A rehabilitation robot

Abstract

The present invention provides a method for controlling a mechanical arm, a rehabilitation robot, a computer and a storage medium, the method comprising the following steps: acquiring data collected by a three-dimensional force sensor and a grip force sensor at the end of the mechanical arm in real time; if the finger joint grip force data is greater than the grip force threshold, and the control mode of the mechanical arm is a passive mode, acquiring multiple first resultant force data corresponding to each posture data within a first preset time in real time based on a preset acquisition frequency, so as to calculate the total resultant force data; if the total resultant force data is greater than or equal to the switching force data, calculating the switching force data required for mode switching based on the finger joint grip force data, so as to switch the passive mode of the mechanical arm to the active mode based on the switching force data. By integrating the three-dimensional force sensor and the grip force sensor, accurate monitoring of the patient's hand posture and grip force is achieved, and the training mode of the mechanical arm can be intelligently switched according to the patient's actual grip force and hand force application, thereby improving the pertinence and effectiveness of rehabilitation training.

Description

Technical Field

[0001] The present invention relates to the technical field of robotic arms, and in particular to a robotic arm control method, a rehabilitation robot, a computer and a storage medium. Background Art

[0002] With the aging of the population, the prevalence and disability rate of stroke are increasing year by year. Limb movement disorders seriously affect the quality of life of patients and bring great burden to their families and society. The upper limbs are involved in most movements in daily activities, and their functional damage seriously affects the patient's motor ability and quality of life. Therefore, upper limb motor function training is an extremely important part of the rehabilitation process of stroke patients.

[0003] There are different training modes for patients with different degrees of upper limb movement disorders. The active training mode is dominated by the patient's active movement, and the robotic arm is used to provide assistance to improve the rehabilitation training effect; the passive training mode is dominated by the robotic arm control, and the preset motion trajectory drives the patient's upper limb movement to improve the rehabilitation training effect.

[0004] In the prior art, different rehabilitation training modes need to be manually switched according to needs, which not only increases the complexity of operation, but also may lead to unstable training effects. In addition, manual mode switching may also affect the patient's rehabilitation process due to untimely or inaccurate operation. Summary of the invention

[0005] In view of the deficiencies in the prior art, the object of the present invention is to provide a robotic arm control method, a rehabilitation robot, a computer and a storage medium, aiming to solve the technical problem of poor training and rehabilitation effects in the prior art.

[0006] In order to achieve the above object, a first aspect of the present invention provides a robot arm control method, comprising the following steps:

[0007] Acquire data collected by the three-dimensional force sensor and the grip force sensor at the end of the robotic arm in real time, so as to acquire hand posture data and finger joint grip force data based on the collected data;

[0008] Determining whether the finger joint grip strength data is greater than a grip strength threshold;

[0009] If the finger joint grip strength data is greater than the grip strength threshold and the control mode of the robotic arm is the passive mode, a plurality of first resultant force data corresponding to each of the posture data within a first preset time are acquired in real time based on a preset acquisition frequency, so as to calculate the total resultant force data based on each of the first resultant force data, wherein the calculation formula of the total resultant force data is as follows:

[0010] ;

[0011] ;

[0012] In the formula, is the total force data, represents the bth first resultant force data, n is the number of the first resultant force data, is the first resultant force data, , , They are the collected data of the three-dimensional force sensor in the x, y, and z axes respectively;

[0013] The switching force data required for the current mode switching is calculated according to the three-dimensional force calibration value required for the switching mode and the finger joint grip force data. If the total force data is greater than or equal to the switching force data, the passive mode of the robot arm is switched to the active mode based on the switching force data. The calculation formula of the switching force data is as follows:

[0014] ;

[0015] In the formula, is the switching force data, is the finger joint grip strength data, K is the grip strength threshold, is the three-dimensional force calibration value.

[0016] According to one aspect of the above technical solution, the method further includes:

[0017] If the finger joint grip strength data is greater than the grip strength threshold and the control mode of the robotic arm is the active mode, a plurality of second combined force data corresponding to each of the posture data are acquired in real time based on a preset acquisition frequency;

[0018] If each of the second combined force data acquired within the second preset time is smaller than the three-dimensional force calibration value, the active mode of the robot arm is switched to the passive mode.

