Real-time control method and system for hip joint exoskeleton robot

Abstract

The invention discloses a hip joint exoskeleton robot and a real-time control method and system thereof, and belongs to the technical field of exoskeleton robots, and the method comprises the following steps: data preprocessing, reading an angle signal and an angular velocity signal of a hip joint by using a sensor, calculating an angle difference value and an angular velocity difference value of the hip joint, and calculating the angle difference value of the hip joint according to the preprocessed data; the gait process is divided into different stages, foot phase data is estimated according to a classification result and preprocessed data, auxiliary torque of the hip joint is calculated in real time according to the estimated foot phase data and auxiliary parameters, and an execution mechanism of the hip joint exoskeleton robot is controlled according to the auxiliary torque calculated by a torque calculation module; according to the hip joint exoskeleton robot and the real-time control method and system thereof, the auxiliary torque of the hip joint is accurately calculated by processing data input by the sensor in real time, so that the control precision and real-time performance of the hip joint exoskeleton robot are improved, and the hip joint exoskeleton robot is suitable for rehabilitation training and walking assistance.

Description

Technical Field

[0001] The present invention relates to the technical field of exoskeleton robots, and particularly to a hip exoskeleton robot and its real-time control method and control system. Background Art

[0002] With the increasingly severe problem of global population aging, especially in many developed countries, the proportion of the elderly population is continuously increasing, and neurological diseases (such as stroke, spinal cord injury, multiple sclerosis, etc.) are also showing an upward trend year by year. These diseases often lead to limb motor dysfunction, and the daily life and self-care ability of patients are severely affected. With the continuous expansion of the patient group, how to provide effective rehabilitation treatment for these patients has become an urgent problem in the global medical field.

[0003] Traditional rehabilitation means, such as auxiliary devices like crutches and wheelchairs, although can improve the patient's mobility to a certain extent, still have some obvious limitations. For example, crutches cannot provide complete walking support, and wheelchairs limit the patient's activity range, and long-term reliance on these tools may lead to compensatory muscle atrophy and even further affect the joint function recovery. In addition, these traditional devices are difficult to provide personalized rehabilitation training for patients, and the effect is limited.

[0004] In contrast, artificial-assisted walking training, as an innovative rehabilitation means, although can significantly improve the patient's mobility, but its dependence on medical resources is relatively high, such as professional rehabilitation therapists, treatment equipment and treatment sites, etc., resulting in certain limitations in its popularization. Therefore, it is particularly important to explore a new technology that can provide efficient rehabilitation effects and reduce the demand for medical resources.

[0005] In this context, the exoskeleton robot technology emerged. Through the coupling of precise mechanical structures and human movements, exoskeleton robots can not only provide active walking support for patients, but also achieve precise training of the patient's gait through an adjustable mechanical control system. This technology can effectively compensate for the motor function lost by patients due to nerve damage, greatly improving the independence and quality of life of patients. In addition, the emergence of exoskeleton robot technology marks the deep integration of rehabilitation medicine and bioengineering, becoming a research hotspot in this field. Many research institutions and enterprises are committed to the research and development of exoskeleton robots in order to provide more efficient, convenient and personalized rehabilitation treatment plans for a wide range of neurological disease patients.

[0006] Currently, the rehabilitation training of lower limb rehabilitation robots has broad development prospects, but still faces many challenges. During the active rehabilitation training process, it is necessary to stimulate patients' active participation, control biological coordination, and design control strategies that match different training modes. These are all key issues that need to be urgently solved. At present, active rehabilitation training has not been able to fully meet the personalized needs of patients and cannot provide sufficiently diverse solutions, which is an area that future research needs to focus on. From the perspective of the drive system, the development of materials science is relatively slow, and the limitations of motor performance also restrict the wide use of robots in actual application scenarios. In the field of motion awareness recognition, technologies such as electroencephalogram (EEG) and electromyogram (EMG) are still affected by individual differences among patients. In addition, from the perspective of human-machine engineering design, the current rehabilitation robot system still has certain deficiencies in the effective control of neuromuscular activities. At the same time, robot technology cannot fully perceive patients' empathy, resulting in the potential of lower limb rehabilitation robots not being fully utilized during the treatment and rehabilitation process. At this stage, the rehabilitation robot training program still needs clinical support, and a sound application standard, evaluation model, and training program need to be constructed and widely promoted in hospitals and families, which is the future development trend. Summary of the Invention

