A control method and device of an autonomous navigation lower limb robot, a terminal and a medium

Abstract

The application discloses a control method of an autonomous navigation lower limb robot, which comprises the following steps: collecting human gait data to obtain an original gait curve, optimizing the gait in combination with sensor data, and controlling an exoskeleton to perform rehabilitation training; based on lower limb motion parameters and navigation instructions, the speed of a driving wheel is controlled, so that the platform advances to a target direction in cooperation with the lower limb motion parameters, thereby realizing the functions of gait training and autonomous navigation, and the comfort during lower limb rehabilitation training and the experience of a patient are improved, and real-time regulation and control of lower limb motion and autonomous control of a walking direction of the patient during training are realized.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of rehabilitation training devices, and particularly relates to a control method and device of a self-navigation lower limb robot, a terminal and a medium. BACKGROUND

[0002] In the prior art, a variety of lower limb exoskeleton lower limb robots have been designed to help users perform rehabilitation training, and most of them are committed to training the user's ability to walk upright. On the one hand, the existing lower limb rehabilitation lower limb robots provide walking assistance for the user through the lower limb exoskeleton, and on the other hand, the gait of the user when walking is adjusted through the lower limb exoskeleton, so that the user can walk on the ground according to the correct gait.

[0003] At present, lower limb rehabilitation lower limb robots are roughly divided into fixed gait training lower limb rehabilitation lower limb robots and assisted walking lower limb rehabilitation lower limb robots. The fixed gait rehabilitation training lower limb robot mainly drives the user to perform standard gait training at a fixed position through the lower limb exoskeleton, such as the control method of a gait rehabilitation training lower limb robot disclosed in CN103536424A. The assisted walking lower limb rehabilitation lower limb robot provides assistance for the user to walk, combined with a predetermined standard gait, so as to drive the user to walk on the ground according to the standard gait for walking training, which can improve the user's experience during training. At present, most of the assisted walking lower limb rehabilitation lower limb robots can only drive the user to perform straight walking training, and cannot meet the user's demand for actively controlling the walking direction. For example, CN105456004A discloses an exoskeleton type mobile walking rehabilitation training device and method, which drives the lower limbs of the human body to perform walking rehabilitation training in a mobile platform through an exoskeleton type mechanical leg. The platform can be moved by wheels. However, the platform needs to be fixed during training, and only standard gait training can be performed. The platform cannot advance and turn in coordination with the gait during training. SUMMARY

[0004] In order to at least solve one of the problems existing in the prior art, the present application provides a control method of a self-navigation lower limb robot. The present application acquires human gait data to obtain an original gait curve, optimizes the gait in combination with sensor data, controls the exoskeleton to perform rehabilitation training, controls the driving wheel speed based on the lower limb motion parameters and navigation instructions, and makes the platform advance towards the target direction in coordination with the lower limb motion parameters, thereby realizing the functions of gait training and autonomous navigation. The present application can allow the user to perform lower limb rehabilitation training and autonomously control the walking direction during training, can improve the user's comfort during training, and has the function of protecting the user's limbs, thereby providing a new technical direction for the design of lower limb rehabilitation exoskeleton lower limb robots.

[0005] To achieve the object of the present application, a control method of a self-navigation lower limb robot of the present application comprises the following steps:

[0006] The user is guided to gait training according to a standard movement gait by the lower limb robot;

[0007] The safety state detection is performed according to the priority order of the obstacle monitoring feedback, the plantar pressure sensor feedback and the plantar ground clearance feedback, and the movement state of the lower limb robot is adjusted according to the feedback of the safety state detection;

[0008] After the safety state detection ensures that the user does not encounter obstacles and is in a touch-down state, the travel control of the lower limb robot is realized by controlling the rotation of the driving wheels of the lower limb robot;

[0009] The movement parameters of the lower limbs of the user are obtained by the lower limb robot, the horizontal velocity of the plantar of the user is calculated, and the speed of the driving wheels of the lower limb robot is controlled based on the horizontal velocity of the plantar of the user, so that the speed of the driving wheels is consistent with the horizontal velocity of the plantar of the user, and the user can perform straight-line training;

[0010] According to the turning radius r and the horizontal velocity of the plantar of the user, the angular velocity of the turning of the lower limb robot and the speed of the driving wheels on both sides are calculated, and the turning of the lower limb robot is controlled by the speed difference of the driving wheels on both sides, so that the user can turn during straight-line training;

[0011] The target heading angle of the lower limb robot is calculated according to the turning radius and the horizontal velocity of the plantar, and the actual heading angle of the lower limb robot is corrected according to the target heading angle.

