A method and system for human motion compensation in wearable exoskeleton robots

Abstract

This invention relates to the field of wearable exoskeleton robot technology, and specifically proposes a method and system for human motion compensation in wearable exoskeleton robots. By fusing multi-sensor information through Kalman filtering, the state of the human-exoskeleton system is observed in real time to obtain the influence of human motion on the exoskeleton robot. Based on this, an artificial potential field is constructed for the exoskeleton, and the trajectory of the exoskeleton robot's end effector is adjusted in real time to compensate for the influence of human motion on the preset trajectory of the exoskeleton robot, thereby achieving precise operation of the exoskeleton during operation. Compared with existing technologies, this invention has higher accuracy and stability, lower manufacturing costs, and stronger versatility.

Description

Technical Field

[0001] This invention relates to the field of wearable exoskeleton robot technology, and in particular proposes a method for human motion compensation for wearable exoskeleton robots. Background Technology

[0002] In recent years, the field of human-robot collaboration has developed rapidly. Exolimb robots, as an extension of collaborative robots and exoskeleton robots, have received widespread attention and become a research hotspot. Compared with traditional collaborative robots, exolimb robots have stronger autonomous mobility, overcoming the limitations of joint range of motion in exoskeleton robots, and providing the human body with movement and manipulation capabilities beyond those of traditional limbs. This new type of wearable assistive device, through the docking and integration of mechanical limbs with human limbs, not only improves the human body's activity, perception, and manipulation capabilities, but will also generate new human-robot work models in multiple fields.

[0003] In the industrial production sector, while traditional collaborative robots have improved efficiency, they still have limitations when handling complex tasks. Exoskeleton robots, on the other hand, can assist human arms in performing more complex tasks, such as opening doors under load or performing precision operations with hand tools. This reduces reliance on additional human workers, and in hazardous or polluted environments, exoskeleton robots can even replace human workers, enabling contactless and safe operations.

[0004] The application of exolimb robots has greatly improved work efficiency and flexibility. However, when performing tasks such as grasping and transferring objects, the preset trajectories of existing exolimbs are easily disturbed by human movement or physiological tremors, leading to task failure. Furthermore, such disturbances and movements are dynamic and unpredictable changes in the human-machine system state. Therefore, there is an urgent need for a method that can compensate for the impact of human movement on exolimb robots in real time, and consider obstacle avoidance during the compensation process to improve operational accuracy and safety. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a method and system for human motion compensation in wearable exolimb robots. By fusing data from an inertial measurement unit and a visual odometry system, the motion state of the human body and exolimbs is accurately estimated using a Kalman filter. Furthermore, the trajectory of the exolimb robot is planned in real time using an artificial potential field method, thereby effectively compensating for the interference of human motion on the trajectory of the exolimbs.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for human motion compensation in wearable exoskeleton robots includes the following steps:

[0008] Step 1: Collect relevant data during the operation of the exolimb robot in real time and convert it into a unified Cartesian space. The relevant data includes human acceleration information, position information and velocity information of the exolimb robot.

[0009] Step 2: Construct a Kalman filter and use the Kalman filter to process the acceleration, position and velocity information in Cartesian space to obtain the human body's estimated state and the end effector of the extremity robot's estimated state, namely the estimated acceleration, estimated position and estimated velocity in Cartesian space.

[0010] Step 3: Based on the estimated state of the human body and the estimated state of the extremity robot end effector obtained in Step 2, construct an artificial potential field for dynamic trajectory planning of the extremity robot; the artificial potential field includes a gravitational potential field, a repulsive potential field, a uniform flow potential field, and a spatial constraint potential field.

[0011] Step 4: Based on the human-external limb system state obtained in Step 2 and the artificial potential field constructed in Step 3, plan the safe path of the end effector of the extremity robot in real time, and use the velocity planning method to assign velocity information to the path points to realize the real-time trajectory planning of the end effector of the extremity robot.

[0012] Step 5: Based on the real-time trajectory of the end effector of the extremity robot, use the inverse kinematics of the extremity robot to calculate the desired angle and velocity of the extremity joint;

[0013] Step 6: Control the movements of each joint of the exolimb robot according to the desired angle and speed.

