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CN119036459A - Man-machine interaction control method and system for wearable flexible outer limb - Google Patents

Man-machine interaction control method and system for wearable flexible outer limb Download PDF

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Publication number
CN119036459A
CN119036459A CN202411384632.2A CN202411384632A CN119036459A CN 119036459 A CN119036459 A CN 119036459A CN 202411384632 A CN202411384632 A CN 202411384632A CN 119036459 A CN119036459 A CN 119036459A
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China
Prior art keywords
flexible external
limb
joint
human
interaction control
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Inventor
蒋天裕
刘丹丹
任倩
李圣节
王娜
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Second Medical Center of PLA General Hospital
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Second Medical Center of PLA General Hospital
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Priority to CN202411384632.2A priority Critical patent/CN119036459A/en
Publication of CN119036459A publication Critical patent/CN119036459A/en
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1602—Programme controls characterised by the control system, structure, architecture
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/0006—Exoskeletons, i.e. resembling a human figure
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1615—Programme controls characterised by special kind of manipulator, e.g. planar, scara, gantry, cantilever, space, closed chain, passive/active joints and tendon driven manipulators

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  • Engineering & Computer Science (AREA)
  • Robotics (AREA)
  • Mechanical Engineering (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Orthopedic Medicine & Surgery (AREA)
  • Automation & Control Theory (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)

Abstract

The invention provides a man-machine interaction control method and system for a wearable flexible outer limb, which are used for carrying out data processing based on Kalman filtering through action data captured in real time by an inertial sensor and a Hall sensor to obtain accurate pose of the flexible outer limb, combining the obtained accurate pose of the flexible outer limb with target action data based on symmetry of a human kinematic chain to adjust the flexible outer limb, and integrating various sensor technologies and the Kalman filtering technology.

Description

Man-machine interaction control method and system for wearable flexible outer limb
Technical Field
The invention belongs to the technical field of man-machine interaction control, and particularly relates to a man-machine interaction control method and system for a wearable flexible outer limb.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The flexible outer limb robot has the characteristics of high flexibility, high safety and the like, and is widely applied to assisting people in completing tasks such as detection, grabbing and carrying, rehabilitation and medical treatment and the like.
Symmetrical task tasks can ensure coordination and synchronization of flexible outer limbs in complex operating environments. The current method for realizing symmetrical operation has various limitations, such as insufficient sensor precision, data processing delay, poor environmental adaptability and the like.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides the man-machine interaction control method and system for the wearable flexible outer limb, which are integrated with various sensor technologies, perform data processing and integration updating by using Kalman filtering, have the advantages of high precision, quick response, good environmental adaptability and the like, and can realize the accurate symmetrical operation of the flexible outer limb.
To achieve the above object, a first aspect of the present invention provides a human-computer interaction control method of a wearable flexible outer limb, the method comprising:
acquiring a measured value when the outer limb performs a task through an inertial sensor arranged at a joint of the flexible outer limb;
defining a Kalman filtering state vector, and obtaining a state transition matrix based on the current measured value of the inertial sensor and the predicted value of the next moment;
establishing an observation equation of Kalman filtering, and obtaining a state covariance matrix of Kalman filtering prediction based on the state transition matrix;
Updating a state covariance matrix and a state vector based on observed values of an inertial sensor and a Hall sensor arranged at the joint of the flexible outer limb to obtain the joint pose of the flexible outer limb;
And taking the obtained joint pose of the flexible outer limb as feedback data, and adjusting the pose of the flexible outer limb by combining the feedback data with target motion data obtained based on the symmetry of an ergonomic chain.
