Self-balancing walking control method of rehabilitation exoskeleton under dynamic deformation

Abstract

The application provides a self-balancing walking control method of a rehabilitation exoskeleton under dynamic deformation, comprising the following steps: collecting plantar force and torque data of an exoskeleton robot when the exoskeleton robot performs a walking action; inputting the plantar force and torque data into a feedforward deformation compensator to obtain feedforward compensation; a centroid active compliant controller performs calculation based on the plantar force and torque data and a desired trajectory to obtain centroid compensation; a new desired posture of the robot is determined according to the feedforward compensation and the centroid compensation; an inverse kinematics solver calculates an instruction joint angle of each joint of the robot according to the new desired posture; and the exoskeleton robot performs a walking and standing task based on the instruction joint angle. The method improves the stability of the rehabilitation exoskeleton robot walking by improving the deformation problem.

Description

Technical Field

[0001] This invention relates to the field of robotics, and in particular to a self-balancing walking control method for a rehabilitation exoskeleton under dynamic deformation. Background Technology

[0002] In recent years, with the aging population and the aggravation of related diseases, the number of patients with lower limb dysfunction or loss, such as hemiplegia and quadriplegia, caused by factors such as stroke, accidents, and spinal cord injury has increased rapidly. Globally, there are more than 13.7 million new stroke cases each year, making it the third leading cause of disability worldwide. Previous studies have shown that early intervention and goal-oriented training that gradually adapts to the patient's degree of injury and recovery stage can improve the functional prognosis of patients with lower limb weakness or loss of function. Therefore, people with motor function loss are increasingly reliant on various medical devices in their daily lives. Lower limb exoskeleton robots, as assistive devices, can provide support and balance for patients with motor dysfunction, enabling them to regain normal walking ability. Based on whether they possess self-balancing capabilities, lower limb exoskeleton robots can be divided into non-self-balancing exoskeleton robots and self-balancing exoskeleton robots. This patent focuses on self-balancing lower limb exoskeleton robots (SBLLEs). Since they lack external support devices, the core control objective of SBLLEs is to maintain the balance of the human-machine hybrid system while carrying different patients.

[0003] Design limitations inevitably lead to insufficient stiffness in exoskeleton robots, introducing unnecessary elasticity into the robot structure. This results in deformations with non-ideal characteristics, which hinder the accurate movement of self-balancing exoskeleton robots, making them prone to falls and potentially causing secondary injuries to patients. Therefore, existing exoskeleton robots have poor stability during walking. Summary of the Invention

[0004] In view of this, the present invention provides a self-balancing walking control method for a rehabilitation exoskeleton under dynamic deformation to solve the above problems.

[0005] This invention provides a self-balancing walking control method for a rehabilitation exoskeleton under dynamic deformation, comprising: collecting plantar force and torque data of the exoskeleton robot when performing walking actions; inputting the plantar force and torque data into a feedforward deformation compensator to obtain feedforward compensation; a centroid active compliant controller calculating centroid compensation based on the plantar force and torque data and the desired trajectory; determining a new desired posture of the robot based on the feedforward compensation and the centroid compensation; an inverse kinematics solver calculating the command joint angle of each joint of the robot based on the new desired posture; and the exoskeleton robot performing walking and standing tasks based on the command joint angles.

[0006] In another implementation of the present invention, the feedforward compensation is expressed as:

[0007] Δ p T W = d T W - r T W

[0008] in, d T W This indicates that the robot's position in the reference coordinate system Σ was calculated offline in advance. W China's expected stance r T W This indicates that the robot is in the reference coordinate system Σ W The true stance in the middle.

[0009] In another implementation of the present invention, the feedforward deformation compensator consists of an encoder and a decoder, used to establish a mapping relationship between the plantar force and torque data and the feedforward compensation, the mapping relationship being expressed as follows:

[0010] Δ p T = f Δ (F)

[0011] Where, Δ p T represents feedforward compensation, and F represents foot force and torque data.

[0012] In another implementation of the present invention, the method further includes: calculating the deviation formula between the actual position and the desired position of the robot's center of mass based on torque data, according to the robot's actual torque and desired torque in the reference coordinate system.

[0013] r τ B - d τ B =F ext ·z c

[0014] in, r τ B In the reference coordinate system Σ B The actual torque at the origin, In the reference coordinate system Σ B The desired torque at the origin, z c Represents the coordinate system Σ B The height of the centroid.

[0015] In another implementation of the present invention, the active compliance controller for the center of mass calculates center of mass compensation based on the plantar force and torque data and the desired trajectory, including: the active compliance controller for the center of mass calculates a deviation formula between the actual position and the desired position of the robot's center of mass based on the position data, based on the plantar force and torque data and the desired trajectory.