[0007] The purpose of the present invention is to provide a hip exoskeleton robot, its real-time control method, and control system. By processing the data input by sensors in real time, the auxiliary torque of the hip joint is accurately calculated, thereby improving the control accuracy and real-time performance of the hip exoskeleton robot.

[0008] To achieve the above purpose, the present invention provides a real-time control method for a hip exoskeleton robot, including the following steps:

[0009] S1. Data preprocessing: Use sensors to read the angle signals and angular velocity signals of the left and right hip joints of the wearer in real time, and calculate the hip joint angle difference and angular velocity difference;

[0010] S2. Gait phase classification: According to the preprocessed data, divide the gait process into different phases, and the gait phases include the stance phase and the swing phase;

[0011] S3. Foot phase estimation: According to the classification result and the preprocessed data, estimate the foot phase data;

[0012] S4. Auxiliary torque calculation: According to the estimated foot phase data and auxiliary parameters, calculate the auxiliary torque of the hip joint in real time;

[0013] S5. According to the auxiliary torque calculated by the torque calculation module, control the actuator of the hip exoskeleton robot to achieve auxiliary control of the hip joint.

[0014] Preferably, the specific steps of S1 are as follows:

[0015] S11. Read the angle signals lhip and rhip of the left and right hip joints;

[0016] S12. Calculate the angle difference qf through the following formula nb :

[0017] qf nb = rhip - lhip;

[0018] S13. Read the angular velocity signals lhip vel and rhip vel ;

[0019] S14. Calculate the angular velocity difference dq through the following formula:

[0020] dq = rhip vel - lhip vel .

[0021] Preferably, the specific steps of S2 are as follows:

[0022] S21. Calculate the parameter r for gait phase classification:

[0023]

[0024] S22. Classify the gait phase according to the parameter r, set the threshold rT, and the classification rules are as follows:

[0025] When r > rT, it indicates that the gait change is significant, reset the timer timer r , and enter a new gait phase;

[0026] When r ≤ rT, it indicates that the gait change is not significant, increase the value of the timer timer r , and continue to be in the current gait phase;

[0027] S23. According to the above rules, determine the gait phase flag gflag. When the gait change is significant, the gait phase flag gflag is set to 1, and when the gait change is not significant, the gait phase flag gflag is set to 0.

[0028] Preferably, the foot phase data in S3 includes phase, phase flag, and frequency.

[0029] Preferably, the specific steps of S3 are as follows:

[0030] S31. Initialize the phase flag pha, frequency freg, and phase p;

[0031] S32. Update the phase flag pha and the time interval time according to the gait phase flag gflag itv ;

[0032] S33. Calculate the frequency w of the current gait phase update through the following formula g :

[0033]

[0034] where Δt is the time interval between two phase changes;

[0035] S34. Update the phase p:

[0036] p += w g *DT;

[0037] where DT represents the time step and is used to update the state variables of the system.

[0038] Preferably, the auxiliary parameters described in S4 include the maximum flexion amplitude, the flexion duration, the maximum extension amplitude, and the extension duration.

[0039] Preferably, the specific steps of S4 are as follows:

[0040] S41. Initialize the auxiliary parameters, including the maximum flexion amplitude fle max , the flexion duration fle duration , the maximum extension amplitude ext max and the extension duration max duration ;

[0041] S42. Update the auxiliary parameters according to the current time t;

[0042] S43. Calculate the auxiliary torque hipAss of the hip joint according to the phase p and the auxiliary parameters:

[0043] When the phase p is in the flexion phase:

[0044]

[0045] When the phase p is in the extension phase:

[0046]

[0047] In other cases:

[0048] hipAss = 0.

