Hip joint exoskeleton robot anti-falling detection method, system, equipment and medium

Abstract

The invention provides a hip joint exoskeleton robot anti-falling detection method, and relates to the technical field of exoskeleton robots, and the method comprises the following steps: setting a hip joint angle threshold value of a robot; collecting a hip joint real-time angle value of the robot; based on the collected hip joint real-time angle value and the hip joint angle threshold value, whether leg binding of the robot fails or not is judged; and in response to failure of leg binding of the robot, stopping applying forward current to a motor of the robot, applying reverse current to the motor of the robot according to the real-time angle value of the hip joint and the angle threshold value of the hip joint, and when it is detected that the leg binding is in a falling state and the rotation angle of the leg rod exceeds the set angle threshold value, stopping applying forward current to the motor of the robot. The motor timely provides reverse current to counteract inertia of upward movement of the leg rod, the leg rod is prevented from hitting the abdomen, the motor stops assisting when the leg rod falls back to the angle threshold value, the leg rod is prevented from hitting the thigh, and the robot can be used simply, efficiently, conveniently and safely.

Description

Technical Field

[0001] The present invention relates to the technical field of exoskeleton robots, and particularly to a name. Background Art

[0002] As a complex human-machine interaction system, exoskeleton robots involve the intersection of multiple disciplines such as ergonomics, mechanical structures, kinematics, and biomechanics, and involve the integration of technologies such as human-machine integration, intelligent control, and human motion intention recognition. In the field of rehabilitation training, exoskeletons can assist patients with limited mobility in exercising or recovering their mobility, and assist caregivers in nursing operations; in the field of fire rescue, exoskeletons can help firefighters reach the rescue site faster; in the industrial field, exoskeletons can achieve walking and bending for carrying assistance, reducing the work burden. Traditional exoskeletons are mostly based on rigid structures, facing practical problems such as heavy equipment, slow rotation, and lack of comfort. Their lightweight design and human intention recognition and perception still need to be further optimized, and they cannot be widely used in actual application scenarios.

[0003] Traditional exoskeleton robots mostly use fixed gait recognition methods for assistance, with limited adaptability and weak adaptability. When the human body is incompatible with the device or the binding falls off, the machine still moves according to the original motion mode, and the leg rod will continue to swing up and down. When the set assistance is large, it may cause injury to the wearer's limbs.

[0004] The anti-detachment detection of mainstream devices in the market mostly uses detection devices such as infrared distance sensors, sets the absolute value of the distance threshold, and judges whether the binding has fallen off and whether the leg rod has separated from the thigh by comparing with the absolute value read by the sensor in real time. This method requires adding an additional sensor system, resulting in the complication of gait recognition. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for detecting anti-detachment of a hip exoskeleton robot to achieve rapid detection of the detachment of the robot's binding.

[0006] To achieve the above purpose, the present invention provides the following technical solution: A method for detecting anti-detachment of a hip exoskeleton robot, comprising the following steps: setting a hip joint angle threshold of the robot; collecting the real-time hip joint angle value of the robot; judging whether the leg binding of the robot fails based on the collected real-time hip joint angle value and the hip joint angle threshold; in response to the failure of the leg binding of the robot, stopping applying a positive current to the motor of the robot, and applying a reverse current to the motor of the robot according to the real-time hip joint angle value and the hip joint angle threshold.

[0007] Further, setting the hip joint angle threshold of the robot includes: obtaining the range of human hip joint activities of the user of the robot at different movement speeds; setting the hip joint angle threshold of the robot according to the range of human hip joint activities of the user of the robot at different movement speeds.

[0008] Further, the calculation formula for the reverse current is:

[0009] i = k·|θ - θ max |

[0010] where, i is the value of the reverse current, k is a proportionality constant, θ is the real-time hip joint angle value of the robot, and θ max is the hip joint angle threshold.

[0011] Further, the movement speed range of the user of the robot is 1 km / h - 16 km / h.

[0012] Further, collecting the real-time hip joint angle value of the robot includes: obtaining the rotation angle of the robot motor; calculating the real-time hip joint angle value of the robot according to the rotation angle of the robot motor.

[0013] Further, after applying the reverse current to the motor of the robot according to the real-time hip joint angle value and the hip joint angle threshold, the following steps are further included; determining whether the real-time hip joint angle value is less than the hip joint angle threshold; in response to the real-time hip joint angle value being less than the hip joint angle threshold, stopping applying the reverse current to the motor of the robot.