[0014] Furthermore, in step 1, an inertial measurement unit is used to collect and convert human acceleration information into Cartesian space, and a visual odometry is used to collect and convert the position and velocity information of the exolimb robot into Cartesian space.

[0015] Furthermore, step 2 uses a Kalman filter to process the position, velocity, and acceleration information in Cartesian space. The implementation method includes the following steps:

[0016] Step 2.1: Based on robot kinematics, construct the discrete state-space equation x of the human-external limb system using the geometric relationship between the human and the extremity robot. t+1 ;

[0017] x t+1 =Ax t +Bu t +w t

[0018] in, p h Indicates the position and state of the human body, v h Indicates the speed state of the human body. Indicates transpose; a m p represents the acceleration state of the inertial measurement unit. o Indicates the positional state of the visual odometry, v o A represents the velocity state of the visual odometry; A is the state transition matrix, which integrates the effects of the inertial measurement unit's acceleration and the visual odometry's position and velocity; B is the control matrix, reflecting the influence of the input vector on the system state; u t w is the system input vector. t It represents the process noise of the model, which follows a normal distribution with a mean of 0 and a covariance matrix of Q; A and B are defined by the following formulas:

[0019]

[0020] Where Δt is the time step and I3 is the identity matrix;

[0021] The u t Defined as:

[0022]

[0023] Where g is the acceleration due to gravity, f o,p (q) represents the position vector derived from the homogeneous transformation matrix from the extremum robot base coordinate system to the visual odometry coordinate system, derived from robot kinematics, where q is the joint angle of the extremum. This represents the Jacobian matrix that maps the joint velocities of an exolimb robot to the visual odometry linear velocities.

[0024] Constructing the discrete-time observation equation z of the human-external limb system t :

[0025] z t =Hx t +v t

[0026] Among them, z t The observation results are obtained by fusing data from two sensors, where H is the observation matrix and v t It is measurement noise, which follows a normal distribution with a mean of 0 and a covariance matrix of R;

[0027] Step 2.2: Based on the discrete state-space equation x of the human-external limb system t+1 and discrete-time observation equation z t Construct the Kalman filter, which includes the Kalman filter prediction equation and the Kalman filter update equation;

[0028] The Kalman filter prediction equation is as follows:

[0029] The Kalman filter update equation is:

[0030] in, This is the predicted value of the state, also known as the prior state estimate; K is the optimal estimate of the state, also known as the posterior state estimate; t P is the Kalman filter gain. t The error covariance matrix, For the predicted values ​​of the control variables, R is the observation covariance matrix and Q is the state covariance matrix;

[0031] Step 2.3: Using the Kalman filter, the acceleration, velocity, and position information in Cartesian space are processed to obtain more accurate acceleration information, as well as the velocity and position information of the end effector of the extremity robot.

[0032] 2.3.1 The prediction process is carried out using the Kalman filter prediction equation, and the update process is carried out using the Kalman filter update equation. The prediction equation and the update equation are alternately iterated to obtain the optimal state estimate of the human-external limb system, that is, the observed human velocity and position.

[0033] 2.3.2. Based on step 2.3.1, the optimal state estimate of the end effector of the extremity robot is calculated using the kinematic principles of the human-extremity system. This yields the optimal state estimate of the end effector, namely, the predicted velocity and predicted position of the end effector. The expression for the kinematic principles of the human-extremity system is as follows:

[0034]

[0035] in, For the estimated end effector position, f is the estimated human body position. e,p J is the position vector in the homogeneous transformation matrix from the human body to the end effector. e,v (q) represents the Jacobian matrix under the current external limb configuration. Indicates the velocity of external limb joints;

[0036] Step 2.4 Substitute the predicted position, predicted velocity, and predicted acceleration data obtained in step S2.3 into the Kalman filter to obtain the state of the human-external limb system.