[0049] A control system for a hip exoskeleton robot, comprising:

[0050] Physiological detection module: It includes an angle sensor for collecting left and right hip joint angle signals and an angular velocity sensor for collecting left and right hip joint angular velocity signals;

[0051] Data processing module: It is used to receive the data collected by the sensor module in real time, preprocess the collected data, and calculate the hip joint angle difference and angular velocity difference;

[0052] Gait classification module: It is used to classify gait phases according to the data processed by the data processing module;

[0053] Phase estimation module: It is used to estimate foot phase data according to the classification result of the gait classification module and the data processed by the data processing module;

[0054] Torque calculation module: It is used to calculate the auxiliary torque of the hip joint in real time according to the foot phase data estimated by the phase estimation module and the preset auxiliary parameters;

[0055] Control module: It is used to control the actuator of the hip exoskeleton robot according to the auxiliary torque calculated by the torque calculation module to achieve auxiliary control of the hip joint.

[0056] A hip exoskeleton robot, comprising:

[0057] Waist flexible binding module: It is used for binding and tightness adjustment of the waist, including a flexible tightening belt. The flexible tightening belt passes through the rectangular guide holes on both sides of the waist carbon fiber composite structure and circumferentially surrounds the wearer's waist. The outer side of the flexible tightening belt is integrated with a self-adhesive magic tape locking device;

[0058] Composite binding module: It is used for fixing the waist flexible binding module, the lower limb linkage module and the legs, and is in the form of a bionic pelvis covering. It is symmetrically provided with a bidirectional through-hole array on both sides and is rigidly connected to the U-shaped bearing platform through fasteners; The hip joint mechanical transmission structure is built in the module to conduct the power of the drive module to the lower limb linkage module;

[0059] Multi-dimensional adjustment mechanism: It is used to match wearers with different waist circumferences, including an integrated operation component, a toothed adjustment card plate and a waist telescopic guide rail. The integrated operation component includes a double-link quick dial, a return spring and a positioning pin;

[0060] Bionic drive module: It is used to drive the leg linkages to drive the wearer forward, including a U-shaped bearing platform, a motor mounting base, a Cyber Gear micro servo motor and a power output end. The Cyber Gear micro servo motor is equipped with a motion perception system and is dynamically coupled with the transmission mechanism through a feedback control circuit. The output shaft transmits a controllable torque to the linkages through a reduction mechanism;

[0061] Lower limb linkage module: used to match the drive of the motor and at the same time adjust the leg binding to match wearers with different thigh widths, including a bionic joint transmission component and an adaptive tightening device. The bionic joint transmission component includes a carbon fiber connecting rod mechanism, and the carbon fiber connecting rod mechanism synchronously drives the bilateral lower limbs to move through a servo drive unit. The adaptive tightening device is provided with a quick adjustment mechanism and an anti-slip surface.

[0062] Therefore, the present invention adopts the above-mentioned hip exoskeleton robot and its real-time control method and control system, and has the following beneficial effects:

[0063] (1) By processing the angle and angular velocity signals of the hip joint in real time and combining gait phase classification and phase estimation, the auxiliary torque of the hip joint can be accurately calculated, improving the control accuracy of the hip exoskeleton robot; adopting real-time data processing and control algorithms, it can quickly respond to changes in human gait and adjust the auxiliary torque in real time to ensure that the actions of the hip exoskeleton robot are synchronized with human movements, enhancing the real-time performance of the system.

[0064] (2) By accurately controlling the auxiliary torque of the hip joint, it can effectively assist patients in rehabilitation training, improve gait, and enhance the rehabilitation effect, having important clinical application value; it is applicable to various rehabilitation training and walking assistance scenarios that require hip joint assistance and has a wide application prospect.