[0014] Further, after stopping applying the reverse current to the motor of the robot, the following steps are further included: determining whether the robot is firmly strapped; when the robot is firmly strapped, enabling the robot to continue working.

[0015] On the other hand, a hip exoskeleton robot anti-disconnection detection system is provided, including: an angle threshold setting module for setting the hip joint angle threshold of the robot; a joint angle acquisition module for acquiring the real-time hip joint angle value of the robot; a comparison module for comparing the acquired real-time hip joint angle value with the hip joint angle threshold; a first motor control module for applying a reverse current to the motor of the robot according to the real-time hip joint angle value and the hip joint angle threshold in response to the real-time hip joint angle value not being less than the hip joint angle threshold.

[0016] On the other hand, an electronic device is provided, and the electronic device includes: a processor and a memory for storing executable instructions of the processor; wherein, the processor is configured to execute the above anti-disconnection detection method.

[0017] On the other hand, a computer-readable storage medium is provided. At least one instruction, at least one program, a code set, or an instruction set is stored in the computer-readable storage medium. The at least one instruction, at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the above-mentioned anti-disconnection detection method.

[0018] Analysis shows that the present invention discloses an anti-disconnection detection method for a hip exoskeleton robot. The present invention can achieve automatic anti-disconnection detection and emergency assistance stop protection. When it is detected that the leg binding is in a detached state and the rotation angle of the leg rod exceeds a set angle threshold, the motor timely provides a reverse current to counteract the inertia of the upward movement of the leg rod, preventing the leg rod from hitting the abdomen. When the leg rod falls back within the angle threshold, the motor stops assisting to prevent the leg rod from hitting the thigh. Without additional complex sensor control, it is simple and efficient, convenient and safe to realize the use of the robot. Description of the Drawings

[0019] The specification drawings forming a part of this application are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. Among them:

[0020] Figure 1 Schematic structural diagram of a hip exoskeleton robot according to an embodiment of the present invention.

[0021] Figure 2 Schematic diagram of the range of motion of the human hip joint according to an embodiment of the present invention.

[0022] Figure 3 Schematic diagram of the angle curve of the hip joint according to an embodiment of the present invention.

[0023] Figure 4 Flowchart of an embodiment of the present invention.

[0024] Figure 5 Simulation diagram of the speed change curve according to an embodiment of the present invention.

[0025] Figure 6 Simulation diagram of the torque change curve according to an embodiment of the present invention.

[0026] Figure 7 Simulation diagram of the anti-disconnection detection algorithm recognition curve according to an embodiment of the present invention.

[0027] Description of the reference numerals: 1. Back group; 2. Hip joint drive assembly; 3. Leg component. Detailed Embodiments

[0028] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments. Each example is provided by way of explanation of the present invention rather than a limitation of the present invention. In fact, those skilled in the art will appreciate that modifications and variations can be made to the present invention without departing from the scope or spirit thereof. For example, features shown or described as part of one embodiment can be used in another embodiment to yield yet another embodiment. Accordingly, it is intended that the present invention cover such modifications and variations that fall within the scope of the appended claims and their equivalents.

[0029] In the description of the present invention, the orientation or positional relationship indicated by terms such as "longitudinal", "transverse", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention rather than requiring the present invention to be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of the present invention. The terms "connected", "connected to", and "disposed" used in the present invention should be understood in a broad sense. For example, it can be a fixed connection or a detachable connection; it can be directly connected or indirectly connected through an intermediate component; it can be a wired connection, a radio connection, or a wireless communication signal connection. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to specific circumstances.

[0030] One or more examples of the present invention are shown in the accompanying drawings. The detailed description uses numerical and alphabetical labels to refer to features in the drawings. Similar or like labels in the drawings and the description have been used to refer to similar or like parts of the present invention. As used herein, terms such as "first", "second", "third", and "fourth" may be used interchangeably to distinguish one component from another and are not intended to indicate the position or importance of individual components.

[0031] As Figure 1 shown, according to an embodiment of the present invention, a method for detecting anti-disengagement of a hip exoskeleton robot is provided.

[0032] Specifically, the hip exoskeleton robot, as a carrier for implementing the anti-disengagement detection method, includes a back component 1 that fits the wearer's waist, a hip joint drive component 2, leg components 3 on both the left and right sides, an inner and outer housing component that fits the curve of the human spine, and straps for convenient wearing. As Figure 1 shown, the exoskeleton structure design refers to the physiological structure and gait movement mechanism of the human hip joint, and determines the drive mode and degrees of freedom of the exoskeleton based on the degrees of freedom and movement ranges of each joint of the human body.

