A control method and device for an exoskeleton robot

Abstract

The present application provides a control method for an exoskeleton robot, which is used to control the exoskeleton to assist the wearer in walking, including: obtaining a target task and generating a perceptual stimulation model based on the target task; obtaining the walking cadence and current torque information of the wearer when performing the target task using the perceptual stimulation model; wherein the current torque information is generated by torque sensors at the joints of the exoskeleton robot; generating a desired torque and a desired joint trajectory based on the walking cadence and current torque information; and adjusting the assistance of the exoskeleton robot based on the current torque information, the desired torque, and the desired joint trajectory. The perceptual stimulation model can stimulate the wearer to complete the task, and at the same time, the assistance state of the exoskeleton robot is adjusted in real time according to the wearer's actual walking state, so as to stimulate the wearer to more actively mobilize their own muscles and achieve the purpose of active training.

Description

Technical Field

[0001] The present invention mainly relates to the field of exoskeleton, and in particular to a control method and device for an exoskeleton robot. Background Art

[0002] Patients with lower limb motor dysfunction due to brain damage or limb injury require physical assistance therapy to achieve the goal of lower limb motor function recovery or improvement. Physical assistance therapy can be provided to patients through lower limb rehabilitation training exoskeleton robots. Common lower limb rehabilitation exoskeleton robots are mainly designed for adult patients. After wearing the lower limb exoskeleton robot, patients rely on handheld crutches or simple hand supports for walking training; or the machine support system can provide support for patients to meet the needs of walking training.

[0003] However, the existing technology mainly relies on therapists to manually assist patients in leg joint flexion and extension recovery training, and only passive training is performed without active training mode (passive mode lower limb rehabilitation training means that there is no human-machine interaction between the machine system and the patient. Regardless of whether the patient exerts force, the lower limb exoskeleton robot will perform cyclic movements according to the preset gait trajectory), and the training and rehabilitation effect is poor. Summary of the Invention

[0004] In view of the above problems, the present application is proposed to provide a control method and device for an exoskeleton robot that overcomes the above problems or at least partially solves the above problems, including:

[0005] A control method for an exoskeleton robot, the control method being used to control the exoskeleton robot to perform actions included in a target task on a wearer, comprising:

[0006] Acquiring the target task, and generating a perceptual stimulation model according to the target task;

[0007] Obtaining walking cadence and current torque information of the wearer when performing a target task through the perceptual stimulation model; wherein the current torque information is generated by a torque sensor at a joint of the exoskeleton robot;

[0008] generating an expected torque and an expected joint trajectory according to the walking cadence and the current torque information;

[0009] The power assistance of the exoskeleton robot is adjusted according to the current torque information, the expected torque and the expected joint trajectory.

[0010] Furthermore, the step of obtaining a target task and generating a perceptual stimulation model according to the target task includes:

[0011] Get the target task;

[0012] generating a visual stimulus model according to the target task;

[0013] generating an auditory stimulation model according to the target task;

[0014] A perception stimulation model is generated according to the visual stimulation model and the auditory stimulation model.

[0015] Furthermore, the step of obtaining the walking cadence and current torque information of the wearer when performing the target task through the perceptual stimulation model, wherein the current torque information is generated by the torque sensor at the joint of the exoskeleton robot, includes:

[0016] Obtaining the time interval between two adjacent foot strikes on the same leg when the wearer performs a target task using the perceptual stimulation model;

[0017] Calculating an average value of the time intervals within a preset time and generating a walking cadence based on the average value;

[0018] Obtain the current torque information of the wearer.

[0019] Furthermore, before the step of obtaining the time interval between two adjacent foot strikes on the same leg when the wearer performs the target task through the perception stimulation model, the method further includes:

[0020] The equipment weight is balanced according to the weight reduction algorithm.

[0021] Furthermore, the step of generating the expected torque and expected joint trajectory based on the walking cadence and current torque information includes:

[0022] generating an expected joint movement speed according to the walking cadence;

[0023] generating an expected joint motion angle according to the expected joint motion speed;

[0024] The expected torque and the expected joint trajectory are generated according to the current joint motion angle and the expected joint motion angle.

[0025] Furthermore, the step of adjusting the power assistance of the exoskeleton robot according to the current torque information, the expected torque and the expected joint trajectory includes:

[0026] Calculating an average difference between the actual joint angle and the desired torque within a preset time period;

[0027] The power assistance level is adjusted according to the average difference until the average difference is lower than a preset value.