[0019] According to one aspect of the above technical solution, the step of switching the active mode of the robot arm to the passive mode specifically includes:

[0020] A software timer is set, and if each of the second combined force data obtained within the second preset time is less than the three-dimensional force calibration value, the software timer is controlled to send a timeout signal to switch the active mode of the robot arm to the passive mode.

[0021] According to one aspect of the above technical solution, the method further includes:

[0022] Establishing a spatial rectangular coordinate system with the base of the robotic arm as the origin;

[0023] According to a preset motion path of the wrist joint in the passive mode of the robotic arm, acquiring a plurality of path points corresponding to the preset motion path based on the spatial rectangular coordinate system;

[0024] The DH coordinate system of the robot arm is established, and based on the inverse solution algorithm of kinematics, the motor rotation angle corresponding to each path point is calculated in turn.

[0025] According to one aspect of the above technical solution, the method further includes:

[0026] Calculate the effect of gravity on the target motor torque value;

[0027] When the control mode of the robot arm is the active mode, the influence value is set to the feedforward torque size of the target motor.

[0028] According to one aspect of the above technical solution, the step of calculating the influence value of gravity on the target motor torque value specifically includes:

[0029] According to the kinematics forward solution algorithm, the coordinates of all motors and connecting rods corresponding to the robotic arm are calculated;

[0030] Obtain the coordinates of the target motor that needs to be gravity compensated in the current posture, and obtain the rotation axis coordinates and the rotation axis vector of the target motor according to the coordinates of the motor corresponding to the shoulder joint;

[0031] According to the weight of other motors and connecting rods in the robotic arm except the target motor, the gravity vectors of other motors and connecting rods in the rotation plane of the target motor are obtained, so as to calculate the influence value of gravity on the torque value of the target motor based on the gravity vector.

[0032] According to one aspect of the above technical solution, the calculation formula of the rotation axis coordinates and the rotation axis vector is as follows:

[0033] ;

[0034] ;

[0035] Where Z is the rotation axis coordinate, is the coordinate of the motor corresponding to the shoulder joint, is the coordinate of the target motor, is the unit vector of the rotation axis, is the vector of the rotation axis;

[0036] The calculation formula of the impact value is as follows:

[0037] ;

[0038] ;

[0039] ;

[0040] ;

[0041] ;

[0042] Wherein, T is the impact value, is the vector of the force arm of other motors and connecting rods in the rotation plane of the target motor, for The unit vector of is the weight of other motors and connecting rods in the rotation plane of the target motor, is the weight vector of other motors and connecting rods, are the coordinates of other motors and connecting rods, for Perpendicular to the lever arm Vector.

[0043] In a second aspect, the present invention provides a rehabilitation robot, including a robotic arm and a robotic arm control system, wherein the robotic arm control system includes:

[0044] An acquisition module, used to acquire data collected by a three-dimensional force sensor and a grip force sensor at the end of the robotic arm in real time, so as to acquire hand posture data and finger joint grip force data based on the collected data;

[0045] A judgment module, used to judge whether the finger joint grip strength data is greater than a grip strength threshold;

[0046] A calculation module is used for acquiring a plurality of first resultant force data corresponding to each of the posture data in real time within a first preset time based on a preset acquisition frequency if the finger joint grip force data is greater than the grip force threshold and the control mode of the robotic arm is a passive mode, so as to calculate the total resultant force data based on each of the first resultant force data, wherein the calculation formula of the total resultant force data is as follows:

[0047] ;

[0048] ;

[0049] In the formula, is the total force data, represents the bth first resultant force data, n is the number of the first resultant force data, is the first resultant force data, , , They are the collected data of the three-dimensional force sensor in the x, y, and z axes respectively;

[0050] The first switching module is used to calculate the switching force data required for the current mode switching according to the three-dimensional force calibration value required for the switching mode and the finger joint grip force data, and if the total force data is greater than or equal to the switching force data, the passive mode of the robot arm is switched to the active mode based on the switching force data, wherein the calculation formula of the switching force data is as follows:

[0051] ;

[0052] In the formula, is the switching force data, is the finger joint grip strength data, K is the grip strength threshold, is the three-dimensional force calibration value.