[0012] Further, the acquisition method of the standard movement gait comprises the following steps:

[0013] Collecting gait images of normal people, extracting gait information and obtaining a gait movement parameter database;

[0014] Based on the gait movement parameter database, a gait model is established, and a standard movement gait is obtained based on the gait model.

[0015] Further, the safety state detection according to the priority order of the obstacle monitoring feedback, the plantar pressure sensor feedback and the plantar ground clearance feedback, and the adjustment of the movement state of the lower limb robot according to the feedback of the safety state detection, comprises:

[0016] The obstacle monitoring feedback is performed, the external torque received by each joint of the lower limb robot is monitored in real time, when the external torque received by any joint of the lower limb robot exceeds the preset torque threshold, it is determined that an obstacle is encountered, and at this time the lower limb robot is controlled to stop moving;

[0017] When the external torque on the joint of the lower limb robot does not exceed the preset torque threshold, the plantar pressure of the user is monitored in real time, and when the plantar pressure exceeds the preset pressure value, the lower limb robot is controlled to make the plantar pressure lower than the preset pressure value.

[0018] When the plantar pressure does not exceed the preset pressure value, the user's plantar is ensured to be in the ground contact state through the foot-off ground height feedback.

[0019] Further, when the user performs the gait optimization training, the obstacle monitoring feedback has a higher priority than the foot-off ground height feedback, and the foot-off ground height feedback has a higher priority than the plantar pressure sensor feedback.

[0020] Further, before the lower limb robot drives the user's lower limbs to perform gait optimization training according to the standard motion gait, the plantar of the lower limb robot is also ensured to be in the ground contact state.

[0021] Further, the user's plantar is ensured to be in the ground contact state through the foot-off ground height feedback, and the calculation formula of the foot-off ground height is

[0022] h = H - x - d

[0023] x = L1cos·(θ1) + L2cos·(θ1+θ2)

[0024] In the formula, h is the foot-off ground height, H is the distance from the hip joint of the lower limb robot to the ground, d is the distance from the ankle joint of the lower limb robot to the plantar plane, x is the height difference between the hip joint and the ankle joint, L1 is the length of the thigh of the lower limb robot, L2 is the length of the lower leg of the lower limb robot, θ1 is the angle between the thigh of the lower limb robot and the vertical line, and θ2 is the angle between the thigh and the lower leg of the lower limb robot.

[0025] When the foot-off ground height is less than zero, θ1 and θ2 are adjusted to ensure that the foot height remains zero.

[0026] Further, the user's plantar is ensured to be in the ground contact state through the plantar pressure sensor feedback. Specifically, the plantar pressure of the user is monitored in real time through the pressure sensor, and when the plantar pressure exceeds the preset pressure value, the lower limb robot is controlled to make the plantar pressure lower than the preset pressure value.

[0027] Further, the calculation formula of the foot horizontal speed is

[0028]

[0029] In the formula, v f is the foot horizontal speed, v ankleFor the ankle horizontal velocity, L1 is the thigh length of the lower limb robot, L2 is the shank length of the lower limb robot, θ1 is the angle between the thigh and the vertical line of the lower limb robot, θ2 is the angle between the thigh and the shank of the lower limb robot, respectively, the first derivative of θ1 and θ2.

[0030] Further, the user can perform straight training by the following formula: v = -v f , v l = v r = v, wherein v is the moving speed of the lower limb robot, v f is the horizontal velocity of the foot, the forward velocity is positive, and the backward velocity is negative. v l is the left driving wheel speed, and v r is the right driving wheel speed.

[0031] Further, the calculation formula of the angular velocity of the lower limb robot turning and the speed of the left and right driving wheels is

[0032]

[0033] In the formula, ω is the angular velocity, d is the distance between the two feet of the lower limb robot, D is the distance between the two driving wheels of the lower limb robot, r is the turning radius, sign l is the direction variable related to the foot of the lower limb robot, v l is the speed of the left driving wheel, and v r is the speed of the right driving wheel. When the foot and the turning direction are on the same side, the variable sign l is -1, and when the foot and the turning direction are on the opposite sides, the variable sign l is 1. sign m is the direction variable related to the driving wheel, which is -1 when turning left and 1 when turning right.