[0016]

[0017] in, r T represents the robot's position in the reference coordinate system Σ. B The actual location in d T represents the robot's position in the reference coordinate system Σ. B The desired position in the center is determined; the centroid compensation is obtained by combining the deviation formula obtained based on the torque data and the deviation formula obtained based on the position data.

[0018] In another implementation of the present invention, the new desired posture is represented as:

[0019]

[0020] in, d T W It is the robot's desired pose calculated offline in advance, Δ p T represents feedforward compensation, ΔT W It indicates centroid compensation.

[0021] The self-balancing walking control method of the rehabilitation exoskeleton under dynamic deformation of the present invention significantly improves the deformation problem through a feedforward deformation compensator; and solves the problem of strong parameter disturbance caused by different wearers in the hybrid exoskeleton robot system through a centroid active compliance controller; thus enabling the exoskeleton robot to achieve stable walking under dynamic deformation and with different subjects. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. By reading the detailed description of the embodiments below, the advantages and benefits of the solutions will become clear to those skilled in the art. The accompanying drawings are only for illustrating preferred embodiments and are not intended to limit the present invention.

[0023] In the attached diagram:

[0024] Figure 1 This is a schematic flowchart of a self-balancing walking control method for a rehabilitative exoskeleton under dynamic deformation, according to an embodiment of the present invention.

[0025] Figure 2This is a schematic diagram of an exoskeleton robot system according to an embodiment of the present invention.

[0026] Figure 3 This is a schematic diagram of a robot compliance model according to an embodiment of the present invention.

[0027] Figure 4 This is a schematic diagram of a deformation mapping data acquisition platform according to an embodiment of the present invention.

[0028] Figure 5 This is a schematic diagram of a feedforward deformation compensator according to an embodiment of the present invention.

[0029] Figure 6 This is a schematic diagram illustrating the process of establishing a mapping dataset according to an embodiment of the present invention.

[0030] Figure 7 This is a schematic diagram of a walking experiment according to an embodiment of the present invention. Detailed Implementation

[0031] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art should fall within the protection scope of the present invention.

[0032] Figure 1 This is a schematic flowchart of a self-balancing walking control method for a rehabilitation exoskeleton under dynamic deformation, provided by an embodiment of the present invention. Figure 1 As shown, this embodiment mainly includes:

[0033] S101. Collect foot force and torque data of the exoskeleton robot when performing walking actions.

[0034] S102. Input the foot force and torque data into the feedforward deformation compensator to obtain feedforward compensation.

[0035] For example, such as Figure 2 As shown, the sensing system provides the foot force and torque data acquired by the preceding F / T sensor to the feedforward deformation compensator (IFDC), which predicts the next feedforward compensation.

[0036] S103, the center of gravity active compliance controller calculates the center of gravity compensation based on the foot force and torque data and the desired trajectory.

[0037] For example, the sensing system provides sensor data that reflects the real-time status of the exoskeleton robot system to the center of mass active compliant controller (CACC), which calculates the center of mass compensation based on the desired trajectory generated by the trajectory generator and the current system status.

[0038] S104. Determine the robot's new desired posture based on feedforward compensation and centroid compensation.

[0039] S105, the inverse kinematics solver calculates the commanded joint angles of each joint of the robot based on the new desired posture.

[0040] S106, the exoskeleton robot performs walking and standing tasks based on commanded joint angles.

[0041] The self-balancing walking control method of the rehabilitation exoskeleton under dynamic deformation of the present invention significantly improves the deformation problem through a feedforward deformation compensator; and solves the problem of strong parameter disturbance caused by different wearers in the hybrid exoskeleton robot system through a centroid active compliance controller; thus enabling the exoskeleton robot to achieve stable walking under dynamic deformation and with different subjects.

[0042] In another implementation of the present invention, the feedforward compensation is expressed as:

[0043] Δ p T W = d T W - r T W

[0044] in, d T W This indicates that the robot's position in the reference coordinate system ∑ was calculated offline in advance. W China's expected stance r T W This indicates that the robot is in the reference coordinate system ∑ W The true stance within.

[0045] For example, such as Figure 4 As shown, due to deformation, ∑ O In the world coordinate system ∑ W The center moved to ∑ O′ The IFDC algorithm aims to convert ∑ O′ attitude compensation return point ∑ O The posture.

[0046] These deformation parameters can be directly used as inputs for inverse kinematics. They not only compensate for the premature contact of the swing leg (deformation in the z-direction) but also address the issue of the robot tilting towards the swing leg (deformation in the y-direction). For self-balancing exoskeleton robots, the deformation parameters caused by non-ideal characteristics vary with the changes in the six-dimensional forces / torques under the feet. (Robot posture) r T W It reflects the robot's overall deformation parameters.