[0053] According to one aspect of the above technical solution, the system further includes:

[0054] A second switching module is used to acquire a plurality of second combined force data corresponding to each of the posture data in real time based on a preset acquisition frequency if the finger joint grip strength data is greater than the grip strength threshold and the control mode of the robotic arm is an active mode;

[0055] If each of the second combined force data acquired within the second preset time is smaller than the three-dimensional force calibration value, the active mode of the robot arm is switched to the passive mode.

[0056] According to one aspect of the above technical solution, the second switching module is specifically used for:

[0057] A software timer is set, and if each of the second combined force data obtained within the second preset time is less than the three-dimensional force calibration value, the software timer is controlled to send a timeout signal to switch the active mode of the robot arm to the passive mode.

[0058] According to one aspect of the above technical solution, the system further includes:

[0059] Passive training modules for:

[0060] Establishing a spatial rectangular coordinate system with the base of the robotic arm as the origin;

[0061] According to a preset motion path of the wrist joint in the passive mode of the robotic arm, acquiring a plurality of path points corresponding to the preset motion path based on the spatial rectangular coordinate system;

[0062] The DH coordinate system of the robot arm is established, and based on the inverse solution algorithm of kinematics, the motor rotation angle corresponding to each path point is calculated in turn.

[0063] According to one aspect of the above technical solution, the system further includes:

[0064] Active training modules for:

[0065] Calculate the effect of gravity on the target motor torque value;

[0066] When the control mode of the robot arm is the active mode, the influence value is set to the feedforward torque size of the target motor.

[0067] According to one aspect of the above technical solution, the active training module is specifically used for:

[0068] According to the kinematics forward solution algorithm, the coordinates of all motors and connecting rods corresponding to the robotic arm are calculated;

[0069] Obtain the coordinates of the target motor that needs to be gravity compensated in the current posture, and obtain the rotation axis coordinates and the rotation axis vector of the target motor according to the coordinates of the motor corresponding to the shoulder joint;

[0070] According to the weight of other motors and connecting rods in the robotic arm except the target motor, the gravity vectors of other motors and connecting rods in the rotation plane of the target motor are obtained, so as to calculate the influence value of gravity on the torque value of the target motor based on the gravity vector.

[0071] In a third aspect, an embodiment of the present application provides a computer, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a robotic arm control method as described in the first aspect is implemented.

[0072] In a fourth aspect, an embodiment of the present application provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements a robotic arm control method as described in the first aspect.

[0073] Compared with the prior art, the beneficial effects of the present invention are as follows: by integrating a three-dimensional force sensor and a grip force sensor, accurate monitoring of the patient's hand posture and grip force is achieved, and the training mode of the robotic arm can be intelligently switched according to the patient's actual grip strength and hand force application, thereby improving the pertinence and effectiveness of rehabilitation training. In the passive training mode, by establishing a spatial rectangular coordinate system and a DH coordinate system, the motion path of the robotic arm and the motor rotation angle are calculated using the inverse kinematic solution algorithm, thereby ensuring that the robotic arm can drive the patient to perform rehabilitation training according to a preset trajectory; in the active training mode, by calculating the influence of gravity on the target motor torque value and performing gravity compensation, the influence of the robotic arm on the patient's autonomous movement is reduced, thereby improving the comfort and effectiveness of training. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 This is a flow chart of the robot arm control method in the first embodiment of the present invention;

[0075] Figure 2 It is a structural block diagram of a robot arm control system in a second embodiment of the present invention;

[0076] Figure 3 is a schematic diagram of the hardware structure of a computer in the third embodiment of the present application;

[0077] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0078] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0079] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0080] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0081] See also Figure 1 , which is a flow chart of a robot arm control method in a first embodiment of the present invention. As shown in the figure, the method includes the following steps:

[0082] Step S100, acquiring data collected by a three-dimensional force sensor and a grip force sensor at the end of the robotic arm in real time, so as to acquire hand posture data and finger joint grip force data based on the collected data.

[0083] For ease of understanding, in this embodiment, the end of the upper limb mechanical arm is connected to an arm support, the patient's arm is fixed on the arm support, and a grip sensor is provided at the front end of the arm support as a grip. The palm holds the grip sensor, and a three-dimensional force sensor is connected below the grip sensor. The grip sensor can accurately obtain the grip force of the finger joints, and the three-dimensional force sensor can accurately obtain the direction of the hand force.