[0037] Furthermore, in step 3, based on the estimated human body state and the estimated state of the extremity robot end effector obtained in step 2, the artificial potential field for dynamic trajectory planning of the extremity robot is constructed, including:

[0038] Gravitational potential field

[0039]

[0040] where λ d η, κ, and ξ are constants, the function C(x) represents the equipotential curve generated by the target g, i represents the index value of a point in space, and x i Let θ represent a point in space, and let θ represent the angle between the estimated velocity v of the end effector and the vector pointing from the current position x of the end effector to the target g.

[0041] Repulsive potential field

[0042]

[0043] in, R represents the repulsive force intensity of the i-th obstacle; i Represents the effective range of the repulsive force of the i-th obstacle; Heviside step function Only when the extremity of the extremity robot enters its radius of influence R i The repulsive potential field of the i-th obstacle is activated at a certain time, and the repulsive force is invalidated after the end of the extremity robot leaves the radius; distance l i This represents the shortest distance from the end effector of the extremity robot to the nearest point on the periphery of the i-th obstacle; The symbol representing the outer contour. Indicates taking O i Outer contour, O i This represents the geometry formed by the i-th obstacle;

[0044] Uniform flow potential field

[0045]

[0046] Where, |λ u | represents the magnitude of the uniform flow potential field intensity, and the unit vector d represents the direction of the potential field, which is usually specified using the direction cosine;

[0047] Spatial constrained potential field

[0048]

[0049] Where, λ c >0 represents the potential field strength, Ω represents the safe workspace, and SDF represents the distance from each location in the workspace to the boundary of Ω. If SDF Ω (x)<0, the spatial constraint gradient field guides the robot to move towards the center of the safe workspace.

[0050] Furthermore, the speed planning method used in step 4 to assign speed information to path points is as follows:

[0051]

[0052] Where, λ v This is a scaling factor for controlling the convergence speed of the system. This indicates the potential field between the current position x and the target position g. Geodesic distance on, v m To adjust the maximum threshold of speed, for the field The gradient.

[0053] A human motion compensation system for wearable exoskeleton robots, comprising:

[0054] An inertial measurement unit is used to collect human acceleration information in real time and convert it into a unified Cartesian space.

[0055] Visual odometry is used to collect the velocity and position information of exoskeleton robots in real time and convert it into a unified Cartesian space.

[0056] The data processing unit receives human acceleration information in Cartesian space, as well as the velocity and position information of the extremity robot. Based on the received human acceleration information, velocity and position information of the extremity robot, it calculates the desired angle and velocity of the extremity joints according to the human motion compensation method for wearable extremity robots described above, so as to control the movement of each joint of the extremity robot.

[0057] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the above-described method for human motion compensation for a wearable extremity robot.

[0058] A computer-readable storage medium storing a computer program, characterized in that, when executed by a processor, the computer program implements the steps of the above-described human motion compensation method for wearable exoskeleton robots.

[0059] By adopting the above technical solution, the present invention has the following advantages:

[0060] 1) The human motion compensation method and system for wearable exoskeleton robots provided by this invention can improve the operational accuracy of exoskeleton robots. During human-exoskeleton robot collaborative operation, Kalman filtering is used to fuse multi-sensor information, allowing real-time observation of the human-exoskeleton system state and the influence of human motion on the exoskeleton robot. Based on this, an artificial potential field for the exoskeleton is constructed, and the trajectory of the exoskeleton robot's end effector is adjusted in real time to compensate for the influence of human motion on the preset trajectory of the exoskeleton robot, thereby achieving precise operation of the exoskeleton during operation. Compared with existing technologies, this invention has higher accuracy and stability.

[0061] 2) In the process of obtaining trajectory planning information for the end effector of the extremity robot, the present invention uses a velocity planning method to assign velocity information to the path points, which can effectively prevent the end effector of the extremity robot from exceeding the workspace or reaching a singularity.

[0062] 3) The equipment used in this invention has low manufacturing cost, stable and reliable operation, good versatility and interchangeability, and can be applied to a variety of external limbs, making the invention applicable to a variety of external limb applications, including industrial, medical, and elderly care fields, with higher versatility. Attached Figure Description

[0063] Figure 1 This is a flowchart of a human motion compensation method for wearable exoskeleton robots, as described in the embodiment.