[0034] Further, the calculation formula of the target heading angle is

[0035]

[0036] In the formula, φ t is the target heading angle, φ0 is the heading angle when starting to walk, and ω(t) is the function of the angular velocity, which describes the angular velocity changing with time t.

[0037] The way of correcting the actual heading angle of the lower limb robot by the target heading angle is

[0038] v l ′ = v l + f(φ t , φ)

[0039] v r ’=v r -f(φ t ,φ)

[0040] wherein, v l is the left drive wheel speed before correction, v r is the right drive wheel speed before correction, v l ' is the left drive wheel speed after correction, v r ' is the right drive wheel speed after correction, phi is the current heading angle, f(phi t ,phi) is an error correction function for calculating drive wheel speed increment.

[0041] Further, the external torque received by the lower limb robot is monitored in real time through the joint torque pressure sensor of the lower limb robot, and the external torque received by the lower limb robot is calculated through the following formula:

[0042] T=T r -T0

[0043] T0 is the torque generated by the weight of the joint, T r is the real-time monitoring torque, and T is the external torque.

[0044] The control device of the autonomous navigation lower limb robot provided by the application comprises:

[0045] The training module drives the user to start training on the spot according to the standard movement gait.

[0046] The movement state adjustment module is used for safety state detection according to the priority order of obstacle monitoring feedback, foot pressure sensor feedback and foot off ground height feedback, and adjusts the movement state of the lower limb robot according to the feedback of safety state detection.

[0047] The advancing control module is used for advancing control of the lower limb robot by controlling the rotation of the drive wheel of the lower limb robot after ensuring that the user does not encounter obstacles and is in the touch ground state through safety state detection.

[0048] The straight training control module is used for acquiring the movement parameters of the lower limbs of the user through the lower limb robot, calculating the horizontal speed of the foot of the user, controlling the speed of the drive wheel of the lower limb robot based on the horizontal speed of the foot of the user, so that the user can perform straight training.

[0049] A turning control module is configured to calculate an angular velocity of the lower limb robot and speeds of left and right driving wheels according to a turning radius r and the horizontal speed of the user's foot, and control the lower limb robot to turn by the speed difference between the left and right driving wheels, so that the user can turn during straight training.

[0050] An actual heading angle correction module is configured to calculate a target heading angle of the lower limb robot according to the turning radius and the horizontal speed of the foot, and correct the actual heading angle of the lower limb robot according to the target heading angle.

[0051] The application also provides a terminal device.

[0052] The application also provides a computer readable storage medium.

[0053] Compared with the prior art, the application can at least achieve the following beneficial effects:

[0054] The application integrates multiple sensors to detect state parameters in real time, and can optimize gait according to the lower limb movement state and stress state of the user during training, thereby improving the comfort during lower limb training. The walking direction is actively controlled according to the user's awareness and state parameters (the user's awareness is a turning instruction and a turning radius, and the state parameters are the movement state of the lower limb exoskeleton, including the speed of the foot, whether an obstacle is encountered, and whether the foot touches the ground), the obstacle is bypassed, the target point is autonomously reached during indoor and outdoor training, the participation and self-confidence are increased, the work intensity of the therapist is reduced, and a new technical direction is provided for the design of the lower limb rehabilitation training exoskeleton lower limb robot. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 A structural diagram of the autonomous navigation lower limb robot provided by the embodiment of the application.

[0056] Figure 2 A control diagram of the control method of the autonomous navigation lower limb robot provided by the embodiment of the application.

[0057] Figure 3 A step flowchart of the control method of the autonomous navigation lower limb robot provided by the embodiment of the application. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the application.

[0059] The core of the present application is to provide a control method of an autonomous navigation lower limb robot. When the lower limb of the exoskeleton touches the ground for lower limb walking training, the movement, stop, moving speed and moving direction of the lower limb robot are controlled based on the lower limb movement parameters of the exoskeleton lower limb robot, so as to ensure that the exoskeleton foot bottom and the ground are relatively static when the lower limb robot moves, and the state that the body movement is consistent with the lower limb movement during walking is realized.