[0084] Step S200, judging whether the knuckle grip strength data is greater than the grip strength threshold. It can be understood that the grip strength threshold indicates the minimum grip strength required to switch modes. When the knuckle grip strength data is less than the grip strength threshold, no matter how large the three-dimensional force sensor is, the mode will not be judged and switched, to prevent the posture data from being affected by external force, and the user actually does not hold the grip sensor handle, resulting in the wrong mode switch.

[0085] Step S300, if the finger joint grip strength data is greater than the grip strength threshold and the control mode of the robotic arm is a passive mode, a plurality of first resultant force data corresponding to each of the posture data within a first preset time are acquired in real time based on a preset acquisition frequency, so as to calculate the total resultant force data based on each of the first resultant force data.

[0086] Step S400, calculate the switching force data required for the current mode switching according to the three-dimensional force calibration value required for the switching mode and the finger joint grip strength data; if the total force data is greater than or equal to the switching force data, switch the passive mode of the robotic arm to the active mode based on the switching force data.

[0087] Specifically, in this embodiment, the calculation formula of the total resultant force data is as follows:

[0088] ;

[0089] ;

[0090] In the formula, is the total force data, represents the bth first resultant force data, n is the number of the first resultant force data, is the first resultant force data, , , They are the collected data of the three-dimensional force sensor in the x, y, and z axes.

[0091] The calculation formula of the switching force data is as follows:

[0092] ;

[0093] In the formula, is the switching force data, is the finger joint grip strength data, K is the grip strength threshold, is the three-dimensional force calibration value.

[0094] Preferably, in this embodiment, the preset acquisition frequency is once every 50ms, and the resultant force data is calculated once every 50ms based on the three-dimensional force sensor. The total value of the data within 1s-2s is the total resultant force data. The greater the grip force, the greater the force required to switch to the active mode, so that the switching force data is obtained based on the total resultant force data. When the total resultant force data is greater than or equal to the three-dimensional force calibration value, the passive mode is switched to the active mode based on the switching force data; when the total resultant force data is less than the three-dimensional force calibration value, the mode is not switched, the passive mode is maintained, and the sensor data is continuously read. The speed of switching the mode in the above manner can also be adjusted according to the patient's ideas. The patient hopes to switch to the active mode quickly, and only needs to apply more force to the hand. The fastest 20 resultant force data are added within 1s to meet the switching requirements. If the patient's force is relatively small, the adaptive switching of the mode can be completed in 2s at the slowest, that is, 40 data.

[0095] Preferably, in this embodiment, the method further includes:

[0096] Step S500, if the finger joint grip strength data is greater than the grip strength threshold, and the control mode of the robotic arm is the active mode, a plurality of second combined force data corresponding to each of the posture data are acquired in real time based on a preset acquisition frequency;

[0097] If each of the second combined force data acquired within the second preset time is smaller than the three-dimensional force calibration value, the active mode of the robot arm is switched to the passive mode.

[0098] It can be understood that when the finger joint grip strength data is less than the grip strength threshold, the mode will not be switched, and the calculation formula of the second resultant force data is the same as that of the first resultant force data.

[0099] Specifically, in this embodiment, the step of switching the active mode of the robot arm to the passive mode specifically includes:

[0100] A software timer is set, and if each of the second combined force data obtained within the second preset time is less than the three-dimensional force calibration value, the software timer is controlled to send a timeout signal to switch the active mode of the robot arm to the passive mode.

[0101] Preferably, when the finger joint grip strength data is greater than the grip strength threshold, the second resultant force data is calculated every 50ms, and a software timer is set to calculate whether the size of the second resultant force data is continuously less than the threshold value of the switching mode (three-dimensional force calibration value) within 4s. If it is continuously less than the three-dimensional force calibration value, the timer sends a timeout signal when the 4s time is reached, and the mode is switched to the passive mode; if within 4s, when the second resultant force data is greater than the above-mentioned three-dimensional force calibration value, the software timer is turned off, and when the second resultant force data is greater than the above-mentioned three-dimensional force calibration value, the timer is turned on again. When the timer is turned off or the timeout period is not reached, the active mode is maintained.