[0033] Specifically, the motor encoder is a magnetic linear encoder built into the motor of the hip joint drive assembly 2, which can detect joint angle values and angular acceleration values. The electronic system of the robot, such as the main control board, is located in the back assembly 1 at the rear waist of the human body. The main control board is equipped with an IMU (Inertial Measurement Unit) inertial sensor, which can convert the measured motion parameters into position, posture, and speed information during human movement for identifying the motion gait. The motor encoder can be used as an angle measurement device to record and measure the change angle of the magnetic material, and the main control board processes the output signal of the motor encoder to obtain the measured angle value.

[0034] Specifically, the robot collects gait motion data of the user's usage habits according to the motion characteristics of the human body during running and bending to carry objects. It obtains the position information of the hip joint drive assembly 2 using the three-direction angular velocity and angular acceleration data real-time feedback by the IMU inertial sensor, calculates the user's posture, speed, and displacement information, realizes gait perception, predicts the user's next moment action, and compares the predicted assist parameter at the next moment with the actual assist parameter at the next moment. If a large deviation occurs, it is regarded as abnormal information. Since the leg assembly 3 is connected to the motor through a connecting piece, the rotation angle of the leg assembly 3 is the rotation angle of the motor. The hip joint rotation angle of the human body can be calculated from the rotation angles of the motors on both sides of the hip joint drive assembly 2.

[0035] It can be understood that the main way for the hip exoskeleton robot to transmit power is to actively drive through the hip joint drive assembly 2 to drive the leg assembly to swing up and down. The hip joint drive assembly 2 is controlled by the main control board. The real-time angle value of the robot's hip joint can be understood as the real-time angle value of the hip joint drive assembly 2. The hip joint drive assembly 2 can be a driving mechanism such as a motor. The straps on the leg tightly bind the human thigh and the leg assembly 3. The binding effect directly affects the power transmission efficiency. If the binding falls off, not only will the power fail, but the swinging leg assembly 3 will also cause harm to the human body.

[0036] Specifically, generally, the motion speed range of the robot's user is 1 km / h - 16 km / h.

[0037] The anti-detachment detection method of the present invention specifically includes the following steps:

[0038] Step S101, set the hip joint angle threshold of the robot;

[0039] Step S102, collect the real-time hip joint angle value of the robot;

[0040] Step S103, based on the collected real-time hip joint angle value and the hip joint angle threshold, judge whether the leg binding of the robot fails;

[0041] Step S104, in response to the failure of the leg binding of the robot, stop applying positive current to the motors of the robot, and apply reverse current to the motors of the robot according to the real-time hip joint angle value and the hip joint angle threshold.

[0042] The above steps will be introduced below in combination with specific embodiments.

[0043] First, regarding step S101, set the hip joint angle threshold of the robot;

[0044] The above setting of the hip joint angle threshold of the robot includes:

[0045] Obtain the range of human hip joint activities of the user of the robot at different movement speeds;

[0046] Set the hip joint angle threshold of the robot according to the range of human hip joint activities of the user of the robot at different movement speeds.

[0047] It can be understood that the hip joint can rotate inwards and outwards around the axis direction, abduct and adduct in the coronal plane, and flex and extend in the sagittal plane. It can be understood that with the hip joint as the center, bending forward towards the front of the human body is hip flexion, and extending backward is hip extension;

[0048] Specifically, the range of human movement angle activities is shown in Table 1. It can be seen from the table that the range of human hip joint activities includes the hip flexion angle α (hip flexion) and the hip extension angle β (hip extension). Among them, the limit activity range of the hip flexion angle α is 0° - 130°; the limit activity range of the hip extension angle β is -20° to 0°. The hip flexion angle α (hip flexion) and the hip extension angle β (hip extension) are the range of rotation angles of the human hip joint.

[0049] Table 1 Table of the range of human hip joint activities

[0050] Joint movement Normal range of joint motion Limit range of joint motion Hip flexion angle α 0°-60° 0°-130° Hip extension angle β -10°-0° -20°～0°

[0051] As Figure 2 shown, when a human walks normally, the maximum flexion position of the hip joint is the position where the knee is closest to the abdomen on the sagittal plane. Then the maximum angle of the hip flexion angle α is α max , and the maximum extension position is the position where the knee is farthest from the abdomen on the sagittal plane. The maximum angle of the hip extension angle β is β max .