[0060] For the convenience of understanding, the structure of the lower limb robot is introduced here. Please refer to Figure 1 , Figure 1 The structure diagram of an autonomous navigation lower limb robot provided by the embodiment of the present application.

[0061] An autonomous navigation lower limb robot comprises:

[0062] A left hip joint structure 1 comprises a motor for controlling the rotation of a left thigh structure 1.1 and a torque sensor for measuring the output torque of the motor.

[0063] A left knee joint structure 2 comprises a motor for controlling the rotation of a left shank structure 2.1 and a torque sensor for measuring the output torque of the motor.

[0064] A right hip joint structure 4 comprises a motor for controlling the rotation of a right thigh structure 4.1 and a torque sensor for measuring the output torque of the motor.

[0065] A right knee joint structure 5 comprises a motor for controlling the rotation of a right shank structure 5.1 and a torque sensor for measuring the output torque of the motor.

[0066] A left driving wheel 3 and a right driving wheel 6 are installed at the left front and right front of the frame of the lower limb robot, and are used to realize the movement of the lower limb robot. The rotation direction and rotation speed of the left driving wheel 3 are adjustable, and the rotation direction and rotation speed of the right driving wheel 6 are adjustable.

[0067] The forward speed and forward direction of the lower limb robot are controlled by adjusting the speed of the left driving wheel 3 and the right driving wheel 6.

[0068] A left foot bottom structure 7 is installed with a foot bottom pressure sensor for detecting the foot bottom pressure of the left foot bottom structure 7 and detecting whether the left foot bottom structure 7 touches the ground.

[0069] Right foot structure 8, which is installed with a foot pressure sensor for detecting the foot pressure of the right foot structure 8 and detecting whether the right foot structure 8 touches the ground.

[0070] Rear universal wheels 9, which are used to rotate freely following the movement of the lower limb robot, and there are two of them, installed on the left and right rear of the frame of the lower limb robot.

[0071] The lower limb robot is also provided with an electronic compass, which is used to measure the orientation direction of the lower limb robot.

[0072] Joint torque, which includes the joint motor output torque of the hip joint and knee joint of the lower limb robot.

[0073] Joint angle, which includes the angle θ1 between the thigh and the vertical line and the angle θ2 between the thigh and the calf of the lower limb robot.

[0074] Lower limb robot height, which is the vertical distance between the hip joint of the lower limb robot and the ground.

[0075] Thigh and calf length, which includes the thigh length L1 and the calf length L2.

[0076] End foot position, which is the foot height from the ground.

[0077] Joint angular velocity, which includes the hip joint motor rotation angular velocity and the knee joint motor rotation angular velocity of the lower limb robot.

[0078] Travel direction instruction, which is the direction instruction input by the user through the input device, including the turning direction and the turning radius. When the direction instruction is straight, v l = v r = v.

[0079] Travel control, which is achieved by controlling the left and right wheel speeds and executing the direction feedback.

[0080] Referring to Figure 2 and Figure 3 , the present application provides a control method for an autonomous navigation lower limb robot, mainly including gait control and travel control, which includes the following steps:

[0081] Step 1: collect normal gait images, extract effective gait information, and obtain a gait motion parameter database; based on the gait motion parameter database, a gait model is established, and a standard motion gait is obtained based on the gait model, and the lower limb robot can drive the user to start training on the spot according to the standard motion gait.

[0082] In one embodiment of the present application, the step specifically includes the following sub-steps:

[0083] Step 101: collect walking gait videos of multiple people through a camera, and extract effective gait information from the walking gait videos, wherein the gait information includes time stamp, hip joint angle and knee joint angle;

[0084] Step 102: establish a gait motion parameter database through the gait information;

[0085] The gait motion parameter database includes multiple time points and corresponding knee joint angles and hip joint angles at each time point.

[0086] Step 103: obtain a gait model through Fourier transform of the knee joint angle and hip joint angle data of the gait motion parameter database, and the gait model is a function of joint angle with respect to time;

[0087] In this sub-step, the hip joint angle and knee joint angle can be calculated at any time according to the gait model.

[0088] Step 104: input a time parameter to the gait model, and obtain a standard motion gait after calculation by the gait model, wherein the parameters of the standard motion gait are hip joint angle and knee joint angle;

[0089] Step 105: the lower limb robot drives the user to train according to the obtained standard gait.