A second aspect of the invention provides a human-machine interaction control system for a wearable flexible outer limb, the system comprising:
the acquisition module is used for acquiring a measured value when the outer limb executes a task through an inertial sensor arranged at the joint of the flexible outer limb;
the computing module is used for defining a Kalman filtering state vector and obtaining a state transition matrix based on the current measured value of the inertial sensor and the predicted value of the next moment;
The prediction module is used for establishing an observation equation of Kalman filtering and obtaining a state covariance matrix predicted by the Kalman filtering based on the state transition matrix;
The updating module is used for updating the state covariance matrix and the state vector based on the observed values of the inertial sensor and the Hall sensor arranged at the joint of the flexible outer limb to obtain the joint pose of the flexible outer limb;
And the adjusting module is used for taking the obtained joint pose of the flexible outer limb as feedback data, and adjusting the pose of the flexible outer limb by combining the obtained joint pose of the flexible outer limb with target action data obtained based on the symmetry of the human kinematic chain.
A third aspect of the invention provides a computer device comprising a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory in communication via the bus when the computer device is in operation, the machine-readable instructions when executed by the processor performing a method of human-machine interaction control of a wearable flexible outer limb.
A fourth aspect of the invention provides a computer readable storage medium having a computer program stored thereon, which when executed by a processor performs a method of human-machine interaction control of a wearable flexible outer limb.
The one or more of the above technical solutions have the following beneficial effects:
According to the invention, the motion data captured in real time by the inertial sensor and the Hall sensor are processed based on Kalman filtering to obtain accurate pose of the flexible outer limb, the obtained accurate pose of the flexible outer limb is combined with the target motion data based on symmetry of the human kinematic chain to adjust the flexible outer limb, and the flexible outer limb automatic control device integrates various sensor technologies and Kalman filtering technologies, has the advantages of high precision, quick response, good environmental adaptability and the like, and can realize accurate symmetrical operation of the flexible outer limb.
Additional aspects of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is a flowchart of a man-machine interaction control method according to a first embodiment of the invention;
FIG. 2 is a flow chart of the pose processing according to the first embodiment of the present invention in combination with sensor data;
fig. 3 is a flowchart of a particle swarm optimization algorithm according to an embodiment of the invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention.
Embodiments of the invention and features of the embodiments may be combined with each other without conflict.
Example 1
The embodiment discloses a man-machine interaction control method of a wearable flexible outer limb, comprising the following steps:
acquiring a measured value when the outer limb performs a task through an inertial sensor arranged at a joint of the flexible outer limb;
defining a Kalman filtering state vector, and obtaining a state transition matrix based on the current measured value of the inertial sensor and the predicted value of the next moment;
establishing an observation equation of Kalman filtering, and obtaining a state covariance matrix of Kalman filtering prediction based on the state transition matrix;
Updating a state covariance matrix and a state vector based on observed values of an inertial sensor and a Hall sensor arranged at the joint of the flexible outer limb to obtain the joint pose of the flexible outer limb;
And taking the obtained joint pose of the flexible outer limb as feedback data, and adjusting the pose of the flexible outer limb by combining the feedback data with target motion data obtained based on the symmetry of an ergonomic chain.
The following describes in detail a human-computer interaction control method for a wearable flexible outer limb according to the embodiment with reference to fig. 1, which specifically includes:
S1, arranging a sensor at a limb joint of a wearer needing a work task.
The step S1 comprises the following steps:
S1.1, mounting an IMU and a Hall sensor at the joint of a wearer.
The IMU is used for acquiring acceleration, angular velocity and magnetic field direction data of the flexible outer limb, and the Hall sensor is used for acquiring absolute angle data of the flexible outer limb.
S2, capturing action data of limbs in real time, and processing the data to ensure stability and accuracy of the data.
As shown in fig. 2, S2 includes the steps of:
And S2.1, carrying out initial calibration on the IMU to eliminate zero offset errors and scaling errors. The hall sensor is calibrated under a known magnetic field to determine the relationship between the magnetic field strength and direction and the sensor output.
S2.2, performing initial pose estimation, estimating a pitch angle, a roll angle and an azimuth angle, and calculating the angular change of the pose through numerical integration.