[0028] Furthermore, the step of adjusting the power assistance level according to the average difference until the average difference is lower than a preset value includes:

[0029] Obtaining a human-machine interaction force between the wearer's lower limbs and the exoskeleton robot; wherein the human-machine interaction force is generated by the torque sensor;

[0030] determining a gait phase according to the joint motion angle, and determining a power assistance level according to the gait phase and the human-machine interaction force;

[0031] The power-assistance training is performed according to the power-assistance level output torque.

[0032] A control device for an exoskeleton robot, wherein the device for active rehabilitation training of children using an exoskeleton implements the steps of any of the above-mentioned methods for active rehabilitation training of children using an exoskeleton, including:

[0033] A task acquisition module, configured to acquire a target task and generate a perceptual stimulus model based on the target task;

[0034] A human-computer interaction module, configured to obtain the walking cadence and current torque information of the wearer when performing the target task through the perceptual stimulation model; wherein the current torque information is generated by torque sensors at the joints of the exoskeleton robot;

[0035] An expected motion generation module, configured to generate an expected torque according to the walking cadence;

[0036] The power assistance level adjustment module is used to adjust the power assistance of the exoskeleton robot according to the current torque information and the expected torque.

[0037] An electronic device comprises a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the steps of the control method of the exoskeleton robot as described in any one of the above items are implemented.

[0038] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for controlling an exoskeleton robot.

[0039] This application has the following advantages:

[0040] In an embodiment of the present application, in response to the problem that exoskeleton robots in the prior art have no targeted control and no active training mode, the present application provides a control method for an exoskeleton robot, the control method being used to control the exoskeleton to assist the wearer in walking, comprising: obtaining the target task and generating a perceptual stimulation model based on the target task; obtaining the walking cadence and current torque information of the wearer when performing the target task through the perceptual stimulation model; wherein the current torque information is generated by torque sensors at the joints of the exoskeleton robot; generating an expected torque and an expected joint trajectory based on the walking cadence and current torque information; and adjusting the assistance of the exoskeleton robot based on the current torque information, the expected torque, and the expected joint trajectory. The perceptual stimulation model can stimulate the wearer to complete the task, and at the same time, the assistance state of the exoskeleton robot is adjusted in real time according to the wearer's actual walking state, so as to stimulate the wearer to more actively mobilize his or her own muscles and achieve the purpose of active training. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for the description of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0042] Figure 1 This is a flowchart of a control method for an exoskeleton robot provided in one embodiment of the present application;

[0043] Figure 2 This is a control flow chart of a control method for an exoskeleton robot provided in one embodiment of the present application;

[0044] Figure 3 This is a schematic diagram of the module structure of a control device for an exoskeleton robot provided in one embodiment of the present application;

[0045] Figure 4 This is a flow chart of controlling an exoskeleton robot through a weight reduction algorithm, a power assist algorithm, and a force control algorithm, provided in one embodiment of the present application;

[0046] Figure 5 It is a structural diagram of a computer device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0047] To make the objectives, features, and advantages of this application more readily apparent, the present application is further described below in conjunction with the accompanying drawings and specific embodiments. It is apparent that the embodiments described are only a portion of the embodiments of this application, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments in this application without inventive effort are also within the scope of protection of this application.

[0048] By analyzing existing technologies, the inventors discovered that by providing sensory stimulation to the wearer and obtaining the wearer's movement status when wearing the exoskeleton, preset tasks can be completed more efficiently while providing a power assist state suitable for the wearer.

[0049] Reference Figure 1-2 , shows a control method of an exoskeleton robot of the present application, the control method is used to control the exoskeleton to assist the wearer in walking;

[0050] The control method includes:

[0051] S110, obtaining a target task, and generating a perceptual stimulation model according to the target task;

[0052] S120, obtaining walking cadence and current torque information of the wearer when performing a target task using the perceptual stimulation model; wherein the current torque information is generated by torque sensors at joints of the exoskeleton robot;

[0053] S130, generating an expected torque and an expected joint trajectory according to the walking cadence and the current torque information;

[0054] S140. Adjust the power assistance of the exoskeleton robot according to the current torque information, the expected torque, and the expected joint trajectory.