[0064] Figure 2 This is a schematic diagram of sensor installation for collecting relevant data in a human motion compensation method for wearable exolimb robots, where (a) is a visual odometer and (b) is an inertial measurement unit.

[0065] Figure 3 This is a schematic diagram of the experimental environment for Experiment 1 given in the embodiment;

[0066] Figure 4 This is a diagram showing the experimental results of Experiment 1 in Example 1; where a represents the distance the base of the exolimb robot moves along the X, Y, and Z coordinate axes when the exolimb robot moves in three-dimensional space; b represents the distance the end effector of the exolimb robot moves along the X, Y, and Z coordinate axes when the exolimb robot moves in three-dimensional space; c represents the deviation between the actual posture of the exolimb robot's end effector (represented by roll-pitch-yaw angle) and the desired posture we want it to achieve; d compares the distances the base and end effector of the exolimb robot move in space.

[0067] Figure 5 This is a schematic diagram of the experimental environment and experimental process for Experiment 2 of the Human Motion Compensation Method for Wearable Exolimb Robots, using ① to ④ to illustrate the motion trajectory and posture adjustment process of the exolimb robot. Detailed Implementation

[0068] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, 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 application pertains.

[0069] like Figure 1 As shown in the figure, this embodiment provides a method for human motion compensation for wearable exoskeleton robots, including the following steps:

[0070] Step 1: Acquire raw data collected by sensors installed on the human-external limb system during the operation of the exoskeleton robot. Specifically:

[0071] by Figure 2 Taking the human-external limb system shown as an example, in this human-external limb system, such as Figure 2 (a) and Figure 2 As shown in (b), the inertial measurement unit 1 and the visual odometer 2 are installed at the end effector of the extremity robot. The inertial measurement unit 1 is used to collect human acceleration information, and the visual odometer 2 is used to collect the position and velocity information of the extremity robot. The acceleration, velocity, and position information are unified into Cartesian space. This step is implemented using existing technology and will not be described in detail here.

[0072] Step 2: Construct a Kalman filter and use it to process the acceleration, velocity, and position information in Cartesian space.

[0073] Step 2.1: Based on robot kinematics, construct the discrete state-space equation x of the human-external limb system shown in formula (1) using the geometric relationship between the human and the extremity robot. t+1 ;

[0074] x t+1 =Ax t +Bu t +w t #(1)

[0075] in, p h Indicates the position and state of the human body, v h Indicates the speed state of the human body. Indicates transpose; a m p represents the acceleration state of the inertial measurement unit. o Indicates the positional state of the visual odometry, v oA represents the velocity state of the visual odometry; A is the state transition matrix, which integrates the effects of the inertial measurement unit's acceleration and the visual odometry's position and velocity; B is the control matrix, reflecting the influence of the input vector on the system state; u t w is the system input vector. t It represents the process noise of the model, which follows a normal distribution with a mean of 0 and a covariance matrix of Q; A and B are defined by the following formulas:

[0076]

[0077] Where Δt is the time step and I3 is the identity matrix;

[0078] The u t Defined as:

[0079]

[0080] Where g is the acceleration due to gravity, f o,p (q) represents the position vector derived from the homogeneous transformation matrix from the extremum robot base coordinate system to the visual odometry coordinate system, derived from robot kinematics, where q is the joint angle of the extremum. This represents the Jacobian matrix that maps the joint velocities of an exolimb robot to the visual odometry linear velocities.

[0081] Constructing the discrete-time observation equation z of the human-external limb system t :

[0082] z t =Hx t +v t #(5)

[0083] Among them, z t The observation results are obtained by fusing data from two sensors, where H is the observation matrix and v t It is measurement noise, which follows a normal distribution with a mean of 0 and a covariance matrix of R;

[0084] Step 2.2: Based on the discrete state-space equation x of the human-external limb system t+1 and discrete-time observation equation z t The Mann filter is constructed as follows:

[0085]

[0086] Wherein, formula (6) is the prediction process of the Kalman filter, It is the predicted value of the state, also known as the prior state estimate. The optimal estimate of the state is called the posterior state estimate. Equation (7) represents the Kalman filter update process, which improves the state estimate based on new measurements obtained from the sensor. Where is the Kalman filter gain, and is the error covariance matrix. t P is the Kalman filter gain. t Let be the error covariance matrix.

