. Design and Optimization of an Omnidirectional Humanoid Walk: A Winning Approach]at the RoboCup 2011 3D Simulation Competition[C/OL]. [2018-05-25]. https://cse.iitk.ac.in/users/cs365/2013/hw2/macalpine-stone-12_omnidirectional-humanoid-robocup-winner.pdf. [15]WANG M, LI X, DONG H F, et al. A PSO-Based CPG Model Parameter Optimization Method for Biomimetic Robotic Fish // Proc of the Chinese Automation Congress. Washington, USA: IEEE, 2017: 3445-3449. [16]HANSEN N. The CMA Evolution Strategy: A Tutorial[EB/OL]. [2018-05-25]. https://arxiv.org/pdf/1604.00772.pdf. [17]HANSEN N. The CMA Evolution Strategy: A Comparing Review // LOZANO J A, LARRAN~AGA P, INZA I, et al., eds. Towards a New Evolutionary Computation. Berlin, Germany: Springer, 2006: 75-102. [18]URIELI D, MACALPINE P, KALYANAKRISHNAN S, et al. On Optimizing Interdependent Skills: A Case Study in Simulated 3D Humanoid Robot Soccer // Proc of the 10th International Conference on Autonomous Agents and Multiagent Systems. Richland, USA: IFAAMS, 2011: 769-776. [19]MACALPINE P, STONE P. Overlapping Layered Learning. Artificial Intelligence, 2018, 254: 21-43. [20]薛建. 基于CMA-ES算法的足球仿人机器人步态研究与实现.硕士学位论文.合肥:合肥工业大学, 2014. (XUE J. Gait Research and Implementation of Soccer Humanoid Robot Based on CMA-ES Algorithm. Master Dissertation. Hefei, China: Hefei University of Technology, 2014.) [21]ZHOU Z X, ZHOU Y, WANG J W. Omnidirectional Walk Design of Humanoid Robots Using Layered Learning Method Based on CMA-ES // Proc of the IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference. Washington, USA: IEEE, 2017: 464-468.
2018, Vol. 31 