[0055] In an embodiment of the present application, in response to the problem that exoskeleton robots in the prior art have no targeted control and no active training mode, the present application provides a control method for an exoskeleton robot, the control method being used to control the exoskeleton to assist the wearer in walking, comprising: obtaining a target task and generating a perceptual stimulation model based on the target task; obtaining the walking cadence and current torque information of the wearer when performing the target task through the perceptual stimulation model; wherein the current torque information is generated by torque sensors at the joints of the exoskeleton robot; generating an expected torque and an expected joint trajectory based on the walking cadence and current torque information; and adjusting the assistance of the exoskeleton robot based on the current torque information, the expected torque, and the expected joint trajectory. The perceptual stimulation model can stimulate the wearer to complete the task, and at the same time, the assistance state of the exoskeleton robot is adjusted in real time according to the wearer's actual walking state, so as to stimulate the wearer to more actively mobilize his or her own muscles and achieve the purpose of active training.

[0056] Next, a control method and device for an exoskeleton robot in this exemplary embodiment will be further described.

[0057] As described in step S110 above, a target task is acquired, and a perceptual stimulation model is generated according to the target task.

[0058] It should be noted that the target task is the preset time or distance that the wearer uses the exoskeleton robot for exercise or training. The specific task is set according to the wearer's personal situation. The specific task will be presented in the form of a perceptual stimulation model. The perceptual stimulation model will make corresponding prompts based on the difference between the real-time motion information and the ideal motion data during use. At the same time, it will evaluate the training status of the child patient in real time based on the gait rehabilitation assessment algorithm, and dynamically adjust the training strategy of the exoskeleton robot according to the evaluation results to guide the wearer to complete the task correctly.

[0059] In one embodiment of the present invention, the specific process of "obtaining a target task and generating a perceptual stimulation model according to the target task" in step S110 may be further explained in combination with the following description.

[0060] As described in the following steps, a target task is obtained; the target task is set manually or automatically generated according to the wearer's own situation.

[0061] As described in the following steps, a visual stimulation model is generated according to the target task; the visual stimulation model is presented through a visual stimulation device (display, VR, AR).

[0062] As described in the following steps, an auditory stimulation model is generated according to the target task; the auditory stimulation model is presented through an auditory feedback device (speaker, earphone).

[0063] As described in the following steps, a perceptual stimulation model is generated according to the visual stimulation model and the auditory stimulation model.

[0064] It should be noted that the perceptual stimulation model is presented in the form of games, etc., which guides the wearer to complete the target task through the game, while obtaining the motion information of the exoskeleton robot and integrating the motion information into the target task as a trigger event to generate a task that is more suitable for wear.

[0065] As described in step S120 above, the walking cadence and current torque information of the wearer when performing the target task through the perceptual stimulation model are obtained; wherein the current torque information is generated by the torque sensor at the joint of the exoskeleton robot.

[0066] It should be noted that the current torque information is human-computer interaction torque information, and also includes real-time joint motion information such as the current joint motion angle and the current joint motion speed.

[0067] The current torque information is generated by a torque sensor installed at the joint of the exoskeleton robot, which is responsible for collecting interaction force information. The torque sensor will collect the torque information acting on the joint in real time during training, and combine it with the dynamic model of the exoskeleton to calculate the torque applied to the joint of the exoskeleton robot due to the squeezing between the user's limbs and the leg rods of the exoskeleton robot, so as to characterize the human-computer interaction information between the user and the exoskeleton robot.

[0068] In one embodiment of the present invention, the specific process of "obtaining the walking cadence and current torque information of the wearer when performing the target task through the perceptual stimulation model; wherein the current torque information is generated by the torque sensor at the joint of the exoskeleton robot" in step S120 can be further explained in combination with the following description.

[0069] As described in the following steps, the time interval between two adjacent foot strikes on the same leg of the wearer when performing the target task through the perceptual stimulation model is obtained; the time interval can represent the cadence of the wearer when walking.

[0070] As described in the following steps, the average value of the time interval within the preset time is calculated and the walking cadence is generated based on the average value; the change of the cadence is continuously monitored, multiple cadence data within the preset time are obtained and the average value of the cadence data is calculated, which can be used as a basis for real-time adjustment of the desired torque of the exoskeleton robot.

[0071] As described in the following steps, the current torque information of the wearer is obtained. The actual joint motion angle of the wearer is obtained.

[0072] As described in the following steps, balance the device weight according to the weight reduction algorithm.