[0065] (3) The hip exoskeleton robot uses a "future 6130 nylon PA6 + carbon fiber" composite material, which has characteristics such as high strength, high stiffness, corrosion resistance, and wear resistance, and manufactures key components (such as the waistband, leg connecting rods, and knee pads) through 3D printing technology. The overall weight is controlled between 1.1 and 1.2 kg, significantly reducing the burden on users; at the same time, based on human body scanning technology and database analysis, leg connecting rods that fit the shape of the thigh are designed, and the walking assistance function is realized in combination with motors, knee baffle plates, and binding systems, ensuring the adaptability and comfort of the connecting rods, optimizing the power transmission efficiency, and improving gait stability and safety.

[0066] (4) Integrates a physiological detection module and an intelligent adjustment algorithm module. The lower limb movement state (including gait analysis, joint angle, muscle load, etc.) is collected in real time through embedded sensors without affecting the user's daily activities, ensuring comfort and efficiency during the rehabilitation process. The intelligent adjustment algorithm module analyzes the monitoring data through a multi-layer control structure, calculates and adjusts the assistance intensity of the exoskeleton in real time, and provides personalized rehabilitation support. In addition, the cloud platform monitoring and interaction module connects the physiological detection and exoskeleton drive modules, stores and analyzes a large amount of motion data in real time, constructs a personalized rehabilitation database, and provides dynamic monitoring and alarm functions through intelligent feedback to help realize a continuously optimized personalized rehabilitation treatment plan.

[0067] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Description of the Drawings

[0068] Figure 1 It is a flowchart of a real-time control method for a hip exoskeleton robot of the present invention;

[0069] Figure 2 It is a schematic diagram of the overall structure of a hip exoskeleton robot of the present invention;

[0070] Figure 3 It is a schematic diagram of the back of the overall structure of a hip exoskeleton robot of the present invention;

[0071] Reference numerals: 1, abdominal flexible binding module; 2, composite binding module; 3, multi-dimensional adjustment mechanism; 4, bionic drive module; 5, lower limb linkage module. Detailed Embodiments

[0072] The following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0073] As Figure 1 shown, the present invention provides a real-time control method for a hip exoskeleton robot, including the following steps:

[0074] S1. Data preprocessing: Use sensors to read the angle signals and angular velocity signals of the left and right hip joints of the wearer in real time, and calculate the hip joint angle difference and angular velocity difference.

[0075] S11. Read the angle signals lhip and rhip of the left and right hip joints;

[0076] S12. Calculate the angle difference qf through the following formula nb :

[0077] qf nb = rhip - lhip;

[0078] S13. Read the angular velocity signals lhip vel and rhip vel ;

[0079] S14. Calculate the angular velocity difference dq through the following formula:

[0080] dq = rhip vel - lhip vel .

[0081] S2. Gait phase classification. According to the preprocessed data, the gait process is divided into different phases, and the gait phases include the stance phase and the swing phase.

[0082] S21. Calculate the parameter r for gait phase classification:

[0083]

[0084] S22. Perform gait phase classification based on the parameter r. Set the threshold rT, and the classification rules are as follows:

[0085] When r > rT, it indicates that the gait change is significant, and reset the timer timer r , enter a new gait phase, which usually indicates that the gait changes from the stance phase to the swing phase or from the swing phase to the stance phase;

[0086] When r ≤ rT, it indicates that the gait change is not significant, and increase the value of the timer timer r , and continue to be in the current gait phase, which usually indicates that the gait remains in the current phase (stance phase or swing phase);

[0087] S23. According to the above rules, determine the gait phase flag gflag. When the gait change is significant, set the gait phase flag gflag to 1, indicating that the gait change is significant. When the gait change is not significant, set the gait phase flag gflag to 0, indicating that the gait change is stable.

[0088] S3. Foot phase estimation. According to the classification result and the preprocessed data, estimate the foot phase data, and the foot phase data includes the phase, the phase flag, and the frequency.