[0087] Step 2.3: Based on the Kalman filter, process the acceleration, velocity, and position information in Cartesian space as follows:

[0088] 2.3.1 The prediction process uses a Kalman filter to predict the equations, and the update process uses a Kalman filter to update the equations. The prediction and update processes are alternated and cyclical to obtain the optimal estimate of the system. The state estimation process begins with initializing the optimal state estimate and the error covariance matrix, with the initial conditions being: Then, the prediction and update processes iteratively alternate to obtain the optimal estimate of the system, denoted as... This represents the optimal state estimate of a person, i.e., the observed speed and position of the person.

[0089] 2.3.2. Based on step 2.3.1, the optimal state estimate of the end effector of the extremity robot is calculated using the kinematic principles of the human-extremity system. This yields the optimal state estimate of the end effector, namely, the predicted velocity and predicted position of the end effector. The expression for the kinematic principles of the human-extremity system is as follows:

[0090]

[0091] Step 2.4 Substitute the predicted acceleration data, predicted velocity and predicted position data obtained in step S2.3 into the Kalman filter to obtain the state of the human-external limb system.

[0092] Step 3: Based on the predicted human body state and the predicted state of the extremity robot end effector obtained in Step 2, construct the artificial potential field for dynamic trajectory planning of the extremity robot; the construction of the artificial potential field for dynamic trajectory planning of the extremity robot includes:

[0093] (1) Gravitational potential field, used to enable the exolimb robot to reach the target point, is expressed as shown in equation (9):

[0094]

[0095] where λ d η, κ, and ξ are constants, the function C(x) represents the equipotential curve generated by the target g, and θ represents the angle between the estimated velocity v of the end effector and the vector pointing from the current position x of the end effector to the target g;

[0096] The simplest equipotential body generated by the target point g in three-dimensional space is a concentric sphere C(x), which is obtained from formula (10):

[0097]

[0098] θ can be calculated using formula (11):

[0099]

[0100] The gravitational potential field in this embodiment takes into account the velocity factor, which is more in line with the dynamic characteristics of the robot.

[0101] (2) Repulsive potential field, used to enable the exolimb robot to avoid obstacles, is expressed as shown in equation (12):

[0102]

[0103] in, R represents the repulsive force intensity of the i-th obstacle; i The Heaviside step function represents the effective range of the repulsive force from the i-th obstacle. Only when the extremity of the extremity robot enters its radius of influence R i The repulsive potential field of the i-th obstacle is activated at a certain time, and the repulsive force is lost after the end of the extremity robot leaves this radius. Distance l i This represents the shortest distance from the end effector of the extremity robot to the nearest point on the periphery of the i-th obstacle, i.e. in The symbol representing the outer contour. Indicates taking O i Outer contour, O i This represents the geometry formed by the i-th obstacle;

[0104] (3) A uniform flow potential field enables the exolimb robot to converge to the target point faster and avoid obstacles more smoothly. Its expression is shown in equation (13):

[0105]

[0106] Where, |λ u | represents the magnitude of the uniform flow potential field intensity, and the unit vector d represents the direction of the potential field, which is usually specified using the direction cosine;

[0107] A spatially constrained potential field is used to keep the end effector of the extremity robot within a safe operating region without violating its kinematic constraints. The region of this spatially constrained potential field is defined as a subset of a convex hull with high maneuverability and the workspace of the extremity robot, as shown in Equation (14).