[0073] It should be noted that the weight reduction algorithm is used to reduce the weight of the robotic exoskeleton, so that the wearer only needs a very small force to drive the joints of the exoskeleton robot to move.

[0074] When the exoskeleton robot is in use, the dynamic equation of the human-machine interaction force between the wearer and the robot's legs in the robot's joint space is:

[0075]

[0076] Where M(q) is the inertia matrix, is the Coriolis force and centrifugal force matrix, g(q) is the gravitational torque; τ is the joint driving torque; τ f is the joint friction torque; τ ext is the external torque of the joint.

[0077] The inertia constants, gravity magnitude, and center-of-gravity distance of the exoskeleton robot's large and small legs can all be obtained through 3D modeling and simulation. The classic CV model is used to compensate for friction:

[0078]

[0079] Where μ is the Coulomb friction coefficient; v is the viscous friction coefficient; q min is the speed threshold from static friction to kinetic friction.

[0080] By accurately modeling the exoskeleton's dynamics, the weight-reduction algorithm uses encoders installed on the exoskeleton's joints to obtain real-time information about the exoskeleton's thigh and calf joint angles. It then uses dynamic equations based on joint space to calculate the torques acting on the hip and knee joints, controlling the motors in each joint to produce the same torque, thus achieving real-time gravity compensation. Because the weight of the robot's legs is offset by the weight-reduction algorithm in real time, the wearer barely needs to bear the weight of the device during rehabilitation training.

[0081] As described in step S130 above, the expected torque and expected joint trajectory are generated according to the walking cadence and the current torque information.

[0082] It should be noted that obtaining the desired torque simultaneously generates the desired torque and the desired joint trajectory. The desired torque includes the desired joint motion speed and the desired joint motion angle, which are generated by the walking cadence and the target task or are manually set.

[0083] In one embodiment of the present invention, the specific process of "generating the expected torque and expected joint trajectory according to the walking cadence and the current torque information" in step S130 can be further explained in combination with the following description.

[0084] As described in the following steps, generating an expected joint movement speed according to the walking cadence;

[0085] As described in the following steps, generating a desired joint motion angle according to the desired joint motion speed;

[0086] As described in the following steps, the expected torque and the expected joint trajectory are generated according to the current joint motion angle and the expected joint motion angle.

[0087] As described in step S140 above, the power assist condition of the exoskeleton robot is adjusted according to the current torque information, the expected torque, and the expected joint trajectory. The power assist condition is a power assist level with a gradient of power assist magnitude.

[0088] It should be noted that by monitoring the wearer's real-time joint motion information and comparing it with ideal joint data, the exoskeleton's assistance level is adjusted in real time based on the quality of the data fit. Simultaneously, the exoskeleton's foot pressure sensor signals are monitored, recording the ground contact interval between the ipsilateral leg. This interval can represent the child's walking speed during training, also known as cadence. The gait rehabilitation assessment algorithm dynamically adjusts the speed of the expected joint motion trajectory based on this cadence data to better adapt to the wearer's movement speed.

[0089] In one embodiment of the present invention, the specific process of "adjusting the assistance of the exoskeleton robot according to the current torque information, the expected torque and the expected joint trajectory" in step S140 can be further explained in combination with the following description.

[0090] As described in the following steps, calculating the average difference between the actual joint angle and the expected torque within a preset time period;

[0091] As described in the following steps, the power assistance level is adjusted according to the average difference until the average difference is lower than a preset value.

[0092] It should be noted that the difference between the actual joint movement angle and the expected joint movement angle is calculated; when the difference is greater than 0, it indicates that the wearer's muscles are strong and their autonomous participation is high, and the power assistance level can be adjusted lower; when the difference is less than 0, it indicates that the wearer's muscles are weak and their autonomous participation is low, and the power assistance level should be increased to better complete the task. The changes in the difference are continuously monitored, and multiple differences over a period of time are obtained and the average value of the difference is calculated. This is used as a basis for real-time adjustment of the exoskeleton robot's power assistance level. The power assistance level is adjusted in real time until the average value is lower than the preset value. At this point, it can be considered that the power assistance level is adapted to the wearer's walking ability.

[0093] In one embodiment of the present invention, the specific process of the step of "adjusting the power level according to the average difference until the average difference is lower than the preset value" can be further explained in combination with the following description:

[0094] As described in the following steps, the human-machine interaction force between the wearer's lower limbs and the exoskeleton robot is obtained; wherein the human-machine interaction force is generated by the torque sensor;

[0095] As described in the following steps, a gait phase is determined according to the joint motion angle, and a power assistance level is determined according to the gait phase and the human-machine interaction force;

[0096] As described in the following steps, power-assisted training is performed according to the power-assisted level output torque.