[0089] S31. Initialize the phase flag pha, the frequency freg, and the phase p. The frequency freg is an initial value used to start the phase estimation process;

[0090] S32. Update the phase flag pha and the time interval time according to the gait phase flag gflag itv , time itv is a fixed time interval used for calculating the time step in the gait classification and phase estimation processes, and time is used to update and calculate relevant parameters in each time step; itv to update and calculate relevant parameters;

[0091] S33. Calculate the frequency w of the current gait phase update through the following formula g , w g is dynamically calculated according to the actual gait change and adjusted with the real-time change of the gait, which is used to ensure the accuracy and real-time performance of the phase update:

[0092]

[0093] Among them, Δt is the time interval between two phase changes, which is used to dynamically adjust the frequency of phase update;

[0094] S34, update phase p:

[0095] p+=w g *DT;

[0096] Among them, DT represents the time step, which is used to update the state variables of the system. In discrete time simulation and control, each time step is used to update the state variables of the system, thereby achieving real-time control of the system.

[0097] S4, auxiliary torque calculation, calculates the auxiliary torque of the hip joint in real time according to the estimated foot phase data and auxiliary parameters, and the auxiliary parameters include the maximum flexion range, flexion duration, maximum extension range and extension duration.

[0098] S41, initialization of auxiliary parameters, including the maximum buckling amplitude fl max , flexion duration fl duration , maximum extension ext max and stretch duration max duration ;

[0099] S42, updating auxiliary parameters according to the current time t;

[0100] S43. Calculate the auxiliary torque hipAss of the hip joint according to the phase p and the auxiliary parameters:

[0101] When the phase p is in the buckling stage:

[0102]

[0103] When the phase p is in the extension phase:

[0104]

[0105] In other cases:

[0106] hipAss=0.

[0107] S5. According to the auxiliary torque calculated by the torque calculation module, the actuator of the hip joint exoskeleton robot is controlled to achieve auxiliary control of the hip joint.

[0108] A control system for a hip joint exoskeleton robot, comprising:

[0109] Physiological detection module: includes an angle sensor for collecting left and right hip joint angle signals and an angular velocity sensor for collecting left and right hip joint angular velocity signals;

[0110] Data processing module: used to receive in real time the data collected by the sensor module, preprocess the collected data, and calculate the hip joint angle difference and angular velocity difference;

[0111] Gait classification module: used to classify gait phases according to the data processed by the data processing module, and the gait phases include the stance phase and the swing phase;

[0112] Phase estimation module: used to estimate foot phase data according to the classification result of the gait classification module and the data processed by the data processing module;

[0113] Torque calculation module: used to calculate the auxiliary torque of the hip joint in real time according to the foot phase data estimated by the phase estimation module and the preset auxiliary parameters;

[0114] Control module: used to control the actuator of the hip exoskeleton robot according to the auxiliary torque calculated by the torque calculation module to achieve auxiliary control of the hip joint.

[0115] As Figure 2 and Figure 3 shown, a hip exoskeleton robot includes:

[0116] Waist flexible binding module 1: used for binding and tension adjustment of the waist, including a flexible tightening belt. The flexible tightening belt is made of high-density nylon braided belt. The flexible tightening belt passes through the rectangular guide holes on both sides of the waist carbon fiber composite structure and circumferentially surrounds the wearer's waist, and is precisely fitted with the curved surface of the wearer's abdomen. The outer side of the flexible tightening belt is integrated with a self-adhesive Velcro locking device, supporting dynamic tension adjustment function to ensure the physiological adaptability of the pressure distribution during the wearing process.

[0117] Composite binding module 2: used for fixing the waist flexible binding module, the lower limb linkage module and the legs. It is integrally formed by 3D printing technology based on nylon-based carbon fiber composite material, and is in the shape of a bionic pelvis coating. There are bilateral symmetrically arranged bidirectional through-hole arrays on both sides, and are rigidly connected to the U-shaped bearing platform through standard M5-class fasteners, providing a stable installation base for the lower limb adjustment system; the hip joint mechanical transmission structure is built in the module body for transmitting the power of the drive module to the lower limb linkage module;

[0118] Multi-dimensional adjustment mechanism 3: It is used to match wearers with different waist circumferences, and includes an integrated operation component, a toothed adjustment card board, and a waist telescopic guide rail. The integrated operation component includes a double-link quick shifter, a return spring, and a positioning pin. It adopts a double-point synchronous triggering mechanism. When operating, it applies opposite pressure to the upper and lower shifters, which can release the linkage locking mechanism of the telescopic guide rail, realize continuous adjustment of the wearer's waist circumference parameters, and finally form a multi-stage telescopic positioning function.

