基于改进鲸鱼优化算法的外骨骼机器人步态检测
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摘要
针对传统的外骨骼机器人步态检测算法中的信息单一化、准确率低、易陷入局部最优等问题,提出基于改进鲸鱼算法优化的支持向量机(IWOA-SVM)的外骨骼机器人步态检测算法,即在鲸鱼优化算法(WOA)中引入遗传算法(GA)的选择、交叉、变异操作,进而去优化支持向量机(SVM)的惩罚因子与核参数,再使用参数优化后的SVM建立分类模型,从而扩大算法的搜索范围,减小算法陷入局部最优的概率。首先,使用混合传感技术采集步态数据,即通过足底压力传感器和膝关节、髋关节角度传感器采集外骨骼机器人的运动数据,并作为步态检测系统的输入;然后,使用门限法对步态相位进行划分并标记标签;最后,将足底压力信号与髋关节、膝关节角度信号融合作为输入,使用IWOA-SVM算法完成对步态的检测。对6个标准测试函数进行仿真实验,并与GA、粒子群优化(PSO)算法、WOA进行比较,数值实验表明,改进鲸鱼优化算法(IWOA)的鲁棒性、寻优精度、收敛速度均优于其他优化算法。通过分析不同穿戴者的步态检测结果发现,准确率可达98.8%,验证了所提算法在新一代外骨骼机器人中的可行性和实用性,并与基于遗传优化算法的支持向量机(GA-SVM)、基于粒子群优化算法的支持向量机(PSO-SVM)、基于鲸鱼优化算法的支持向量机(WOA-SVM)算法进行比较,结果表明,该算法识别准确率分别提高了5.33%、2.70%、1.44%,能够对外骨骼机器人的步态进行有效检测,进而实现外骨骼机器人的精确控制及稳定行走。
In order to solve problems in traditional gait detection algorithms, such as simplification of information, low accuracy, being easy to fall into local optimum, a gait detection algorithm for exoskeleton robot called Support Vector Machine optimized by Improved Whale Optimization Algorithm(IWOA-SVM) was proposed. The selection, crossover and mutation of Genetic Algorithm(GA) were introduced to Whale Optimization Algorithm(WOA) to optimize the penalty factor and kernel parameters of Support Vector Machine(SVM), and then classification models were established by SVM with optimized parameters, expanding the search scope and reduce the probability of falling into local optimum. Firstly, the gait data was collected by using hybrid sensing technology. With the combination of plantar pressure sensor, knee joint and hip joint angle sensors, motion data of exoskeleton robot was acquired as the input of gait detection system. Then, the gait phases were divided and tagged according to the threshold method. Finally, the plantar pressure signal was integrated with hip and knee angle signals as input, and gait detection was realized by IWOA-SVM algorithm. Through the simulation experiments of six standard test functions, the results demonstrate that Improved Whale Optimization Algorithm(IWOA) is superior to GA, Particle Swarm Optimization(PSO) algorithm and WOA in robustness, optimization accuracy and convergence speed. By analyzing the gait detection results of different wearers, the accuracy is up to 98.8%, so the feasibility and practicability of the proposed algorithm in the new generation exoskeleton robot are verified. Compared with Support Vector Machine optimized by Genetic Algorithm(GA-SVM), Support Vector Machine optimized by Particle Swarm Optimization(PSO-SVM) and Support Vector Machine optimized by Whale Optimization Algorithm(WOA-SVM), the proposed algorithm has the gait detection accuracy improved by 5.33%, 2.70% and 1.44% respectively. The experimental results show that the proposed algorithm can effectively detect the gait of exoskeleton robot and realize the precise control and stable walking of exoskeleton robot.
In order to solve problems in traditional gait detection algorithms, such as simplification of information, low accuracy, being easy to fall into local optimum, a gait detection algorithm for exoskeleton robot called Support Vector Machine optimized by Improved Whale Optimization Algorithm(IWOA-SVM) was proposed. The selection, crossover and mutation of Genetic Algorithm(GA) were introduced to Whale Optimization Algorithm(WOA) to optimize the penalty factor and kernel parameters of Support Vector Machine(SVM), and then classification models were established by SVM with optimized parameters, expanding the search scope and reduce the probability of falling into local optimum. Firstly, the gait data was collected by using hybrid sensing technology. With the combination of plantar pressure sensor, knee joint and hip joint angle sensors, motion data of exoskeleton robot was acquired as the input of gait detection system. Then, the gait phases were divided and tagged according to the threshold method. Finally, the plantar pressure signal was integrated with hip and knee angle signals as input, and gait detection was realized by IWOA-SVM algorithm. Through the simulation experiments of six standard test functions, the results demonstrate that Improved Whale Optimization Algorithm(IWOA) is superior to GA, Particle Swarm Optimization(PSO) algorithm and WOA in robustness, optimization accuracy and convergence speed. By analyzing the gait detection results of different wearers, the accuracy is up to 98.8%, so the feasibility and practicability of the proposed algorithm in the new generation exoskeleton robot are verified. Compared with Support Vector Machine optimized by Genetic Algorithm(GA-SVM), Support Vector Machine optimized by Particle Swarm Optimization(PSO-SVM) and Support Vector Machine optimized by Whale Optimization Algorithm(WOA-SVM), the proposed algorithm has the gait detection accuracy improved by 5.33%, 2.70% and 1.44% respectively. The experimental results show that the proposed algorithm can effectively detect the gait of exoskeleton robot and realize the precise control and stable walking of exoskeleton robot.