[0097] It should be noted that during walking, the degree of lower limb muscle engagement and exertion varies at different phases within a gait cycle. Based on this characteristic, the power assist algorithm can simultaneously monitor the movement angles of the hip, knee, and ankle joints of both legs, and comprehensively analyze the wearer's gait phase. The control coefficients of the power assist algorithm vary at different gait phases, achieving a power assist effect more suitable for the human body. However, the power assist method uses the movement angle as input and the torque of the joint motor as output.

[0098] When the wearer's right toe starts to leave the ground and is about to lift the leg and step forward, the power assist algorithm will introduce the joint angle of the right hip. When the right hip angle is around 0°, the wearer is in the early stage of leg lifting. If the wearer is a child, his or her limb strength is relatively weak. The power assist algorithm will control the right hip motor to output a larger torque value to assist the child in completing the leg lifting action. When the right hip angle gradually increases, that is, it approaches the end of the leg lifting and is about to touch the heel, the power assist algorithm will gradually reduce the output power assist torque value to better guide the wearer to complete the heel touching the ground.

[0099] The power assist algorithm also features an adjustable power assist scaling factor, tailored to the wearer's individual needs. This scaling factor is determined based on the wearer's physical strength, age, medical condition, and stage of rehabilitation. Children with weaker physical strength may require greater power assist to complete gait training, while stronger wearers may require less power assist. This allows the wearer to rely more on their own muscle strength to complete the task, achieving a more optimal rehabilitation or power assist effect.

[0100] The force control algorithm in this invention provides two control modes based on the wearer's situation and usage scenarios: one based on impedance control and the other based on zero-force control. The impedance-based force control algorithm is suitable for wearers with poor autonomy and weak muscle strength, while the admittance-based force control algorithm is suitable for wearers with strong autonomy and strong muscle strength.

[0101] The impedance control model is as follows:

[0102]

[0103] Among them, B d is the impedance damping coefficient matrix, K d is the impedance stiffness coefficient matrix, F is the generalized human-machine interaction force, θ, θ d are the expected joint motion angle and the actual joint angle respectively.

[0104] The zero-force control model is as follows:

[0105]

[0106] Among them, F Z is the joint output torque, K Z is the proportional magnification factor, D Z is the moment damping coefficient, T m is the measurement value of the joint torque sensor, G d is the joint target gravity torque.

[0107] like Figure 4 As shown, this is a flow chart for controlling the exoskeleton robot through weight reduction algorithm, power assist algorithm and force control algorithm.

[0108] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0109] Reference Figure 3 , shows a control device for an exoskeleton robot provided by an embodiment of the present application;

[0110] Specifically include:

[0111] A task acquisition module 310 is used to acquire a target task and generate a perceptual stimulus model according to the target task;

[0112] The human-computer interaction module 320 is used to obtain the walking frequency and current torque information of the wearer when performing the target task through the perception stimulation model;

[0113] An expected motion generation module 330 is configured to generate an expected torque and an expected joint trajectory according to the walking cadence and the current torque information;

[0114] The power assist level adjustment module 340 is configured to adjust the power assist of the exoskeleton robot according to the current torque information, the desired torque, and the desired joint trajectory.

[0115] In one embodiment of the present invention, the task acquisition module 310 includes:

[0116] Target task acquisition submodule, used to obtain target tasks;

[0117] A visual stimulation model generation submodule, configured to generate a visual stimulation model according to the target task;

[0118] An auditory stimulation model generation submodule, configured to generate an auditory stimulation model according to the target task;

[0119] The perceptual stimulation model generation submodule is used to generate a perceptual stimulation model based on the visual stimulation model and the auditory stimulation model.

[0120] In one embodiment of the present invention, the human-computer interaction module 320 includes:

[0121] A ground contact interval acquisition submodule, configured to acquire the time interval between two adjacent ground contact of the sole of the same leg of the wearer when the wearer performs a target task through the perception stimulation model;

[0122] A cadence generation submodule, configured to calculate an average value of the time intervals within a preset time and generate a walking cadence based on the average value;

[0123] The current torque information acquisition submodule is used to obtain the current torque information of the wearer.