[0090] It can be understood that in other embodiments, a publicly disclosed standard motion gait can also be directly used.

[0091] Step 2: perform safety state detection in the order of priority of obstacle monitoring feedback, foot pressure sensor feedback and foot off-ground height feedback, and adjust the motion state of the lower limb robot according to the feedback of safety state detection.

[0092] In order to protect the safety of the user, the priority of safety state detection is obstacle monitoring feedback, foot pressure sensor feedback and foot off-ground height feedback in turn. Among them:

[0093] Safety state detection 1: obstacle monitoring is performed to determine whether an external resistance is encountered during the movement of the lower extremity exoskeleton lower extremity robot. When the external torque received by any joint of the lower extremity robot exceeds a preset torque threshold, it is determined that an obstacle is encountered, and the lower extremity robot is controlled to stop moving, and the detection of the foot pressure and the foot clearance height is no longer performed.

[0094] Safety state detection 2: safety state detection is performed according to the feedback of the foot pressure sensor to ensure that the user's foot is in contact with the ground. Specifically, the foot pressure of the user is monitored in real time by the foot pressure sensor. When the foot pressure exceeds the preset pressure value, the lower extremity robot is controlled to make the foot pressure less than the preset pressure value. When the external torque received by any joint of the lower extremity robot is less than the preset torque threshold, but the foot pressure is greater than the preset pressure value, the detection of the foot clearance height is no longer performed.

[0095] Safety state detection 3: safety state detection is performed according to the feedback of the foot clearance height to ensure that the user's foot is in contact with the ground.

[0096] Step 2 specifically includes:

[0097] First, obstacle monitoring feedback is performed. The external torque received by each joint of the lower extremity robot is monitored in real time by the torque sensor on the output shaft of each joint motor of the lower extremity robot. The external torque received by each joint of the lower extremity robot is calculated by the following formula:

[0098] T = T r - T0

[0099] T0 is the torque generated by the self-weight of each joint of the lower extremity robot (including left and right hip joints and left and right knee joints), T r is the real-time monitored torque of the joint, and T is the external torque of the joint.

[0100] When the external torque received by any joint of the lower extremity robot exceeds a preset torque threshold, it is determined that an obstacle is encountered, and the lower extremity robot is controlled to stop moving at this time;

[0101] When the external torque received by the joint of the lower extremity robot does not exceed the preset torque threshold, the foot pressure sensor feedback is used to ensure that the user's foot is in contact with the ground. Specifically, the foot pressure of the user is monitored in real time by the foot pressure sensor. When the foot pressure exceeds the preset pressure value, the lower extremity robot is controlled to make the foot pressure less than the preset pressure value.

[0102] When the plantar pressure does not exceed the preset pressure value, the foot-off ground height feedback is used to ensure that the user's foot is in the ground-touching state, and specifically, the foot-off ground height is calculated by the following formula:

[0103] x=L1cos·(θ1)+L2cos·(θ1+θ2)h=H-x-d

[0104] In the formula, x is the height difference between the hip joint and the ankle joint, L1 is the thigh length of the lower limb robot, L2 is the calf length of the lower limb robot, θ1 is the angle between the thigh of the lower limb robot and the vertical line, θ2 is the angle between the thigh and the calf of the lower limb robot, H is the distance from the hip joint of the lower limb robot to the ground, d is the distance from the ankle joint of the lower limb robot to the foot bottom plane, and h is the foot-off ground height. The first derivatives of θ1 and θ2 are θ1' and θ2', respectively.

[0105] When the foot-off ground height is less than zero, θ1 and θ2 are adjusted to ensure that the foot-off ground height remains zero.

[0106] Step 3: After the safety state detection ensures that the user does not encounter an obstacle and is in the ground-touching state, the advancing control of the lower limb robot is realized by controlling the rotation of the driving wheel of the lower limb robot.

[0107] Step 4: The motion parameters of the lower limbs of the user are obtained by the lower limb robot, the horizontal speed of the foot bottom of the user is calculated, the speed of the driving wheel of the lower limb robot is controlled to be consistent with the horizontal speed of the foot bottom of the user based on the horizontal speed of the foot bottom of the user, and the user can start the straight-line training.