[0052] Specifically, setting the hip joint angle threshold of the robot generally requires the following steps: Through gait motion simulation, establish a coordinate system with the hip joint as the rotation center, observe the changes in the bending angles of the left and right leg hip joints within a one-second motion cycle, and draw the angle curves of the hip joint during walking and running. As Figure 3As shown Figure 3 In a gait motion simulation conducted in an experiment, a coordinate system is established with the hip joint as the rotation center. In the coordinate system, the abscissa represents the motion time, and the ordinate represents the angle change. The red curve and the blue curve respectively correspond to the hip joint flexion angle changes of the right leg and the left leg in the normal walking state, and the green curve and the pink curve respectively correspond to the hip joint flexion angle changes of the right leg and the left leg in the running state. The hip joint flexion angle ranges from -19° to 49° in both states, within the human limit motion range, which is used as a reference for setting the angle threshold.

[0053] It can be understood that when the motor output is constant, if the angle of the leg rod following the user's leg lift is less than the hip joint angle threshold, it is judged that the wearing is normal; if the lift angle is greater than the hip joint angle threshold, it is judged as abnormal, that is, the leg binding of the robot is in a detached state.

[0054] In some embodiments, in order to obtain the angle thresholds of different users more personalized and objectively, starting from the state where the user stands vertically, different speeds of 1 km / h - 16 km / h can be set on the treadmill. The user wears the hip exoskeleton robot to run, and the limit values of the hip joint flexion angle α and the hip joint extension angle β of the user at different speeds and the leg lift frequency are collected in real time, and the hip joint angle threshold is obtained through comprehensive analysis.

[0055] It involves step S102 of collecting the real-time angle value of the hip joint of the robot;

[0056] Specifically, the above-mentioned collection of the real-time angle value of the hip joint of the robot specifically includes: obtaining the rotation angle of the robot motor; calculating the real-time angle value of the hip joint of the robot according to the rotation angle of the robot motor.

[0057] It can be understood that since the hip joint drive assembly 2 is a motor, the hip joint drive assembly 2 is also equipped with a joint sensor and a motor encoder. The motor encoder can obtain the displacement physical quantity of the motor. Therefore, by obtaining the working data of the motor encoder, the rotation angle of the hip joint robot can be judged. The above-mentioned joint sensor usually includes a rotation angle sensor and a speed sensor. The rotation angle sensor can be used to accurately measure the rotation angle of the joint to determine the specific position of the joint; the speed sensor can be used to measure the speed of the joint movement.

[0058] It involves step S103 of judging whether the leg binding of the robot fails based on the collected real-time angle value of the hip joint and the hip joint angle threshold;

[0059] Specifically, when in use, after the user wears the exoskeleton robot, the adaptive recognition and learning and memory recognition of the robot are carried out simultaneously. The aforementioned adaptive recognition and learning and memory recognition are the common learning and memory functions of existing exoskeleton robots. First, collect the joint angles of the joint sensors and motor encoders of the robot to identify the user's gait. Gait refers to actions such as the user walking, running, going up and down stairs, etc., analyze the user's movement intentions such as walking, running, going up and down stairs, etc., and predict the movement characteristics of the next time period for different typical gait information by setting a movement time period (usually 1 second). The control strategy enables the hip joint drive assembly 2 to output a reasonable torque. Secondly, for each user during the first use, collect the sensor data of their normal walking, running, and climbing stairs, and add the adaptive recognition mode. The above-mentioned adaptive mode means that currently existing robots can adaptively recognize the user's movement state and switch between states without switching the mode selection button of the robot. When walking, it can recognize the walking state and implement a walking assistance strategy, and when running, it can also recognize the running state. When key information such as hip joint angle threshold, step frequency magnitude, and step frequency rate is set, collect the flexion and extension limit angles and leg raising frequency of the user at different speeds, comprehensively analyze the angle threshold, and compare it with the average value of the recorded rotation angle values of the motor output component within the last n seconds. The above-mentioned n is usually 2. Read the motor encoder to obtain the real-time rotation angle value of the hip joint drive assembly 2, and comprehensively analyze whether the leg binding fails.