[0047] In another implementation of the present invention, the feedforward deformation compensator consists of an encoder and a decoder, used to establish a mapping relationship between the plantar force and torque data and the feedforward compensation, the mapping relationship being expressed as follows:

[0048] Δ p T = f Δ (F)

[0049] Where, Δ p T represents feedforward compensation, and F represents foot force and torque data.

[0050] For example, such as Figure 5 As shown, the network model of the feedforward deformation compensator consists of an encoder and a decoder. This network utilizes an efficient attention mechanism to focus attention on more prominent features in the data and capture long-term dependencies in the six-dimensional force dataset. The input to the IFDC is F, which is the six-dimensional force / torque data under the feet, and the output of the IFDC is Δ. p T, the purpose is to establish the transition from F to Δ p The mapping relationship of T describes the transformation from changes in robot force to changes in robot posture.

[0051] In order to obtain the training dataset for F and the robot r T W The true posture was captured using a motion capture system. For example... Figure 4 As shown in b, the motion capture system measures the posture of a reflective ball fixed in front of the exoskeleton. By tracking the posture changes of the reflective ball, the posture changes of the robot can be obtained, which will help to collect deformation datasets.

[0052] By utilizing the Informer-based feedforward deformation compensator (IFDC), the dynamic deformation problem of the self-balancing exoskeleton robot human-robot hybrid system is effectively solved. The feedforward deformation compensator reduces the robot's position error by an average of 79.04% and its posture error by an average of 84.95%, significantly improving the deformation problem.

[0053] In another implementation of the present invention, the method further includes: calculating the deviation formula between the actual position and the desired position of the robot's center of mass based on torque data, according to the robot's actual torque and desired torque in the reference coordinate system.

[0054] r τ B - d τ B =F ext ·z c

[0055] in, r τ B In the reference coordinate system ∑ B The actual torque at the origin, In the reference coordinate system ∑ B The desired torque at the origin, z c Represents the coordinate system Σ B The height of the centroid.

[0056] For example, such as Figure 3 As shown, based on the linear inverted pendulum model, the relationship between the robot's zero torque point (ZMP) and center of mass (COM) can be obtained:

[0057]

[0058] in, and T W Representing ZMP and CoM in Σ W The position of z w The center point represents the coordinate system Σ W The height of the middle.

[0059] Considering deformation compensation To interfere, and set As the control variables, the system model can be described by the following state equations:

[0060]

[0061] y = Cx

[0062] Substitute the control variables into the equation, and use the sampling step size t. s Discretizing it, the state-space equation can be rewritten as:

[0063] x(i+1)=Gx(i)+H(u(i)+u ff (i))

[0064] y(i)=Cx(i)

[0065] in, y represents the robot's ZMP.

[0066] like Figure 3 As shown, the robot is in the reference coordinate system Σ B During the movement, the robot's actual position (represented as...) r T) may deviate from the expected position d T. This deviation can be addressed by introducing a virtual force F. ext Therefore:

[0067] r τ B - d τ B =F ext ·z c

[0068] in, r τ B In the reference coordinate system Σ B The actual torque at the origin, In the reference coordinate system Σ B The desired torque at the origin, z c Represents the coordinate system Σ B The height of the center point.

[0069] In another implementation of the present invention, the active compliance controller for the center of mass calculates center of mass compensation based on the plantar force and torque data and the desired trajectory, including: the active compliance controller for the center of mass calculates a deviation formula between the actual position and the desired position of the robot's center of mass based on the position data, based on the plantar force and torque data and the desired trajectory.

[0070]

[0071] in, r T represents the robot's position in the reference coordinate system Σ. B The actual location in d T represents the robot's position in the reference coordinate system Σ. B The desired position in the center is determined; the centroid compensation is obtained by combining the deviation formula obtained based on the torque data and the deviation formula obtained based on the position data.

[0072] For example, CACC can be used to describe the relationship between the actual position and the desired position of the robot's center point:

[0073]

[0074] By rearranging the equation, we can obtain:

[0075]

[0076] Continue organizing:

[0077]

[0078] Simplify the equations:

[0079]

[0080] Based on the above equations, if it is known r τ B and This allows us to obtain the position deviation of the centroid in the world coordinate system for each cycle. These two variables can be calculated using the following equation:

[0081]

[0082] therefore:

[0083]

[0084] The control law can be updated as follows:

[0085] T W (i+1)=T W (i)+ΔT W (i)

[0086] By utilizing a centroid-based active compliance controller, the problem of strong parameter disturbances caused by different wearers in a hybrid exoskeleton robot system was solved.