[0124] In one embodiment of the present invention, the human-computer interaction module 320 further includes:

[0125] The weight reduction algorithm submodule balances the equipment weight according to the weight reduction algorithm.

[0126] In one embodiment of the present invention, the expected motion generation module 330 includes:

[0127] An expected joint motion speed generating submodule, configured to generate an expected joint motion speed according to the walking cadence;

[0128] an expected joint motion angle generating submodule, configured to generate an expected joint motion angle according to the expected joint motion speed;

[0129] The expected torque generation submodule is used to generate an expected torque and an expected joint trajectory according to the current joint motion angle and the expected joint motion angle.

[0130] In one embodiment of the present invention, the adjustment module 340 includes:

[0131] an average difference calculation submodule, configured to calculate an average difference between the actual joint angle and the desired torque within a preset time period;

[0132] The power assist level adjustment submodule is configured to adjust the power assist level according to the average difference until the average difference is lower than a preset value.

[0133] In one embodiment of the present invention, the power assist level adjustment submodule includes:

[0134] A human-machine interaction force acquisition submodule is used to acquire the human-machine interaction force between the wearer's lower limbs and the exoskeleton robot; wherein the human-machine interaction force is generated by the torque sensor;

[0135] A power assistance level confirmation submodule, configured to determine a gait phase according to a joint motion angle, and to determine a power assistance level according to the gait phase and the human-machine interaction force;

[0136] The training submodule is used to perform power-assistance training according to the power-assistance level output torque.

[0137] Reference Figure 5 , showing a computer device for controlling an exoskeleton robot of the present invention, which may specifically include the following:

[0138] The computer device 12 is a general-purpose computing device. The components of the computer device 12 may include but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 connecting different system components (including the system memory 28 and the processing unit 16).

[0139] The bus 18 represents one or more of several types of bus 18 structures, including a memory bus 18 or memory controller, a peripheral bus 18, an accelerated graphics port, a processor, or a local bus 18 that utilizes any of a variety of bus 18 architectures. Examples of such architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus 18, a Micro Channel Architecture (MAC) bus 18, an Enhanced ISA bus 18, an Audio Video Electronics Standards Association (VESA) local bus 18, and a Peripheral Component Interconnect (PCI) bus 18.

[0140] The computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0141] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be configured to read and write to non-removable, non-volatile magnetic media (commonly referred to as a "hard drive"). Although Figure 4 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules 42 configured to perform the functions of various embodiments of the present invention.

[0142] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in a memory. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules 42, and program data, each of which, or some combination thereof, may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0143] The computer device 12 may also communicate with one or more external devices 14 (e.g., a keyboard, a pointing device, a display 24, a camera, etc.), one or more devices that enable medical personnel to interact with the computer device 12, and / or any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the computer device 12 may also communicate with one or more networks (e.g., a local area network (LAN)), a wide area network (WAN), and / or a public network (e.g., the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with the other modules of the computer device 12 via the bus 18. It should be understood that although Figure 4 Not shown, other hardware and / or software modules may be used in conjunction with the computer device 12, including but not limited to microcode, device drivers, redundant processing units 16, external disk drive arrays, RAID systems, tape drives, and data backup storage systems 34.

[0144] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing a control method for an exoskeleton robot provided in an embodiment of the present invention.

[0145] That is, when the processing unit 16 executes the above program, the following steps are achieved: obtaining a target task, and generating a perceptual stimulus model according to the target task;

[0146] Obtaining walking cadence and current torque information of the wearer when performing a target task through the perceptual stimulation model; wherein the current torque information is generated by a torque sensor at a joint of the exoskeleton robot;

[0147] generating an expected torque and an expected joint trajectory according to the walking cadence and the current torque information;

[0148] The power assistance of the exoskeleton robot is adjusted according to the current torque information, the expected torque and the expected joint trajectory.

[0149] In an embodiment of the present invention, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a control method for an exoskeleton robot as provided in all embodiments of the present application:

[0150] That is, when the program is executed by the processor, the following steps are achieved: obtaining a target task, and generating a perceptual stimulus model according to the target task;

[0151] Obtaining walking cadence and current torque information of the wearer when performing a target task through the perceptual stimulation model; wherein the current torque information is generated by a torque sensor at a joint of the exoskeleton robot;

[0152] generating an expected torque and an expected joint trajectory according to the walking cadence and the current torque information;

[0153] The power assistance of the exoskeleton robot is adjusted according to the current torque information, the expected torque and the expected joint trajectory.