[0108]

[0109] Where, λ c >0 represents the potential field strength, Ω represents the safe workspace, and SDF represents the distance from each location in the workspace to the boundary of Ω. If SDF Ω (x)<0, the spatial constraint gradient field guides the robot to move towards the center of the safe workspace, thereby preventing the robot joints from exceeding their limits or the robot from reaching a singularity. Step 4: Based on the velocity information, position information, and target point position information of the end effector of the extremity robot obtained in Step 2, and the artificial potential field constructed in Step 3, the safe path of the end effector of the extremity robot is planned in real time. The velocity planning method is used to assign velocity information to the path points to realize the real-time trajectory planning of the end effector of the extremity robot.

[0110] In this embodiment, the speed information for path points is assigned using the speed planning method shown in formula (15):

[0111]

[0112] Where, λ v This is a scaling factor for controlling the convergence speed of the system. This indicates the potential field between the current position x and the target position g. Geodesic distance on, v m To adjust the maximum threshold of speed, for the field The gradient of the potential field is used to ensure that the velocity of the end effector of the extremum robot is modulated according to the normalized negative gradient of the potential field and the geodesic distance along the potential field, thereby making the movement of the extremum robot smoother and more controllable, while preventing the velocity from exceeding the maximum threshold.

[0113] Step 5: Based on the real-time trajectory of the end effector of the extremity robot, use the inverse kinematics of the extremity robot to calculate the desired angle and velocity of the extremity joint.

[0114] Step 6: Control the joints of the exolimb robot to move according to the desired angle and speed, and return to S1.

[0115] Simulation experiments were conducted on the above-mentioned human motion compensation method for wearable exolimb robots:

[0116] Experiment 1:

[0117] Verification was conducted using a simulation environment built in MuJoCo 210. A perturbation simulating the natural motion of a stationary human body was provided to the exoskeleton robot, simulating the impact of human motion on the robot. Throughout the process, the proposed human motion compensation method for exoskeletal robots was used to compensate for the influence of human motion on the robot. The experimental environment was as follows: Figure 3 As shown.

[0118] from Figure 4 a- Figure 4 As can be seen from d in the figure, the human motion compensation method for wearable extremity robots proposed in this embodiment significantly compensates for the impact of human motion on the extremity robot. When the human body disturbance is 0.5m, the position error of the extremity robot end is still less than 0.15m. This result ensures the stable operation of the human-extremity robot system, improves the working efficiency and accuracy of the extremity robot, and avoids the failure of extremity robot operations caused by human motion.

[0119] Verification was conducted in a simulation environment built on MuJoCo 210, using a human mannequin wearing an exoskeleton robot. The mannequin walked from a distance towards the workbench. When the target entered the exoskeleton robot's grasping range, the robot grasped the target object and moved with it. Throughout the grasping process, the exoskeleton robot remained in constant motion with the mannequin. The experimental environment was as follows: Figure 5 As shown.

[0120] Experiment 2:

[0121] Initially, the human model is positioned behind the workbench, with the exoskeleton robot worn around its waist in a grasping posture. The human model then moves towards the workbench until it reaches the front. Throughout this process, the wearable exoskeleton robot's motion compensation method, as proposed in this invention, is used to control and adjust the exoskeleton, enabling it to avoid obstacles in the environment and successfully grasp the target object while moving with the human body.

[0122] from Figure 5 It can be seen that the human motion compensation method for wearable exolimb robots proposed in this invention can compensate for the impact of human motion on exolimb robots in real time, and takes obstacle avoidance into account during the compensation process, which significantly improves the operating efficiency and safety of exolimb robots.

[0123] Based on the above-described human motion compensation method for wearable exoskeleton robots, this invention also provides a human motion compensation system for wearable exoskeleton robots, comprising:

[0124] An inertial measurement unit is used to collect human acceleration information in real time and convert it into a unified Cartesian space.

[0125] Visual odometry is used to collect the velocity and position information of exoskeleton robots in real time and convert it into a unified Cartesian space.

[0126] The data processing unit receives human acceleration information in Cartesian space, as well as the velocity and position information of the extremity robot. Based on the received human acceleration information, velocity and position information of the extremity robot, it calculates the desired angle and velocity of the extremity joints according to the human motion compensation method for wearable extremity robots described above, so as to control the movement of each joint of the extremity robot.