[0060] The above-mentioned collection of the flexion and extension limit angles and leg raising frequency of the user at different speeds, and the comprehensive analysis of the angle threshold are as follows: Through the joint sensor and the motor encoder, obtain the real-time angle data of the hip joint in the flexion and extension directions at a sampling frequency per second (corresponding to the 1-second movement time period), denoted as θ t , where represents the time series (t = 1, 2,..., n, n is the number of sampling points within a time period). Step frequency data: Within a movement time period (1 second), the number of steps of the user, denoted as S f . Leg raising frequency data: Similarly within a 1-second period, the number of leg raising actions of the user, denoted as L f . When the user first uses the exoskeleton robot for movements such as normal walking, running, and climbing stairs, through the adaptive recognition mode, record the flexion limit angle θ max-flex and the extension limit angle θ max-extend of the hip joint in each movement state.

[0061] Within a movement time period, calculate the average value of the hip joint angle as the basic reference value of the angle within this period. The calculation formula is:

[0062]

[0063] where is the average value of the hip joint angle within a movement time period, and n is the number of sampling points within this period.

[0064] is to measure the amplitude of angle change and calculate the fluctuation range of the angle. The standard deviation formula is used to reflect the degree of data dispersion, and the formula is as follows:

[0065]

[0066] The angle fluctuation range can be expressed as where k is a coefficient adjusted according to the actual situation, generally taking values between (1.5 - 3). The larger the k value, the wider the fluctuation range.

[0067] The step frequency reflects the speed of the user's movement and has an important impact on the angle threshold. Define the step frequency influence coefficient α, and the calculation formula is:

[0068]

[0069] where S f is the step frequency of the current movement time period, and S f -avg is the average value of the normal step frequency recorded when the user first used it.

[0070] The leg - lifting frequency is closely related to the movement of the hip joint. Define the leg - lifting frequency influence coefficient β, and the formula is:

[0071]

[0072] where L f is the leg - lifting frequency of the current movement time period, and L f -avg is the average value of the normal leg - lifting frequency recorded when the user first used it.

[0073] Combining the basic angle threshold with the step frequency and leg - lifting frequency influence coefficients, the final angle threshold range is obtained. Taking the flexion direction as an example, the adjusted lower limit θ min-flex and upper limit θ max-flex-adj . The calculation formula is as follows:

[0074]

[0075] Similarly, for the extension direction, the calculation formula for the lower limit θ min-extend is:

[0076]

[0077] For the extension direction, the calculation formula for the upper limit θ max-extend-adj is:

[0078]

[0079] Typically, when a user first uses an exoskeleton robot, it is necessary to activate the adaptive recognition mode. During normal walking, running, stair climbing and other movements, data such as joint angles, step frequencies, and leg-lifting frequencies are completely recorded, and parameters such as the limit angles, average normal step frequencies, and average normal leg-lifting frequencies in each movement state are calculated as the basic data for subsequent calculations. During the subsequent use of the user, after each movement time period (1 second) ends, immediately according to the above calculation formula, combined with the data collected in the current period and the basic data recorded during the first use, calculate the angle threshold range in the current movement state. If it is detected that the user's movement state changes (such as switching from walking to running), then recalculate and adjust according to the basic data of the corresponding movement state. The calculated angle threshold information is fed back to the control strategy module of the exoskeleton robot to control the hip joint drive assembly to output a reasonable torque. At the same time, continuously collect the user's movement data, and regularly (such as every certain usage duration or number of times) update and optimize the basic data recorded during the first use to adapt to the effects brought about by changes in the user's physical state or movement habits.

[0080] The above comprehensive analysis of whether the leg binding fails specifically includes: the system continuously records the hip joint rotation angle. When performing the leg binding failure analysis, extract the angle recorded values within the most recent 2 seconds. Since the data acquisition frequency is per second (corresponding to the 1-second movement time period), assume that a total of m angle data are collected within 2 seconds, denoted as θ t-1 , θ t-2 ,... θ t-m . Calculate the average value of these m angle recorded values ), and the calculation formula is:

[0081]

[0082] By reading the motor encoder, obtain the angle value of the real-time rotation of the hip joint drive assembly 2, denoted as θ real-time . The motor encoder can, with the characteristics of high precision and high real-time performance, feedback the actual rotation angle of the joint and provide accurate current state data for analysis.