[0087] In another implementation of the present invention, the new desired posture is represented as:

[0088]

[0089] in, d T W It is the robot's desired pose calculated offline in advance, Δ p T represents feedforward compensation, ΔT W It indicates centroid compensation.

[0090] In another implementation of the invention, since deformation hinders the acquisition of normal gait data, a standing experiment was used to acquire deformation data for the self-balancing exoskeleton robot. For example... Figure 6 As shown, the experiment involved static movements such as squatting and standing, waist rotation, swinging the left leg and swinging the right foot. The squatting and standing and waist rotation movements simulated the two-leg support phase, while the left leg swing and right leg swing movements simulated the single-leg support phase. Therefore, these four movements can simulate the entire walking cycle, thereby establishing a mapping dataset.

[0091] like Figure 7As shown, a total of 7 groups of self-balancing exoskeleton robot walking experiments were conducted. The results show that the method achieved stable walking of the self-balancing exoskeleton robot within a load range of 0kg to 73kg, indicating that the method improves the stability of the exoskeleton robot during walking.

[0092] This invention analyzes the manifestations and influencing factors of exoskeleton deformation, establishes an end-to-end mapping relationship between exoskeleton centroid deformation and foot force / torque, and directly predicts and compensates for exoskeleton centroid deformation through exoskeleton foot force / torque data, thereby ensuring that the lower limb self-balancing exoskeleton walks along the expected trajectory.

[0093] Specific embodiments of the invention have now been described. Other embodiments are within the scope of the appended claims. In some cases, the actions described in the claims can be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing can be advantageous.

[0094] It should be noted that all directional indicators (such as up, down, left, right, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicator will also change accordingly.

[0095] In the description of this invention, the terms "first" and "second" are used only for convenience in describing different components or names, and should not be construed as indicating or implying a sequential relationship, relative importance, or implicitly specifying the number of technical features indicated. Thus, a feature defined with "first" and "second" may explicitly or implicitly include at least one of that feature.

[0096] Unless otherwise defined, 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 pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0097] It should be noted that although specific embodiments of the present invention have been described in detail with reference to the accompanying drawings, this should not be construed as limiting the scope of protection of the present invention. Various modifications and variations that can be made by those skilled in the art without inventive effort within the scope described in the claims still fall within the scope of protection of the present invention.

[0098] The examples of the embodiments of the present invention are intended to concisely illustrate the technical features of the embodiments of the present invention, so that those skilled in the art can intuitively understand the technical features of the embodiments of the present invention, and are not intended to be an improper limitation of the embodiments of the present invention.

[0099] 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 them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for controlling self-balancing walking in a rehabilitation exoskeleton under dynamic deformation, characterized in that, include: Collect data on plantar force and torque of the exoskeleton robot when performing walking movements; The foot force and torque data are input into the feedforward deformation compensator to obtain the feedforward compensation, which is expressed as: in, This indicates that the robot's position in the reference coordinate system was calculated offline in advance. China's expected stance Indicates the robot in the reference coordinate system The true stance in the middle; The feedforward deformation compensator consists of an encoder and a decoder, used to establish a mapping relationship between the plantar force and torque data and the feedforward compensation, the mapping relationship being expressed as follows: in, This indicates feedforward compensation, and F represents the foot force and torque data; The center-of-gravity active compliance controller calculates center-of-gravity compensation based on the plantar force and torque data and the desired trajectory; The new desired pose of the robot is determined based on the feedforward compensation and the centroid compensation, and the new desired pose is expressed as follows: in, The desired robot pose is calculated offline in advance. Indicates feedforward compensation. Indicates centroid compensation; The inverse kinematics solver calculates the commanded joint angles for each joint of the robot based on the new desired posture; The exoskeleton robot performs walking and standing tasks based on the commanded joint angles.

2. The method according to claim 1, characterized in that, Also includes: Based on the robot's actual torque and desired torque in the reference coordinate system, the deviation formula between the actual position and desired position of the robot's center of mass, derived from torque data, is obtained: in, In the reference coordinate system The actual torque at the origin, In the reference coordinate system The desired torque at the origin, Representing the coordinate system The height of the centroid, It is a virtual force.

3. The method according to claim 2, characterized in that, The center-of-gravity active compliance controller calculates center-of-gravity compensation based on the plantar force and torque data and the desired trajectory, including: The center-of-gravity active compliance controller calculates the deviation between the actual position and the desired position of the robot's center of gravity based on the foot force and torque data and the desired trajectory, using a formula derived from the position data: in, Indicates the robot in the reference coordinate system The actual location in Indicates the robot in the reference coordinate system The desired position in; The centroid compensation is obtained by combining the deviation formulas obtained from torque data and position data.

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