[0102] In some application scenarios of this embodiment, when the patient wants to exert force to control the robotic arm, it can be switched to active mode, and the patient can independently control the rehabilitation training. When the patient does not want to exert force to control the robotic arm, it can be switched to passive mode, and the robotic arm can drive the patient to perform rehabilitation training. The patient's current usage status can be confirmed by the sensor, and the rehabilitation training mode required by the patient can be adaptively switched to improve the training and rehabilitation effect.

[0103] Preferably, in this embodiment, the passive training mode is controlled as follows:

[0104] Establishing a spatial rectangular coordinate system with the base of the robotic arm as the origin;

[0105] According to a preset motion path of the wrist joint in the passive mode of the robotic arm, acquiring a plurality of path points corresponding to the preset motion path based on the spatial rectangular coordinate system;

[0106] The DH coordinate system of the robot arm is established, and based on the inverse solution algorithm of kinematics, the motor rotation angle corresponding to each path point is calculated in turn.

[0107] Specifically, passive training is controlled by a robotic arm to drive the patient's upper limb movements. The motor control adopts a position control mode, with the base of the robotic arm as the origin, to establish a spatial rectangular coordinate system, determine the motion path of the end wrist joint position, and take out the spatial coordinates of all coordinate points on the path at a fixed interval of 500ms. The DH coordinate system of the robotic arm is established, and the spatial coordinates of each coordinate point are calculated in turn through the inverse solution method of kinematics to obtain the rotation angle of each motor, and the interval of each angle is obtained by subtracting them. The calculation formula for the above motor speed is as follows:

[0108] ;

[0109] In the formula, is the motor speed, is each angle interval, The time interval is 500ms. The motor speed of each small path is calculated. The motor is set according to the obtained speed and angle for each operation, and the robot arm runs along the specified path.

[0110] Preferably, in this embodiment, the active training mode is controlled as follows:

[0111] Calculate the effect of gravity on the target motor torque value;

[0112] When the control mode of the robot arm is the active mode, the influence value is set to the feedforward torque size of the target motor.

[0113] Specifically, in active training, the patient controls the robotic arm, which follows the movement of the patient's arm. The motor control adopts impedance control mode to solve the resistance of the robotic arm to the patient's autonomous movement in different postures. The resistance mainly comes from the torque generated by the gravity of other motors or connecting rods on the current motor. Therefore, in terms of control, it is first necessary to eliminate the influence of gravity, make a certain gravity compensation for each motor, reduce the influence of the robotic arm on the patient's autonomous movement, and make the patient feel the presence of the robotic arm as little as possible when exercising his upper limbs.

[0114] Furthermore, in this embodiment, the step of calculating the influence value of gravity on the target motor torque value specifically includes:

[0115] According to the kinematics forward solution algorithm, the coordinates of all motors and connecting rods corresponding to the robotic arm are calculated;

[0116] Obtain the coordinates of the target motor that needs to be gravity compensated in the current posture, and obtain the rotation axis coordinates and the rotation axis vector of the target motor according to the coordinates of the motor corresponding to the shoulder joint;

[0117] According to the weight of other motors and connecting rods in the robotic arm except the target motor, the gravity vectors of other motors and connecting rods in the rotation plane of the target motor are obtained, so as to calculate the influence value of gravity on the torque value of the target motor based on the gravity vector.

[0118] Furthermore, the calculation formulas of the above rotation axis coordinates and the vector of the rotation axis are as follows:

[0119] ;

[0120] ;

[0121] Where Z is the rotation axis coordinate, is the coordinate of the motor corresponding to the shoulder joint, is the coordinate of the target motor, is the unit vector of the rotation axis, is the vector of the rotation axis;

[0122] The calculation formula of the impact value is as follows:

[0123] ;

[0124] ;

[0125] ;

[0126] ;

[0127] ;

[0128] Wherein, T is the impact value, is the vector of the force arm of other motors and connecting rods in the rotation plane of the target motor, for The unit vector of is the weight of other motors and connecting rods in the rotation plane of the target motor, is the weight vector of other motors and connecting rods, are the coordinates of other motors and connecting rods, for Perpendicular to the lever arm Vector.