[0083] Compare the average value of the rotation angle recorded values within the most recent 2 seconds calculated with the previously calculated comprehensive angle threshold (including the lower limit θ min ) and the upper limit (θ max ). At the same time, also compare the real-time angle value θ real-time with the comprehensive angle threshold. The comparison situations are as follows:

[0084] If and θ real-time<θ min , indicating that in the past 2 seconds and the current moment, the hip joint rotation angle is significantly lower than the lower limit of the normal range, and there may be a situation where the leg binding is loose or fails, resulting in abnormal coordinated movement between the exoskeleton robot and the leg. If and θ real-time >θ max , it means that in the past 2 seconds and the current moment, the hip joint rotation angle far exceeds the upper limit of the normal range, and it may also be that the leg binding fails, making the movement of the exoskeleton robot not effectively restricted by the leg. If within [θ min , θ max , but θ real-time exceeds this range, further analysis is needed at this time. It may be an instantaneous external force impact or the initial stage of binding loosening, resulting in abnormal real-time angles, and subsequent data changes need to be continuously monitored.

[0085] Generally, in addition to comparing with the threshold, it is also necessary to analyze the change trend of the angle recorded values and the real-time angle values in the recent 2 seconds. Calculate the change rate of the angle recorded values, calculate the difference Δθ i =θ t-i -θ t-i-1 , and then calculate the average change rate If the average change rate shows violent fluctuations, and the change trend of the real-time angle value θ real-time does not match it (such as the recorded value showing an upward trend while the real-time value suddenly drops and exceeds the normal range), then there may be a problem with the leg binding, resulting in the disruption of the continuity of the angle change.

[0086] Therefore, the judgment rule for leg binding failure is usually: when the situation of " and θ real-time <θ min " or " and θ real-time >θ max " occurs, directly determine that the leg binding fails and activate the safety braking mechanism to stop the movement of the exoskeleton robot to prevent safety accidents caused by binding failure.

[0087] If only within [θ min , θ max , but θ real-timeIf the angle exceeds the range or the angle change trend is abnormal, the system enters the early warning state and continues to collect angle data at a higher frequency (such as every 0.1 second) for monitoring. If more than a certain proportion (such as 60%) of the data exceeds the angle threshold range or the angle change trend continues to be abnormal in the subsequent monitoring n times (n can be set according to actual needs, such as 5-10 times), the leg binding is judged to be invalid, triggering the alarm and braking; if the data returns to normal, the early warning is lifted. The leg binding status is analyzed and judged from multiple dimensions to ensure the safe use of the exoskeleton robot.

[0088] Finally, step S104 is involved. In response to failure of the leg binding of the robot, the forward current is stopped from being applied to the motor of the robot, and a reverse current is applied to the motor of the robot according to the real-time angle value of the hip joint and the hip joint angle threshold; and after step S104, it also includes judging whether the real-time angle value of the hip joint is less than the hip joint angle threshold; then, in response to the real-time angle value of the hip joint being less than the hip joint angle threshold, the reverse current is stopped from being applied to the motor of the robot.

[0089] The specific process of step S104 is as follows Figure 4 As shown, when the leg binding of the robot is judged to be invalid and the rotation angle value of the hip joint drive component 2 is greater than the hip joint angle threshold, a reverse current is provided to the hip joint drive component 2. The specific reverse current size is determined by the rotation angle of the hip joint drive component 2 minus the absolute value of the hip joint angle threshold. The reverse current is used to resist the upward assisting inertia of the leg component 3 to prevent the leg component 3 from hitting the abdomen upward due to inertia, timely protect the human body safety, and improve the stability and reliability of the equipment. When the rotation angle of the leg component 3 falls back to the hip joint angle threshold due to the reverse current provided, the motor stops providing assistance. At this time, the leg component 3 has no assisting effect and falls freely without causing harm to the human body. At this time, the robot returns to the standby mode. After stopping the assisting, the user confirms whether the binding is worn intact. If it is confirmed that it is worn intact, the robot can be restarted to continue assisting. If the rotation angle value of the hip joint drive component 2 is less than the preset hip joint angle threshold, the robot analyzes the motion characteristics predicted by the learning and memory mode and outputs a reasonable torque normally.