And S2.3, reading sensor data in real time, and performing more accurate data fusion by using a Kalman filter to provide more accurate pose estimation.
In the pose estimation of the flexible outer limb, a state vector X k is first defined, including information such as joint angle, angular velocity, and acceleration.
The state transfer equation describes the dynamic change of the system, and the change of angle can be obtained based on the angular velocity measurement of the IMU. By integrating the angular velocity, the angle at the next moment is obtained:
X k-1 is state estimation obtained by the result of the Kalman filter at the last moment, and combines the data of the IMU and the Hall sensor; Is a predicted state vector, a is a state transition matrix used to predict the state of the system next step, B is a control input matrix, U k is a control input, set 0;W k in this application is process noise, representing uncertainty in the state model.
The observation equation, which describes the relationship of the measurements taken from the sensors to the system state, can be written as:
Zk＝HXk+Vk
Wherein Z k is the observed data of the combined IMU and Hall sensor, H is the observed matrix, the state vector is mapped to the measured value, and V k is the measured noise, which represents the measured error of the sensor.
Kalman filtering is divided into two main steps, prediction and updating.
Predicting the state at the current time from the previous state and the sensor data using a state transition equation:
Wherein, Is the predicted state covariance, Q is the process noise covariance matrix, representing the uncertainty of the prediction.
The updating step combines the actual measurements of the hall sensor and IMU to correct the predicted state, preventing drift in long-term operation.
The kalman gain K k is used to weigh the predicted and measured values.
Where R is the measurement noise covariance matrix, representing the uncertainty of the sensor measurements.
Combining the predicted state with the actual measured data to update the current state estimate:
And updating covariance:
And S3, based on a symmetry principle, mapping the motion data of the wearer into symmetrical motion data of the flexible outer limb.
As shown in fig. 3, S3 includes the steps of:
S3.1, according to the symmetry of the human kinematic chain, taking the human body midline of a wearer as a symmetry axis, an axis which is vertically downward from the top of the head to the pelvis is generally selected, and the axis divides the human body into a left part and a right part. The axis is used for defining the joint action mapping relation on the left side and the right side so as to ensure that the generated symmetrical action accords with the natural symmetrical movement characteristic of a human body.
And S3.2, mirror-turning the joint data of the wearer based on the symmetry axis, namely mapping the data of the left joint to the right joint and vice versa, and generating complete symmetrical motion data by mapping in the mode.
And S3.3, selecting a proper optimization algorithm to optimize the action of the mapping flexible outer limb. For example, by reducing abrupt changes in the angular change of the joint, limiting abrupt changes in angular velocity, etc., the joint is made smoother and more natural, and thus, uncoordinated or stiff movements are avoided.
Taking a particle swarm optimization algorithm as an example:
initializing, namely, representing a combination of motion data by each particle, determining the initial position of the particle by mapping to generate symmetrical motion data, and representing the speed of motion change by the speed of the particle.
The fitness function is defined by measuring smoothness and naturalness by calculating joint angle changes, angular velocity changes, acceleration changes between successive time steps. At times t and t+1, the mapped joint angles are θ t and θ t+1, the mapped joint angular velocities are ω t and ω t+1, and the mapped joint accelerations are a t and a t+1, then:
The above parts are weighted and combined into a comprehensive fitness function:
f=w1fangle+w2fv+w3fa
Where w 1,w2,w3 is a different weight coefficient.
The fitness function is used to evaluate the solution for good or bad, and the better the fitness value, the closer the solution represented by the particle is to the optimal solution, and directs the search direction and whether a sufficiently good solution has been found.
Updating the particle velocity:
Wherein: is the velocity of particle i at the kth generation, w is the inertial weight, controls the exploratory ability of the particle, c 1 and c 2 are learning factors for balancing individual experience and population experience, r 1 and r 2 are random numbers to ensure diversity; Is the historical optimal position of particle i, g best is the global optimal position in the population, calculated by the fitness function.