[0154] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0155] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0156] The computer program code for performing the operations of the present invention can be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code can be executed entirely on the medical staff computer, partially on the medical staff computer, as a separate software package, partially on the medical staff computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the medical staff computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, through the Internet using an Internet service provider). The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referenced to each other.

[0157] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0158] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0159] The above is a detailed introduction to the control method and device for an exoskeleton robot provided by the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of the present application. At the same time, for general technical personnel in this field, based on the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A control method for an exoskeleton robot, characterized in that: The control method is used to control the exoskeleton robot to perform actions included in the target task on the wearer, including: Acquiring the target task, and generating a perceptual stimulation model according to the target task; Obtaining walking cadence and current torque information of the wearer when performing a target task through the perceptual stimulation model; wherein the current torque information is generated by a torque sensor at a joint of the exoskeleton robot; Generate an expected torque and an expected joint trajectory based on the walking cadence and the current torque information; specifically, generate an expected joint motion speed based on the walking cadence; generate an expected joint motion angle based on the expected joint motion speed; generate an expected torque and an expected joint trajectory based on the current joint motion angle and the expected joint motion angle; The assistance of the exoskeleton robot is adjusted according to the current torque information, the expected torque and the expected joint trajectory; specifically, the average difference between the actual joint angle and the expected joint motion angle within a preset time period is calculated; the assistance level is adjusted according to the average difference until the average difference is lower than a preset value; the human-machine interaction force between the wearer's lower limbs and the exoskeleton robot is obtained; wherein the human-machine interaction force is generated by the torque sensor; the gait phase is determined according to the joint motion angle, and the assistance level is determined according to the gait phase and the human-machine interaction force; and the torque is output according to the assistance level for assistance training.

2. The method according to claim 1, characterized in that The step of obtaining a target task and generating a perceptual stimulation model according to the target task includes: Get the target task; generating a visual stimulus model according to the target task; generating an auditory stimulation model according to the target task; A perception stimulation model is generated according to the visual stimulation model and the auditory stimulation model.

3. The method according to claim 1, characterized in that The step of obtaining the walking cadence and current torque information of the wearer when performing the target task through the perceptual stimulation model, wherein the current torque information is generated by the torque sensor at the joint of the exoskeleton robot, comprises: Obtaining the time interval between two adjacent foot strikes on the same leg when the wearer performs a target task using the perceptual stimulation model; Calculating an average value of the time intervals within a preset time and generating a walking cadence based on the average value; Obtain the current torque information of the wearer.

4. The method according to claim 3, characterized in that Before the step of obtaining the time interval between two adjacent foot strikes on the same leg of the wearer when performing the target task through the perception stimulation model, the method further includes: The equipment weight is balanced according to the weight reduction algorithm.

5. A control device for an exoskeleton robot, characterized in that: The control device of the exoskeleton robot implements the steps of the control method of the exoskeleton robot according to any one of claims 1 to 4, including: A task acquisition module, configured to acquire a target task and generate a perceptual stimulus model based on the target task; A human-computer interaction module, configured to obtain the walking cadence and current torque information of the wearer when performing the target task through the perceptual stimulation model; wherein the current torque information is generated by torque sensors at the joints of the exoskeleton robot; an expected motion generation module, configured to generate an expected torque based on the walking cadence; specifically, generate an expected joint motion speed based on the walking cadence; generate an expected joint motion angle based on the expected joint motion speed; and generate an expected torque and an expected joint trajectory based on the current joint motion angle and the expected joint motion angle; The assistance level adjustment module is used to adjust the assistance of the exoskeleton robot based on the current torque information and the expected torque; specifically, calculate the average difference between the actual joint angle and the expected joint movement angle within a preset time period; adjust the assistance level based on the average difference until the average difference is lower than a preset value; obtain the human-machine interaction force between the wearer's lower limbs and the exoskeleton robot; wherein the human-machine interaction force is generated by the torque sensor; determine the gait phase based on the joint movement angle, and determine the assistance level based on the gait phase and the human-machine interaction force; output the torque according to the assistance level to perform assistance training.

6. An electronic device, characterized in that: The device comprises a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the steps of the control method of the exoskeleton robot according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the exoskeleton robot control method according to any one of claims 1 to 4.

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