[0108] The end foot bottom speed of the lower limb robot is the horizontal speed of the foot bottom, the horizontal speed of the foot bottom is equivalent to the horizontal speed of the ankle joint, and the horizontal speed of the foot bottom can be calculated according to the calf length and the joint angular velocity.

[0109] In one embodiment of the present application, the thigh length L1, the calf length L2, the angle θ1 between the thigh and the vertical line, and the angle θ2 between the thigh and the calf of different users are obtained by the lower limb robot, and the horizontal speed of the ankle joint of the user, that is, the horizontal speed of the foot bottom v f , is calculated.

[0110]

[0111] In the formula, v ankle is the horizontal speed of the ankle joint.

[0112] Based on the horizontal speed of the foot bottom of the user, the speed of the driving wheel of the lower limb robot is controlled to be consistent with the horizontal speed of the foot bottom of the user, so that the user starts to move straight, and specifically, the advancing speed of the lower limb robot is calculated according to the end horizontal speed of the foot bottom: v=-vf , v l = v r = v, in the formula, v is the moving speed of the lower limb robot, v f is the horizontal speed of the foot bottom, the speed is positive forward and negative backward, v l is the speed of the left drive wheel, v r is the speed of the right drive wheel.

[0113] Step 5: According to the turning radius r and the horizontal speed of the foot bottom of the user, the angular velocity of the lower limb robot turning and the speed of the left and right drive wheels are calculated, and the lower limb robot is controlled to turn by the speed difference of the left and right drive wheels, so that the user realizes turning in the straight training process:

[0114] Specifically, the following formula is used: Wherein, ω is the angular velocity, d is the distance between the two feet of the lower limb robot (the horizontal distance between the two foot bottom structures), D is the wheel base of the left and right drive wheels of the lower limb robot (the distance between the two drive wheels), r is the turning radius, sign l is the direction variable related to the foot of the lower limb robot, which is -1 when the foot is on the same side as the turning direction, and 1 when the foot is on the opposite side of the turning direction, sign m is the direction variable related to the drive wheel, which is -1 when turning left and 1 when turning right.

[0115] The larger the turning radius, the closer to straight line, the smaller the turning radius, the faster the turning, and the minimum turning radius is limited in the algorithm (according to the experience of the developer and the size of the machine, a minimum turning radius value is set in the algorithm in advance, the user cannot be less than this value when changing the turning radius, in some embodiments of the present application, the value is set to 0.5m, and in other embodiments, it can also be modified according to specific circumstances) The user can control the turning radius within the limited range according to the needs of the forward movement.

[0116] Step 6: Calculate the target heading angle of the lower limb robot according to the turning radius and the horizontal speed of the foot bottom, measure the actual heading angle of the lower limb robot according to the electronic compass on the lower limb robot, and correct the actual heading angle of the lower limb robot according to the target heading angle.

[0117] The calculation formula of the target heading angle is:

[0118]

[0119] Wherein, φ tThe target heading angle is φ0, the heading angle when starting to walk, measured by an electronic compass, and ω(t) is a function of angular velocity, describing the angular velocity changing over time t.

[0120] The actual heading angle of the lower limb robot is corrected by the target heading angle, in a correction mode of

[0121] v l ′ = v l +f(φ t ,φ)

[0122] v r ′ = v r -f(φ t ,φ)

[0123] wherein v l is the speed of the left driving wheel before correction, v r is the speed of the right driving wheel before correction, v l ′ is the speed of the left driving wheel after correction, v r ′ is the speed of the right driving wheel after correction, φ is the current heading angle, measured by an electronic compass, and f(φ t ,φ) is an error correction function, used to calculate the speed increment of the driving wheel.