引文
[1] 龙亿.下肢外骨骼人体运动预测与人机协调控制技术研究[D].哈尔滨:哈尔滨工业大学,2017:1-2.(LONG Y.Human motion prediction and human-robot coordination control for lower extremity exoskeleton[D].Harbin:Harbin Institute of Technology,2017:1-2.)
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[7] LIU X H,ZHOU Z H,MAI J G,et al.Multi-class SVM based real-time recognition of sit-to-stand and stand-to-sit transitions for a bionic knee exoskeleton in transparent model[C]// Proceedings of the 2017 International Conference on Intelligent Robotics and Applications.Berlin:Springer,2017:262-272.
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[9] WANG Y B,XIONG R,WANG J N,et al.Multi-class assembly parts recognition using composite feature and random forest for robot programming by demonstration[C]// Proceedings of the 2015 IEEE Conference on Robotics and Biomimetics.Piscataway,NJ:IEEE,2015:698-703.
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[11] 陈建华,奚如如,王兴松,等.外骨骼机器人的非结构地面行走步态分类算法[J].机器人,2017,39(4):505-513.(CHEN J H,XI R R,WANG X S,et al.Walking gait classification algorithm for exoskeleton robot on unstructured ground[J].Robot,2017,39(4):505-513.)
[12] MIRJALILI S,LEWIS A.The whale optimization algorithm[J].Advances in Engineering Software,2016,95:51-67.
[13] ZENG M R,XI L,XIAO A M.The free step length ant colony algorithm in mobile robot path planning [J].Advanced Robotics,2016,30(23):1509-1514.
[14] MIRJALILI S,MIRJALILI S M,LEWIS A.Grey wolf optimizer [J].Advances in Engineering Software,2014,69:46-61.
[15] 李航.统计学习方法[M].北京:清华大学出版社,2018:95-130.(LI H.Statistical Learning Method [M].Beijing:Tsinghua University Press,2018:95-130.)
[16] LONG Y,DU Z J,WANG W D,et al.PSO-SVM-based online locomotion mode identification for rehabilitation robotic exoskeletons [J].Sensors,2016,16(9):1408.
[17] 周志华.机器学习[M].北京:清华大学出版社,2016:121-138.(ZHOU Z H.Machine Learning [M].Beijing:Tsinghua University Press,2016:121-138.)
[18] WU G Z,WANG C,WU X Y,et al.Gait phase prediction for lower limb exoskeleton robots[C]// Proceedings of the 2016 IEEE International Conference on Information and Automation.Piscataway,NJ:IEEE,2016:19-24.
[19] 钟明辉,龙文.一种随机调整控制参数的鲸鱼优化算法[J].科学技术与工程,2017,17(12):68-73.(ZHONG M H,LONG W.Whale optimization algorithm based on stochastic adjustment control parameter[J].Science Technology and Engineering,2017,17(12):68-73.)
[20] WATKINS W A,SCHEVILL W E.Aerial observation of feeding behavior in four baleen whales:Eubalaena glacialis,Balaenoptera borealis,Megaptera novaeangliae,and Balaenoptera physalus [J].Journal of Mammalogy,1979,60(1):155-163.
[21] 潘勇,郭晓东.一种基于遗传算法改进的粒子群优化算法[J].计算机应用与软件,2011,28(9):222-224.(PAN Y,GUO X D.An improved particle swarm optimization algorithm based on genetic algorithm [J].Computer Applications and Software,2011,28(9):222-224.)
[2] 欧阳小平,范伯骞,丁硕.助力型下肢外骨骼机器人现状及展望[J].科技导报,2015,33(23):92-99.(OUYANG X P,FAN B Q,DING S.Status and prospects of the lower extremity exoskeleton robots for human power augmentation[J].Science and Technology Review,2015,33(23):92-99.)