[0119] Bionic drive module 4: It is used to drive the leg linkages to drive the wearer forward, and includes a U-shaped bearing platform, a motor mounting base, a CyberGear micro servo motor, and a power output end. The CyberGear micro servo motor is equipped with a multi-dimensional motion perception system. When it detects the wearer's motion intention, it activates the drive unit through a real-time feedback control circuit, and the output shaft forms a dynamic coupling with the bionic joint transmission mechanism to achieve precise power output for motion assistance.

[0120] Lower limb linkage module 5: It is used to match the drive of the motor and at the same time adjust the leg bindings to match wearers with different thigh widths, and includes a bionic joint transmission component and an adaptive tightening device. The bionic joint transmission component includes a carbon fiber linkage mechanism. The carbon fiber linkage mechanism synchronously drives the bilateral lower limbs to move through a servo drive unit. The high-precision servo drive unit outputs a controllable torque to the transmission link through a reduction mechanism to synchronously drive the bilateral lower limbs to move. The adaptive tightening device uses an anti-stretching nylon strap in combination with a quick adjustment mechanism, combined with surface anti-slip texture treatment, which can adapt to the wearing requirements of different anatomical sizes.

[0121] Therefore, the present invention adopts the above-mentioned hip exoskeleton robot and its real-time control method and control system. By processing the data input by the sensor in real time, it accurately calculates the auxiliary torque of the hip joint, thereby improving the control accuracy and real-time performance of the hip exoskeleton robot, and is applicable to rehabilitation training and walking assistance.

[0122] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A real-time control method for a hip joint exoskeleton robot, characterized in that: The following steps are involved: S1, data preprocessing, using sensors to read the angle signals and angular velocity signals of the left and right hip joints of the wearer in real time, and calculating the hip joint angle difference and angular velocity difference; S2, gait phase classification, based on the preprocessed data, the gait process is divided into different phases, including the stance phase and the swing phase; S3, foot phase estimation, estimating foot phase data according to the classification results and preprocessed data; S4, auxiliary torque calculation, calculating the auxiliary torque of the hip joint in real time according to the estimated foot phase data and auxiliary parameters; S5. According to the auxiliary torque calculated by the torque calculation module, the actuator of the hip joint exoskeleton robot is controlled to achieve auxiliary control of the hip joint.

2. The real-time control method of a hip joint exoskeleton robot according to claim 1, characterized in that: The specific steps of S1 are as follows: S11, reading the angle signals lhip and rhip of the left and right hip joints; S12, calculate the angle difference qf by the following formula nb : qf nb =rhip-lhip; S13, read the angular velocity signals lhip of the left and right hip joints vel and rhip vel ; S14. Calculate the angular velocity difference dq using the following formula: dq=rhip vel -lhip vel 。 3. The real-time control method of a hip joint exoskeleton robot according to claim 1, characterized in that: The specific steps of S2 are as follows: S21. Calculate the parameter r for gait phase classification: S22, classify the gait phase according to the parameter r, set the threshold rT, and the classification rules are as follows: When r>rT, it means that the gait changes significantly, and the timer is reset. r , enter a new gait phase; When r≤rT, it means that the gait change is not significant, and the timer is increased. r , continue in the current gait phase; S23. According to the above rules, the gait stage flag gflag is determined. When the gait changes significantly, the gait stage flag gflag is set to 1. When the gait changes insignificantly, the gait stage flag gflag is set to 0.

4. The real-time control method of a hip joint exoskeleton robot according to claim 1, characterized in that: The foot phase data in S3 includes phase, phase flag and frequency.