[0124] In one embodiment of the present application, a control device for autonomously navigating a lower limb robot is also provided, comprising the following modules:

[0125] A training module, which drives the user to start training in place according to a standard movement gait of the lower limb robot;

[0126] A movement state adjustment module, which is used for safety state detection in the order of priority of obstacle monitoring feedback, plantar pressure sensor feedback and plantar ground clearance feedback, and adjusts the movement state of the lower limb robot according to the feedback of safety state detection;

[0127] A walking control module, which is used for walking control of the lower limb robot by controlling the rotation of the driving wheel of the lower limb robot after ensuring that the user has not encountered obstacles and is in a touch-down state through safety state detection;

[0128] A straight-line training control module, which is used for acquiring the movement parameters of the lower limbs of the user by the lower limb robot, calculating the horizontal speed of the plantar of the user, and controlling the speed of the driving wheel of the lower limb robot to be consistent with the horizontal speed of the plantar of the user, so that the user can perform straight-line training;

[0129] The turning control module is used for calculating the angular velocity of the lower limb robot turning and the speed of the left and right driving wheels according to the turning radius r and the horizontal speed of the user's foot bottom, and controlling the lower limb robot turning through the speed difference of the left and right driving wheels, so that the user can realize turning in the straight training process.

[0130] The actual heading angle correction module is used for calculating the target heading angle of the lower limb robot through the turning radius and the horizontal speed of the foot bottom, and correcting the actual heading angle of the lower limb robot according to the target heading angle.

[0131] The device further comprises a standard motion gait acquisition module, which is used for collecting gait images of normal people to extract effective gait information, obtaining a gait motion parameter database, and establishing a gait model based on the gait motion parameter database, so that the lower limb robot can drive the user to start training on the spot according to the standard motion gait.

[0132] In one of the embodiments of the present application, a terminal device is further provided, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the foregoing method when executing the computer program.

[0133] In one of the embodiments of the present application, a computer readable storage medium is further provided, which stores a computer program, and the computer program is executable on a processor to implement the steps of the foregoing method.

[0134] The foregoing control method of the autonomous navigation lower limb robot provided by the present application is described in detail. The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A control method for an autonomously navigated lower limb robot, characterized in that, Includes the following steps: The lower limb robot guides patients through gait training based on standard movement gait patterns. Safety status detection is performed in the order of priority: obstacle detection feedback, foot pressure sensor feedback, and foot height feedback. The motion state of the lower limb robot is adjusted based on the feedback from the safety status detection. Specifically, when the external torque received by any joint of the lower limb robot exceeds a preset torque threshold as reported by obstacle monitoring, the detection of foot pressure sensor feedback and foot height feedback will no longer be performed. When the external torque received by any joint of the lower limb robot is less than the preset torque threshold as reported by obstacle monitoring, but the foot pressure sensor reports that the foot pressure is greater than the preset pressure value, the detection of foot height will no longer be performed. After a safety status check ensures that the patient has not encountered any obstacles and is in contact with the ground, the movement of the lower limb robot is controlled by rotating the drive wheels of the lower limb robot. The lower limb robot acquires the motion parameters of the patient's lower limbs, calculates the horizontal velocity of the patient's foot, and controls the speed of the drive wheel of the lower limb robot based on the horizontal velocity of the patient's foot, so that the speed of the drive wheel is consistent with the horizontal velocity of the patient's foot, enabling the patient to perform straight-line training. Based on the turning radius r and the horizontal speed of the patient's foot, the angular velocity of the lower limb robot and the speed of the two drive wheels are calculated. The turning of the lower limb robot is controlled by the speed difference between the two drive wheels, so that the patient can turn during straight-line training. The target heading angle of the lower limb robot is calculated using the turning radius and the horizontal speed of the foot, and the actual heading angle of the lower limb robot is corrected based on the target heading angle.

2. The control method for an autonomous navigation lower limb robot according to claim 1, characterized in that, The method for obtaining the standard gait includes the following steps: Collect gait images of ordinary people, extract gait information, and obtain a database of gait motion parameters; Based on the gait motion parameter database, a gait model is established, and a standard gait is obtained based on the gait model.

3. The control method for an autonomous navigation lower limb robot according to claim 1, characterized in that, The safety status detection is performed in the order of priority: obstacle detection feedback, foot pressure sensor feedback, and foot lift-off height feedback. Based on the feedback from the safety status detection, the motion state of the lower limb robot is adjusted, including: Obstacle detection feedback is performed, and the external torque on each joint of the lower limb robot is monitored in real time. When the external torque on any joint of the lower limb robot exceeds the preset torque threshold, it is determined that an obstacle has been encountered, and the lower limb robot is controlled to stop moving. When the external torque on the joints of the lower limb robot does not exceed the preset torque threshold, the plantar pressure of the patient is monitored in real time. When the plantar pressure exceeds the preset pressure value, the lower limb robot is controlled to make the plantar pressure lower than the preset pressure value. When the plantar pressure does not exceed the preset pressure value, the patient's foot is kept in contact with the ground by the foot height feedback.

