Study On The Adaptive Gait Control Method Of Flat And Slope Of Lower Limb Assisted Exoskeleton Robot

The lower extremity assisted exoskeleton,as a medical device to help paraplegic patients recover,is designed to help disabled people complete their daily lives independently.However,the gait plans between different scenes are independent of each other,and the adaptation range of terrain is limited.This paper proposes a gait control method that can determine the stride strategy according to the current posture of the wearer for the switching of the lower extremity walking exoskeleton flat slopes and different slopes,and then complete the continuous walking from flat to slope,suitable for different slope conditions.The research contents of this article are as follows:First,analyze the gait behavior characteristics of normal people and give definitions of gait-related indicators.Model the exoskeleton forward and inverse kinematics for the conversion of joint space and end space trajectories.Plan the walking gait of the exoskeleton robot for flat ground and inclined slopes with different slopes,and verify the wear test.Secondly,the current exoskeleton uses a finite state machine to switch between different scenes,the way from the previous scene to the next scene is manually selected,and there is no correlation between the gait of each scene.In this paper,through the analysis of sensor data on flat ground and different inclined slopes,the features are selected to determine the input,and the gait swing period is divided as the output,and the basic gait parameters are fitted by the neural network method.A short-term wearing exoskeleton sensor data and flat slope gait are used to fit the next planning point.Achieve adaptive gait control for flat ground and different slopes.Furthermore,the gait control method fitted by the neural network has great limitations,that is,the determination of the neural network model determines the shape of the gait,and also determines the step length and pace.In short,the learned gait cannot be changed;Similarly,for the Dynamic Movement Primitive(DMP)frame,the input gait curve is just a reference.In response to the above problems,the DMP is extended,and the sensor parameters are used to fit the gait control items in the DMP,so that the reference gait can be adjusted according to the neural network input sensor data.At the same time,by changing the time and space terms in the control frame,the generated gait is adjusted twice to better adapt to different slopes,and analyze the different situations in the transition stage to verify the effectiveness of the model.Finally,indicators for the availability of slope gait are introduced to evaluate the generated gait.Finally,this article introduces the exoskeleton platform.Perform pre-experiment to collect sensory information at the desired gait,and use it to model the gait.Then the simulation and actual platform verification are carried out for the method proposed in this paper.The evaluation and analysis of the experimental results prove the adaptability of this method to any slope within 0-20 degrees.