If the current fitness value of the particle is better than the historical optimal value, the historical optimal position is updated, and the particle position is updated:
Repeated fitness evaluation and continuous updating And g best, if the fitness function converges or reaches the preset iteration times, generating action data to be executed by the optimized flexible outer limb according to the obtained global optimal solution g best.
And S4, transmitting the mapped symmetrical action data to a control system of the flexible outer limb, detecting the joint pose of the flexible outer limb in real time as feedback, and driving the flexible outer limb to execute corresponding symmetrical action.
And S4.1, transmitting the optimized symmetrical action data to a control system of the flexible outer limb.
And S4.2, driving the flexible outer limb to reach the target position and posture, and executing corresponding symmetrical actions.
And S4.3, reading the IMU and Hall sensor data installed in the flexible outer limb joint in real time, and estimating the actual pose according to the step in S2.
And S4.4, taking the fused actual pose estimation result as feedback data, and continuously adjusting the pose of the flexible outer limb by combining the feedback data with the symmetrical motion data optimized in the S3 so as to realize accurate symmetrical operation.
Example two
An object of the present embodiment is to provide a human-computer interaction control system of a wearable flexible outer limb, including:
the acquisition module is used for acquiring a measured value when the outer limb executes a task through an inertial sensor arranged at the joint of the flexible outer limb;
the computing module is used for defining a Kalman filtering state vector and obtaining a state transition matrix based on the current measured value of the inertial sensor and the predicted value of the next moment;
The prediction module is used for establishing an observation equation of Kalman filtering and obtaining a state covariance matrix predicted by the Kalman filtering based on the state transition matrix;
The updating module is used for updating the state covariance matrix and the state vector based on the observed values of the inertial sensor and the Hall sensor arranged at the joint of the flexible outer limb to obtain the joint pose of the flexible outer limb;
And the adjusting module is used for taking the obtained joint pose of the flexible outer limb as feedback data, and adjusting the pose of the flexible outer limb by combining the obtained joint pose of the flexible outer limb with target action data obtained based on the symmetry of the human kinematic chain.
Example III
It is an object of the present embodiment to provide a computing device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, which processor implements the steps of the method described above when executing the program.
Example IV
An object of the present embodiment is to provide a computer-readable storage medium.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the above method.
The steps involved in the devices of the second, third and fourth embodiments correspond to those of the first embodiment of the method, and the detailed description of the embodiments can be found in the related description section of the first embodiment. The term "computer-readable storage medium" shall be taken to include a single medium or multiple media that includes one or more sets of instructions, and shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the processor and that cause the processor to perform any one of the methodologies of the present invention.
It will be appreciated by those skilled in the art that the modules or steps of the invention described above may be implemented by general-purpose computer means, alternatively they may be implemented by program code executable by computing means, whereby they may be stored in storage means for execution by computing means, or they may be made into individual integrated circuit modules separately, or a plurality of modules or steps in them may be made into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
While the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it is intended to cover all modifications or variations within the scope of the invention as defined by the claims of the present invention.