4. The control method for an autonomous navigation lower limb robot according to claim 3, characterized in that, The formula for calculating the height of the foot off the ground is as follows: h = Hxd x=L1cos·(θ1)+L2cos·(θ1+θ2) In the formula, h is the height of the foot off the ground, H is the distance from the hip joint of the lower limb robot to the ground, d is the distance from the ankle joint of the lower limb robot to the plane of the foot, x is the height difference between the hip joint and the ankle joint, L1 is the thigh length of the lower limb robot, L2 is the lower leg length of the lower limb robot, θ1 is the angle between the thigh of the lower limb robot and the vertical line, and θ2 is the angle between the thigh and the lower leg of the lower limb robot. When the height of the foot off the ground is less than zero, θ1 and θ2 are adjusted to ensure that the height of the foot remains zero.

5. The control method for an autonomous navigation lower limb robot according to claim 1, characterized in that, The formula for calculating the horizontal velocity of the foot is as follows: In the formula, v f v is the horizontal velocity of the foot. ankle Let L1 be the horizontal velocity of the ankle joint, L2 be the thigh length of the lower limb robot, L3 be the lower leg length of the lower limb robot, θ1 be the angle between the thigh and the vertical line, and θ2 be the angle between the thigh and the lower leg of the lower limb robot. These are the first derivatives of θ1 and θ2, respectively.

6. A control method for an autonomously navigated lower limb robot according to any one of claims 1-5, characterized in that, The formulas for calculating the angular velocity of the lower limb robot during turning and the velocities of the left and right drive wheels are as follows: In the formula, ω is the angular velocity, d is the distance between the two feet of the lower limb robot, D is the distance between the two drive wheels of the lower limb robot, r is the turning radius, and sign l For the orientation variable associated with the grounding foot of the lower limb robot, sign m v is the direction variable related to the drive wheels. l v is the speed of the left drive wheel. r This represents the speed of the right drive wheel.

7. The control method for an autonomous navigation lower limb robot according to claim 6, characterized in that, The formula for calculating the target heading angle is as follows: In the formula, φ t Let φ0 be the target heading angle, φ0 be the heading angle at the start of travel, and ω(t) be a function of angular velocity, describing the angular velocity as it changes with time t. The method of correcting the actual heading angle of the lower limb robot using the target heading angle is as follows: v l ′=v l +f(φ t ,φ) v r ’=v r -f(φ t ,φ) In the formula, v l v is the speed of the left drive wheel before correction. r v is the speed of the right drive wheel before correction. l 'V' represents the corrected speed of the left drive wheel. r ′ is the corrected speed of the right drive wheel, φ is the current heading angle, and f(φ) t ,φ) is the error correction function.

8. A control device for an autonomous navigation lower limb robot, characterized in that, The apparatus for implementing the method of any one of claims 1-7 comprises: In the training module, the lower limb robot guides the patient to begin training in place based on standard gait. The motion state adjustment module is used to perform safety state detection according to the priority order of obstacle monitoring feedback, foot pressure sensor feedback, and foot height feedback, and adjust the motion state of the lower limb robot based on the feedback of the safety state detection. The movement control module is used to control the movement of the lower limb robot by controlling the rotation of the drive wheels of the lower limb robot after the safety status detection ensures that the patient has not encountered any obstacles and is in contact with the ground. The straight-line training control module is used to acquire the motion parameters of the patient's lower limbs through the lower limb robot, calculate the horizontal speed of the patient's foot, and control the speed of the drive wheel of the lower limb robot to match the horizontal speed of the patient's foot, so that the patient can perform straight-line training. The turning control module is used to calculate the angular velocity of the lower limb robot and the speed of the left and right drive wheels based on the turning radius r and the horizontal speed of the patient's foot. The turning of the lower limb robot is controlled by the speed difference between the left and right drive wheels so that the patient can turn during straight training. The actual heading angle correction module is used to calculate the target heading angle of the lower limb robot using the turning radius and the foot horizontal velocity, and to correct the actual heading angle of the lower limb robot based on the target heading angle.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-7.

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