基于改进鲸鱼优化算法的外骨骼机器人步态检测

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。

Exoskeleton robot gait detection based on improved whale optimization algorithm

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.