摘要
神经网络等传统的机器学习方法是基于样本数目无穷大的经验风险最小化原则,这对非确定环境下有限样本的步态学习控制非常不利.针对两足机器人面临的非确定环境适应性难题,提出了一种基于支持向量机(SVM)的两足机器人步态控制方法,解决了小样本条件下的步态学习控制问题.提出了一种基于混合核的步态回归方法,仿真研究表明了这种方法比全局核和局部核分别单独用于步态学习时有优越性.SVM以踝关节及髋关节的轨迹作为输入,相应的满足ZMP判据的上体轨迹作为输出,利用有限的理想步态样本对机器人上体轨迹与腿部轨迹之间的动态运动关系进行学习,然后将训练好的SVM置入机器人控制系统,从而增强了步态控制的鲁棒性,有利于实现两足机器人在非结构环境下的稳定步行.仿真结果表明了所提方法的优越性.
Conventional machine learning methods such as neural network（NN） use empirical risk minimization（ERM） based on infinite samples, which is disadvantageous to the gait learning control based on small sample sizes for biped robots walking in unstructured, uncertain and dynamic environments. To deal with the stable walking control problem in the dynamic environments for biped robots, we put forward a method of gait control based on support-vector-machines（SVM）, which provides a solution for the learning control issue based on small sample sizes. A support vector machine regression（SVR） method for gait control with mixed kernel functions is proposed, and the proposed method shows superior performance when compared with SVR with radial basis function（RBF） kernels or polynomial kernels, respectively. Using ankle trajectory and hip trajectory as inputs, and the corresponding trunk trajectory which guarantees the ZMP criterion as outputs, the SVM is trained based on small sample sizes to learn the dynamic kinematic relationships between the legs and the trunk of the biped robot. Then the trained SVM is incorporated into the control system of the robots. Robustness of the gait control is enhanced, which is advantageous to realizing stable biped walking in unstructured environments. Simulation results demonstrate the superiority of the proposed methods.
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2011年第8期1133-1139,1144,共8页
Control Theory & Applications
基金
国家自然科学基金资助项目(U0735003
60974047)
广东省自然科学基金资助项目(8351009001000002
9151009001000011)
广东省科技计划资助项目(2009B010900051)
教育部霍英东青年教师基金资助项目(121061)
广东省高等学校高层次人才项目资助项目
关键词
两足机器人
步态
小样本
学习控制
支持向量机
biped robots
gait
small sample sizes
learning control
support-vector-machines（SVM）