[0090] Specifically, the simulation diagram of the present invention is as follows Figure 5 and Figure 6 As shown in the figure, the output torque value can represent the change value of the hip joint bending angle output, the motor movement speed can represent the current change value, the light color in the figure represents the movement state of the left leg hip joint, and the dark color represents the movement state of the right leg hip joint. Figure 7 As can be seen from the figure, the current motion state is detected in real time, where Figure 7The abscissa represents the number of times of edge detection of the belt, and the ordinate represents the threshold value. 0 represents that the current value is not greater than the set threshold value, and 1 represents that the current value is greater than the set threshold value. When the leg strap starts to fall off, at the first moment, that is, at the 487th detection moment, the strap falling off is recognized. The value of the falling-off recognition curve changes from 0 to 1, indicating that the binding falls off at the current moment and is greater than the set threshold value. At this time, the motor movement speed reaches the maximum value, which means that the current change value also reaches the maximum threshold value of the current, and the output torque is also the maximum value within a period. After exceeding the hip joint angle threshold, there is a period of rise and fall. At this time, a reverse current is applied, and the output torque and the motor movement speed decrease from the maximum position. When the rotation angle of the hip joint drive assembly 2 falls back within the hip joint angle threshold, the application of assistance is stopped, and the output torque and the motor movement speed gradually decrease to zero. At this time, the leg assembly 3 falls freely to protect the human body. If it is necessary to restart the robot assistance, the robot needs to be restarted. After completing the routine startup detection of the robot, normal assistance can be provided.

[0091] It can be seen that once the leg binding falls off, this method will immediately monitor and identify the falling-off state, and judge the binding state of the user within a dozen milliseconds. It can realize automatic detection of human movement without installing pressure sensors on the legs and intervene in time to provide protection.

[0092] Specifically, the calculation formula for the above-mentioned reverse current is:

[0093] i = k·|θ - θmax|

[0094] Among them, i is the value of the reverse current, k is the proportionality constant, which can be obtained by multiple actual measurements. θ is the real-time angle value of the hip joint of the robot, and θmax is the hip joint angle threshold. When using, the rotation angle of the hip joint drive assembly 2 measured in real time is θ. When θ ≥ θ max , the control system intervenes to provide the reverse current i. The magnitude of the provided current i is proportional to the obtained angle value of θ - θ max .

[0095] It can be understood that the main basis for the present invention to judge the binding falling off is the relationship between the output force of the motor and the rotation angle of the hip joint drive assembly 2. First, the hip joint angle threshold is set. When the output force of the motor is certain, if the angle at which the leg assembly 3 follows the user's leg to lift is less than the hip joint angle threshold, it is judged that the wearing is normal; if the lifting angle is greater than the hip joint angle threshold, it is judged as abnormal.

[0096] Specifically, when the robot needs to be used again, it can judge whether the robot is firmly bound after stopping applying the reverse current to the motor of the robot; when the robot is firmly bound, the robot continues to work.

[0097] The present invention also discloses a hip exoskeleton robot anti - detachment detection system, including: an angle threshold setting module 201 for setting the hip joint angle threshold of the robot; a joint angle acquisition module 202 for acquiring the real - time hip joint angle value of the robot; a comparison module 203 for comparing the acquired real - time hip joint angle value with the hip joint angle threshold; and a first motor control module 204, which, in response to the real - time hip joint angle value being not less than the hip joint angle threshold, applies a reverse current to the motor of the robot according to the real - time hip joint angle value and the hip joint angle threshold.

[0098] Further, the angle threshold setting module 201 is configured to: obtain the human hip joint activity range of the user of the robot at different movement speeds; and set the hip joint angle threshold of the robot according to the human hip joint activity range of the user of the robot at different movement speeds.

[0099] Further, the system also includes a reverse current calculation module, which is specifically configured to: calculate the reverse current according to the reverse current calculation formula: i = k·∣θ - θ max ∣, where i is the value of the reverse current, k is a proportionality constant, θ is the real - time hip joint angle value of the robot, and θ max is the hip joint angle threshold.

[0100] Further, the movement speed range of the user of the robot is 1 km / h - 16 km / h.

[0101] Further, the joint angle acquisition module 202 includes a motor acquisition unit, which is specifically configured to:

[0102] obtain the rotation angle of the robot motor;

[0103] calculate the real - time hip joint angle value of the robot according to the rotation angle of the robot motor.

[0104] Further, the system also includes a second motor control module, which is specifically configured to:

[0105] judge whether the real - time hip joint angle value is less than the hip joint angle threshold;

[0106] in response to the real - time hip joint angle value being less than the hip joint angle threshold, stop applying the reverse current to the motor of the robot.