Claims (10)

1.一种可穿戴柔性外肢体的人机交互控制方法，其特征在于，所述方法包括：1. A human-computer interaction control method for a wearable flexible external limb, characterized in that the method comprises: 通过设置在柔性外肢体关节处的惯性传感器，获取外肢体执行任务时的测量值；By means of inertial sensors arranged at the joints of the flexible external limb, the measurement values of the external limb when performing the task are obtained; 定义卡尔曼滤波的状态向量，基于惯性传感器的当前测量值和下一时刻的预测值，得到状态转移矩阵；Define the state vector of the Kalman filter, and obtain the state transfer matrix based on the current measurement value of the inertial sensor and the predicted value at the next moment; 建立卡尔曼滤波的观测方程，基于所述状态转移矩阵，得到卡尔曼滤波预测的状态协方差矩阵；Establishing the observation equation of Kalman filter, and obtaining the state covariance matrix predicted by Kalman filter based on the state transfer matrix; 基于设置在柔性外肢体关节处的惯性传感器和霍尔传感器的观测值，更新状态协方差矩阵和状态向量，得到柔性外肢体的关节位姿；Based on the observation values of the inertial sensors and Hall sensors set at the joints of the flexible external limbs, the state covariance matrix and the state vector are updated to obtain the joint pose of the flexible external limbs; 将所得到的柔性外肢体的关节位姿作为反馈数据，与基于人体运动学链的对称性所得到的目标动作数据相结合，调整柔性外肢体的位姿。The obtained joint posture of the flexible external limb is used as feedback data and combined with the target motion data obtained based on the symmetry of the human kinematic chain to adjust the posture of the flexible external limb. 2.如权利要求1所述的一种可穿戴柔性外肢体的人机交互控制方法，其特征在于，根据惯性传感器的角速度测量值，基于积分角速度，预测得到下一时刻的角度。2. A human-computer interaction control method for a wearable flexible external limb as described in claim 1, characterized in that the angle at the next moment is predicted based on the integrated angular velocity according to the angular velocity measurement value of the inertial sensor. 3.如权利要求1所述的一种可穿戴柔性外肢体的人机交互控制方法，其特征在于，将采集的关节数据，基于运动学链的对称性生成对称肢体的完整运动数据，对所生成的完整运动数据基于粒子群优化算法进行优化，得到目标动作数据。3. A human-computer interaction control method for a wearable flexible external limb as described in claim 1, characterized in that the collected joint data is used to generate complete motion data of the symmetrical limb based on the symmetry of the kinematic chain, and the generated complete motion data is optimized based on a particle swarm optimization algorithm to obtain target motion data. 4.如权利要求3所述的一种可穿戴柔性外肢体的人机交互控制方法，其特征在于，采用粒子群优化算法对生成的完整运动数据进行优化，具体为：4. A human-computer interaction control method for a wearable flexible external limb as claimed in claim 3, characterized in that the generated complete motion data is optimized using a particle swarm optimization algorithm, specifically: 以每个粒子表示一个动作数据的组合，粒子的初始位置以映射生成对称运动数据来确定，粒子的速度则表示动作变化的快慢；Each particle represents a combination of action data. The initial position of the particle is determined by mapping the generated symmetric motion data, and the speed of the particle represents the speed of the action change. 以连续时间步长之间的关节角度变化、角速度变化、加速度变化来定义适应度函数；The fitness function is defined by the change in joint angle, angular velocity, and acceleration between consecutive time steps; 更新粒子速度和位置；Update particle velocity and position; 重复适应度评估并不断更新粒子历史最优位置和群体中的全局最优位置，直至适应度函数收敛或达到预设迭代次数，得到全局最优解；Repeat the fitness evaluation and continuously update the historical optimal position of the particle and the global optimal position in the group until the fitness function converges or reaches the preset number of iterations to obtain the global optimal solution; 根据所得到的全局最优解，生成优化后的柔性外肢体的目标动作数据。According to the obtained global optimal solution, the target motion data of the optimized flexible external limb is generated. 5.如权利要求4所述的一种可穿戴柔性外肢体的人机交互控制方法，其特征在于，所述适应度函数为：5. A human-computer interaction control method for a wearable flexible external limb as claimed in claim 4, characterized in that the fitness function is: f＝w1fangle+w2fv+w3fa，f＝w 1 f angle +w 2 f v +w 3 f a , 其中，在时间t和t+1时刻，映射的关节角度为θt和θt+1，映射的关节角速度为ωt和ωt+1，映射的关节加速度为at和at+1，w1,w2,w3是不同的权重系数。