[3] 宋遒志,王晓光,王鑫,等.多关节外骨骼助力机器人发展现状及关键技术分析[J].兵工学报,2016,37(1):172-185.(SONG Q Z,WANG X G,WANG X,et al.Development of multi-joint exoskeleton-assisted robot and its key technology analysis:an overview[J].Acta Armamentarii,2016,37(1):172-185.)
[4] YANG C J,NIU B,CHEN Y.Adaptive neuro-fuzzy control based development of a wearable exoskeleton leg for human walking power augmentation[C]// Proceedings of the 2005 IEEE/ASME International Conference on Advanced Intelligent Mechatronics Monterey.Piscataway,NJ:IEEE,2005:467-472.
[5] 陈春杰.基于柔性传动的助力全身外骨骼机器人系统研究[D].北京:中国科学院大学,2017:57-63.(CHEN C J.Research on power-assisted full-body exoskeleton robotic system based on flexible drive[D].Beijing:University of Chinese Academy of Sciences,2017:57-63.)
[6] 赵丽娜,刘作军,苟斌,等.基于隐马尔可夫模型的动力型下肢假肢步态预识别[J].机器人,2014,36(3):337-341.(ZHAO L N,LIU Z J,GOU B,et al.Gait pre-recognition of dynamic lower limb prosthesis based on hidden Markov model[J].Robot,2014,36(3):337-341.)
[7] LIU X H,ZHOU Z H,MAI J G,et al.Multi-class SVM based real-time recognition of sit-to-stand and stand-to-sit transitions for a bionic knee exoskeleton in transparent model[C]// Proceedings of the 2017 International Conference on Intelligent Robotics and Applications.Berlin:Springer,2017:262-272.
[8] GUO Q,JIANG D.Method for walking gait identification in a lower extremity exoskeleton based on C4.5 decision tree algorithm [J].International Journal of Advance Robotic Systems,2015,12(4):1-11.
[9] WANG Y B,XIONG R,WANG J N,et al.Multi-class assembly parts recognition using composite feature and random forest for robot programming by demonstration[C]// Proceedings of the 2015 IEEE Conference on Robotics and Biomimetics.Piscataway,NJ:IEEE,2015:698-703.
[10] BAGLEY J D.The behavior of adaptive systems which employ genetic and correlation algorithms[D].Ann Arbor,MI:University of Michigan,1967.
[11] 陈建华,奚如如,王兴松,等.外骨骼机器人的非结构地面行走步态分类算法[J].机器人,2017,39(4):505-513.(CHEN J H,XI R R,WANG X S,et al.Walking gait classification algorithm for exoskeleton robot on unstructured ground[J].Robot,2017,39(4):505-513.)
[12] MIRJALILI S,LEWIS A.The whale optimization algorithm[J].Advances in Engineering Software,2016,95:51-67.
[13] ZENG M R,XI L,XIAO A M.The free step length ant colony algorithm in mobile robot path planning [J].Advanced Robotics,2016,30(23):1509-1514.
[14] MIRJALILI S,MIRJALILI S M,LEWIS A.Grey wolf optimizer [J].Advances in Engineering Software,2014,69:46-61.
[15] 李航.统计学习方法[M].北京:清华大学出版社,2018:95-130.(LI H.Statistical Learning Method [M].Beijing:Tsinghua University Press,2018:95-130.)
[16] LONG Y,DU Z J,WANG W D,et al.PSO-SVM-based online locomotion mode identification for rehabilitation robotic exoskeletons [J].Sensors,2016,16(9):1408.
[17] 周志华.机器学习[M].北京:清华大学出版社,2016:121-138.(ZHOU Z H.Machine Learning [M].Beijing:Tsinghua University Press,2016:121-138.)
[18] WU G Z,WANG C,WU X Y,et al.Gait phase prediction for lower limb exoskeleton robots[C]// Proceedings of the 2016 IEEE International Conference on Information and Automation.Piscataway,NJ:IEEE,2016:19-24.
[19] 钟明辉,龙文.一种随机调整控制参数的鲸鱼优化算法[J].科学技术与工程,2017,17(12):68-73.(ZHONG M H,LONG W.Whale optimization algorithm based on stochastic adjustment control parameter[J].Science Technology and Engineering,2017,17(12):68-73.)
[20] WATKINS W A,SCHEVILL W E.Aerial observation of feeding behavior in four baleen whales:Eubalaena glacialis,Balaenoptera borealis,Megaptera novaeangliae,and Balaenoptera physalus [J].Journal of Mammalogy,1979,60(1):155-163.
[21] 潘勇,郭晓东.一种基于遗传算法改进的粒子群优化算法[J].计算机应用与软件,2011,28(9):222-224.(PAN Y,GUO X D.An improved particle swarm optimization algorithm based on genetic algorithm [J].Computer Applications and Software,2011,28(9):222-224.)