[0127] Based on the above-described method for human motion compensation in wearable exoskeleton robots, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for human motion compensation in wearable exoskeleton robots.

[0128] Based on the above-described method for human motion compensation in wearable exoskeleton robots, the present invention also provides a computer-readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the above-described method for human motion compensation in wearable exoskeleton robots.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for human motion compensation in wearable exoskeleton robots, characterized in that, Includes the following steps: Step 1: Collect relevant data during the operation of the exolimb robot in real time and convert it into a unified Cartesian space. The relevant data includes human acceleration information, position information and velocity information of the exolimb robot. Step 2: Construct a Kalman filter and use it to process the acceleration, velocity, and position information in Cartesian space to obtain the predicted state of the human body and the predicted state of the end effector of the extremity robot, i.e., the predicted position, predicted velocity, and predicted acceleration in Cartesian space. Substitute the predicted position, velocity, and acceleration into the Kalman filter to obtain the state of the human-extremity system. Step 3: Based on the estimated state of the human body and the estimated state of the end effector of the extremity robot obtained in Step 2, construct an artificial potential field for dynamic trajectory planning of the extremity robot; the artificial potential field includes a gravitational potential field, a repulsive potential field, a uniform flow potential field, and a spatial constraint potential field. Step 4: Based on the state of the human-external limb system obtained in Step 2 and the artificial potential field constructed in Step 3, plan the safe path of the end effector of the extremity robot in real time, and use the velocity planning method to assign velocity information to the path points to realize the real-time trajectory planning of the end effector of the extremity robot. Step 5: Based on the real-time trajectory of the end effector of the extremity robot, use the inverse kinematics of the extremity robot to calculate the desired angle and velocity of the extremity joint; Step 6: Control the movements of each joint of the exolimb robot according to the desired angle and speed.

2. The method for human motion compensation in a wearable exoskeleton robot according to claim 1, characterized in that: Step 1 uses an inertial measurement unit to collect and convert human acceleration information, and uses a visual odometry to collect and convert the velocity and position information of the exolimb robot.

3. A method for human motion compensation in a wearable exoskeleton robot according to claim 2, characterized in that, Step 2 uses a Kalman filter to process the position, velocity, and acceleration information in Cartesian space. The implementation method includes the following steps: Step 2.1: Based on robot kinematics, construct the discrete state-space equation x of the human-external limb system using the geometric relationship between the human and the extremity robot. t+1 ; x t+1 =Ax t +Bu t +w t in, p h Indicates the position and state of the human body, v h Indicates the speed state of the human body. Indicates transpose; a m p represents the acceleration state of the inertial measurement unit. o Indicates the positional state of the visual odometry, v o A represents the velocity state of the visual odometry; A is the state transition matrix, which integrates the effects of the inertial measurement unit's acceleration and the visual odometry's position and velocity; B is the control matrix, reflecting the influence of the input vector on the system state; u t w is the system input vector. t It represents the process noise of the model, which follows a normal distribution with a mean of 0 and a covariance matrix of Q; A and B are defined by the following formulas: Where Δt is the time step and I3 is the identity matrix; The u t Defined as: Where g is the acceleration due to gravity, f o，p (q) represents the position vector derived from the homogeneous transformation matrix from the extremum robot base coordinate system to the visual odometry coordinate system, derived from robot kinematics, where q is the joint angle of the extremum. This represents the Jacobian matrix that maps the joint velocities of an exolimb robot to the visual odometry linear velocities. Constructing the discrete-time observation equation z of the human-external limb system t : z t =Hx t +v t Among them, z t The observation results are obtained by fusing data from two sensors, where H is the observation matrix and v t It is measurement noise, which follows a normal distribution with a mean of 0 and a covariance matrix of R; Step 2.2: Based on the discrete state-space equation x of the human-external limb system t+1 and discrete-time observation equation z t Construct a Kalman filter, which includes the Kalman filter prediction equation and the Kalman filter update equation; The Kalman filter prediction equation is as follows: The Kalman filter update equation is: in, This is the predicted value of the state, also known as the prior state estimate. K is the optimal estimate of the state, also known as the posterior state estimate; t P is the Kalman filter gain. t Here is the error covariance matrix; Step 2.3: Use a Kalman filter to process the position, velocity, and acceleration information in Cartesian space to obtain more accurate acceleration information, as well as the velocity and position information of the end effector of the extremity robot; 2.3.1 The prediction process is carried out using the Kalman filter prediction equation, and the update process is carried out using the Kalman filter update equation. The prediction equation and the update equation are alternately iterated to obtain the optimal state estimate of the human-external limb system, that is, the observed human velocity and position. 2.3.