[0107] Further, the system also includes a restart module, which is specifically configured to:

[0108] judge whether the robot is firmly strapped;

[0109] when the robot is firmly strapped, make the robot continue to work.

[0110] The anti - detachment detection system further includes a motor encoder and an IMU sensor. The motor encoder can obtain the motion state of the robot's motor, and the IMU sensor can obtain the angular velocity and angular acceleration of the robot during movement. The motor encoder is a magnetic linear encoder.

[0111] The present invention also discloses an electronic device, which includes: a processor and a memory for storing executable instructions of the processor;

[0112] Wherein, the processor is configured to execute the above - mentioned anti - detachment detection method.

[0113] A computer - readable storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to implement the above - mentioned anti - detachment detection method.

[0114] From the above description, it can be seen that the above - mentioned embodiments of the present invention achieve the following technical effects: The present invention can realize automatic anti - detachment detection and emergency boost stop protection. When it is detected that the leg binding is in a detached state and the rotation angle of the leg rod exceeds the set angle threshold, the motor timely provides a reverse current to offset the inertia of the upward movement of the leg rod, avoiding the leg rod hitting the abdomen. When the leg rod falls back within the angle threshold, the motor stops boosting to avoid the leg rod hitting the thigh. Without additional complex sensor control, it is simple and efficient, convenient and safe to realize the use of the robot.

[0115] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for detecting the anti - detachment of a hip exoskeleton robot, characterized in that, It includes the following steps: Set the hip joint angle threshold of the robot; Collect the real-time hip joint angle value of the robot; Based on the collected real-time hip joint angle value and the hip joint angle threshold, determine whether the leg binding of the robot fails; In response to the failure of the leg binding of the robot, stop applying forward current to the motor of the robot, and apply reverse current to the motor of the robot according to the real-time hip joint angle value and the hip joint angle threshold.

2. The hip exoskeleton robot anti-disconnection detection method according to claim 1, characterized in that The setting of the hip joint angle threshold of the robot includes: Obtain the range of human hip joint activities of the user of the robot at different movement speeds; Set the hip joint angle threshold of the robot according to the range of human hip joint activities of the user of the robot at different movement speeds.

3. A hip exoskeleton robot anti - detachment detection method according to claim 1, characterized in that, The calculation formula of the reverse current is: i = k·|θ - θ max | Wherein, i is the value of the reverse current, k is the proportionality constant, θ is the real-time hip joint angle value of the robot, and θ max is the hip joint angle threshold value.

4. A hip exoskeleton robot anti-disconnection detection method according to claim 2, characterized in that The movement speed range of the user of the robot is 1 km / h - 16 km / h.

5. A hip exoskeleton robot anti-disconnection detection method according to claim 1, characterized in that, The collection of the real-time hip joint angle value of the robot includes: Obtain the rotation angle of the motor of the robot; Calculate the real-time hip joint angle value of the robot according to the rotation angle of the motor of the robot.

6. The hip exoskeleton robot anti-disconnection detection method according to claim 1, characterized in that, After applying the reverse current to the motor of the robot according to the real-time hip joint angle value and the hip joint angle threshold, the following steps are further included; Judge whether the real-time hip joint angle value is less than the hip joint angle threshold; In response to the real-time hip joint angle value being less than the hip joint angle threshold, stop applying reverse current to the motor of the robot.

7. A hip exoskeleton robot anti-disconnection detection method according to claim 6, characterized in that, After stopping applying reverse current to the motor of the robot, the following steps are further included: Judge whether the robot is firmly bound; When the robot is firmly bound, make the robot continue to work.

8. A hip exoskeleton robot anti-disconnection detection system, characterized in that It includes: An angle threshold setting module for setting the hip joint angle threshold of the robot; A joint angle collection module for collecting the real-time hip joint angle value of the robot; A comparison module for comparing the collected real-time hip joint angle value with the hip joint angle threshold; A first motor control module for applying reverse current to the motor of the robot according to the real-time hip joint angle value and the hip joint angle threshold in response to the real-time hip joint angle value not being less than the hip joint angle threshold.

9. An electronic device, characterized in that, The electronic device includes: a processor and a memory for storing executable instructions of the processor; Wherein, the processor is configured to execute the anti-disconnection detection method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, At least one instruction, at least one program, a code set or an instruction set is stored in the computer-readable storage medium, and the at least one instruction, at least one program, the code set or the instruction set is loaded and executed by the processor to implement the anti-disconnection detection method according to any one of claims 1-7.

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