5. The real-time control method of a hip joint exoskeleton robot according to claim 4, characterized in that: The specific steps of S3 are as follows: S31, initializing the phase flag pha, frequency freg and phase p; S32, updating the phase flag pha and the time interval time according to the gait phase flag gflag itv ; S33, calculate the frequency w of the current gait phase update by the following formula g : Where Δt is the time interval between two phase changes; S34, update phase p: p+=w g *DT; Where DT represents the time step.

6. The real-time control method of a hip joint exoskeleton robot according to claim 1, characterized in that: The auxiliary parameters in S4 include the maximum flexion amplitude, the flexion duration, the maximum extension amplitude and the extension duration.

7. The real-time control method of a hip joint exoskeleton robot according to claim 6, characterized in that: The specific steps of S4 are as follows: S41, initialization of auxiliary parameters, including the maximum buckling amplitude fl max , flexion duration fl duration , maximum extension ext max and stretch duration max duration ; S42, updating auxiliary parameters according to the current time t; S43. Calculate the auxiliary torque hipAss of the hip joint according to the phase p and the auxiliary parameters: When the phase p is in the buckling stage: When the phase p is in the extension phase: In other cases: hipAss=0.

8. A control system for a hip joint exoskeleton robot, applied to a real-time control method for a hip joint exoskeleton robot according to any one of claims 1 to 7, characterized in that: include: Physiological detection module: including an angle sensor for collecting left and right hip joint angle signals and an angular velocity sensor for collecting left and right hip joint angular velocity signals; Data processing module: used to receive data collected by the sensor module in real time, pre-process the collected data, and calculate the hip joint angle difference and angular velocity difference; Gait classification module: used to classify gait phases according to the data processed by the data processing module; Phase estimation module: used to estimate foot phase data according to the classification results of the gait classification module and the data processed by the data processing module; Torque calculation module: used to calculate the auxiliary torque of the hip joint in real time according to the foot phase data estimated by the phase estimation module and the pre-set auxiliary parameters; Control module: used to control the actuator of the hip joint exoskeleton robot according to the auxiliary torque calculated by the torque calculation module, so as to realize auxiliary control of the hip joint.

9. A hip joint exoskeleton robot, characterized in that: include: Waist flexible binding module: used for waist binding and tightness adjustment, including a flexible tightening belt, which passes through the rectangular guide holes on both sides of the waist carbon fiber composite structure and surrounds the wearer's waist circumferentially, and a self-adhesive Velcro locking device is integrated on the outer side of the flexible tightening belt; Composite binding module: used for fixing the waist flexible binding module, lower limb linkage module and legs, in the form of bionic pelvic wrapping, with a two-way through screw hole array symmetrically arranged on both sides, rigidly connected to the U-shaped bearing platform through fasteners; the module body has a built-in hip joint mechanical transmission structure, which is used to transmit the power of the drive module to the lower limb linkage module; Multi-dimensional adjustment mechanism: used to match wearers with different waist sizes, including an integrated operating component, a toothed adjustment card plate and a waist telescopic guide rail, wherein the integrated operating component includes a double-linked quick-release, a return spring and a positioning pin; Bionic drive module: used to drive the leg connecting rod to drive the wearer forward, including a U-shaped load-bearing platform, a motor mounting base, a Cyber ​​Gear micro servo motor and a power output terminal. The Cyber ​​Gear micro servo motor is equipped with a motion sensing system, which is dynamically coupled with the transmission mechanism through a feedback control circuit. The output shaft transmits controllable torque to the connecting rod through a reduction mechanism. Lower limb linkage module: used to match the drive of the motor and adjust the leg binding to match wearers with different thigh widths, including a bionic joint transmission component and an adaptive tightening device. The bionic joint transmission component includes a carbon fiber connecting rod mechanism, which synchronously drives the movement of both lower limbs through a servo drive unit. The adaptive tightening device is provided with a quick adjustment mechanism and an anti-slip surface.

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