2. Based on step 2.3.1, the optimal state estimate of the end effector of the extremity robot is calculated using the kinematic principles of the human-extremity system. This yields the optimal state estimate of the end effector, namely, the predicted velocity and predicted position of the end effector. The expression for the kinematic principles of the human-extremity system is as follows: in, For the estimated end effector position, f is the estimated human body position. e，p J is the position vector in the homogeneous transformation matrix from the human body to the end effector. e，v (q) represents the Jacobian matrix under the current external limb configuration. Indicates the velocity of external limb joints; Step 2.4: Substitute the obtained predicted position, predicted velocity, and predicted acceleration information into the Kalman filter to obtain the state of the human-external limb system.

4. The method for human motion compensation in a wearable exoskeleton robot according to claim 3, characterized in that, In step 3, based on the estimated human body state and the estimated state of the extremity robot end effector obtained in step 2, the artificial potential field for dynamic trajectory planning of the extremity robot is constructed, including: Gravitational potential field Where λ d η, k, and ξ are constants, the function C(x) represents the equipotential curve generated by the target g, i represents the index value of a point in space, and x i Let θ represent a point in space, and let θ represent the angle between the estimated velocity v of the end effector and the vector pointing from the current position x of the end effector to the target g. Repulsive potential field in, R represents the repulsive force intensity of the i-th obstacle; i Represents the effective range of the repulsive force of the i-th obstacle; Heviside step function Only when the extremity of the extremity robot enters its radius of influence R i The repulsive potential field of the i-th obstacle is activated at a certain time, and the repulsive force is invalidated after the end of the extremity robot leaves the radius; distance l i This represents the shortest distance from the end effector of the extremity robot to the nearest point on the periphery of the i-th obstacle; The symbol representing the outer contour. Indicates taking O i Outer contour, O i This represents the geometry formed by the i-th obstacle; Uniform flow potential field Where, |λ u | represents the magnitude of the uniform flow potential field intensity, and the unit vector d represents the direction of the potential field, which is specified using the direction cosine; Spatial constrained potential field Where, λ c >0 represents the potential field strength, Ω represents the safe workspace, and SDF represents the distance from each location in the workspace to the boundary of Ω. If SDF Ω (x)<0, the spatial constraint gradient field guides the robot to move towards the center of the safe workspace.

5. A method for human motion compensation in a wearable exoskeleton robot according to claim 4, characterized in that, The velocity planning method used to assign velocity information to path points in step 4 is as follows: Where, λ v This is a scaling factor for controlling the convergence speed of the system. This indicates the potential field between the current position x and the target position g. geodesic distance on, v m To adjust the maximum threshold of speed, for the field The gradient.

6. A human motion compensation system for wearable exoskeleton robots, comprising: An inertial measurement unit is used to collect human acceleration information in real time and convert it into a unified Cartesian space. Visual odometry is used to collect the velocity and position information of exoskeleton robots in real time and convert it into a unified Cartesian space. The data processing unit receives human acceleration information in Cartesian space, as well as the velocity and position information of the exoskeleton robot. Based on the received human acceleration information and the velocity and position information of the exoskeleton robot, it calculates the desired angle and velocity of the exoskeleton joints according to any one of the human motion compensation methods for wearable exoskeleton robots as described in any one of claims 1 to 5, so as to control the movement of each joint of the exoskeleton robot.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of a human motion compensation method for a wearable exoskeleton robot as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of a human motion compensation method for a wearable exoskeleton robot as described in any one of claims 1 to 5.

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