Among them, at time t and t+1, the mapped joint angles are θ t and θ t+1 , the mapped joint angular velocities are ω t and ω t+1 , the mapped joint accelerations are a t and a t+1 , and w 1 , w 2 , and w 3 are different weight coefficients. 6.一种可穿戴柔性外肢体的人机交互控制系统，其特征在于，所述系统包括：6. A wearable flexible external limb human-computer interaction control system, characterized in that the system comprises: 获取模块，用于通过设置在柔性外肢体关节处的惯性传感器，获取外肢体执行任务时的测量值；An acquisition module, used for acquiring measurement values of the external limb when performing a task through inertial sensors arranged at the joints of the flexible external limb; 计算模块，用于定义卡尔曼滤波的状态向量，基于惯性传感器的当前测量值和下一时刻的预测值，得到状态转移矩阵；The calculation module is used to define the state vector of the Kalman filter and obtain the state transfer matrix based on the current measurement value of the inertial sensor and the predicted value at the next moment; 预测模块，用于建立卡尔曼滤波的观测方程，基于所述状态转移矩阵，得到卡尔曼滤波预测的状态协方差矩阵；A prediction module, used to establish an observation equation of a Kalman filter, and obtain a state covariance matrix predicted by the Kalman filter based on the state transfer matrix; 更新模块，用于基于设置在柔性外肢体关节处的惯性传感器和霍尔传感器的观测值，更新状态协方差矩阵和状态向量，得到柔性外肢体的关节位姿；An updating module is used to update the state covariance matrix and the state vector based on the observation values of the inertial sensor and the Hall sensor arranged at the joint of the flexible external limb to obtain the joint posture of the flexible external limb; 调整模块，用于将所得到的柔性外肢体的关节位姿作为反馈数据，与基于人体运动学链的对称性所得到的目标动作数据相结合，调整柔性外肢体的位姿。The adjustment module is used to use the obtained joint posture of the flexible external limb as feedback data, combined with the target action data obtained based on the symmetry of the human kinematic chain, to adjust the posture of the flexible external limb. 7.如权利要求6所述的一种可穿戴柔性外肢体的人机交互控制系统，其特征在于，还包括优化模块，所述优化模块用于将采集的关节数据，基于运动学链的对称性生成对称肢体的完整运动数据，对所生成的完整运动数据基于优化算法进行优化，得到目标动作数据。7. A wearable flexible external limb human-computer interaction control system as described in claim 6, characterized in that it also includes an optimization module, which is used to generate complete motion data of the symmetrical limb based on the symmetry of the kinematic chain from the collected joint data, and optimize the generated complete motion data based on the optimization algorithm to obtain target motion data. 8.如权利要求6所述的一种可穿戴柔性外肢体的人机交互控制系统，其特征在于，在所述计算模块中，根据惯性传感器的角速度测量值，基于积分角速度，预测得到下一时刻的角度。8. A human-computer interaction control system for a wearable flexible external limb as described in claim 6, characterized in that, in the calculation module, the angle at the next moment is predicted based on the integrated angular velocity according to the angular velocity measurement value of the inertial sensor. 9.一种计算机设备，其特征在于，包括：处理器、存储器和总线，所述存储器存储有所述处理器可执行的机器可读指令，当计算机设备运行时，所述处理器与所述存储器之间通过总线通信，所述机器可读指令被所述处理器执行时执行如权利要求1至5任一项所述的一种可穿戴柔性外肢体的人机交互控制方法。9. A computer device, characterized in that it comprises: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, when the computer device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, a human-computer interaction control method for a wearable flexible external limb as described in any one of claims 1 to 5 is executed. 10.一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储有计算机程序，所述计算机程序被处理器运行时执行如权利要求1至5任一项所述的一种可穿戴柔性外肢体的人机交互控制方法。10. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the human-computer interaction control method of a wearable flexible external limb as described in any one of claims 1 to 5 is executed.
CN202411384632.2A 2024-09-30 2024-09-30 Man-machine interaction control method and system for wearable flexible outer limb Pending CN119036459A (en)

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