[0129] In summary, the robotic arm control method in the above-mentioned embodiment of the present invention realizes accurate monitoring of the patient's hand posture and grip strength by integrating a three-dimensional force sensor and a grip force sensor, and can intelligently switch the training mode of the robotic arm according to the patient's actual grip strength and hand force, thereby improving the pertinence and effectiveness of rehabilitation training. In the passive training mode, by establishing a spatial rectangular coordinate system and a DH coordinate system, the motion path of the robotic arm and the motor rotation angle are calculated using the inverse kinematic solution algorithm, thereby ensuring that the robotic arm can drive the patient to perform rehabilitation training according to a preset trajectory; in the active training mode, by calculating the influence of gravity on the target motor torque value and performing gravity compensation, the influence of the robotic arm on the patient's autonomous movement is reduced, thereby improving the comfort and effect of training.

[0130] A second embodiment of the present invention provides a rehabilitation robot, including a mechanical arm and a mechanical arm control system, such as Figure 2 FIG. 1 is a block diagram of a mechanical arm control system in a second embodiment of the present invention. As shown in the figure, the system includes:

[0131] The acquisition module 100 is used to acquire the acquisition data of the three-dimensional force sensor and the grip force sensor at the end of the mechanical arm in real time, so as to acquire the hand posture data and the finger joint grip force data based on the acquisition data;

[0132] A judging module 200, used to judge whether the finger joint grip strength data is greater than a grip strength threshold;

[0133] The calculation module 300 is used for acquiring a plurality of first resultant force data corresponding to each of the posture data within a first preset time in real time based on a preset acquisition frequency if the finger joint grip force data is greater than the grip force threshold and the control mode of the robotic arm is a passive mode, so as to calculate the total resultant force data based on each of the first resultant force data;

[0134] The first switching module 400 is used to calculate the switching force data required for the current mode switching according to the three-dimensional force calibration value required for the switching mode and the finger joint grip strength data. If the total force data is greater than or equal to the switching force data, the passive mode of the robotic arm is switched to the active mode based on the switching force data.

[0135] Preferably, in this embodiment, the system further includes:

[0136] A second switching module is used to acquire a plurality of second combined force data corresponding to each of the posture data in real time based on a preset acquisition frequency if the finger joint grip strength data is greater than the grip strength threshold and the control mode of the robotic arm is an active mode;

[0137] If each of the second combined force data acquired within the second preset time is smaller than the three-dimensional force calibration value, the active mode of the robot arm is switched to the passive mode.

[0138] Preferably, in this embodiment, the second switching module is specifically used for:

[0139] A software timer is set, and if each of the second combined force data obtained within the second preset time is less than the three-dimensional force calibration value, the software timer is controlled to send a timeout signal to switch the active mode of the robot arm to the passive mode.

[0140] Preferably, in this embodiment, the system further includes:

[0141] Passive training modules for:

[0142] Establishing a spatial rectangular coordinate system with the base of the robotic arm as the origin;

[0143] According to a preset motion path of the wrist joint in the passive mode of the robotic arm, acquiring a plurality of path points corresponding to the preset motion path based on the spatial rectangular coordinate system;

[0144] The DH coordinate system of the robot arm is established, and based on the inverse solution algorithm of kinematics, the motor rotation angle corresponding to each path point is calculated in turn.

[0145] Preferably, in this embodiment, the system further includes:

[0146] Active training modules for:

[0147] Calculate the effect of gravity on the target motor torque value;

[0148] When the control mode of the robot arm is the active mode, the influence value is set to the feedforward torque size of the target motor.

[0149] Preferably, in this embodiment, the active training module is specifically used for:

[0150] According to the kinematics forward solution algorithm, the coordinates of all motors and connecting rods corresponding to the robotic arm are calculated;

[0151] Obtain the coordinates of the target motor that needs to be gravity compensated in the current posture, and obtain the rotation axis coordinates and the rotation axis vector of the target motor according to the coordinates of the motor corresponding to the shoulder joint;

[0152] According to the weight of other motors and connecting rods in the robotic arm except the target motor, the gravity vectors of other motors and connecting rods in the rotation plane of the target motor are obtained, so as to calculate the influence value of gravity on the torque value of the target motor based on the gravity vector.

[0153] The third embodiment of the present application provides a computer, which may include a processor 81 and a memory 82 storing computer program commands. It can be understood that the principles mentioned in the robot control system in this embodiment correspond to the robot control method in the first embodiment of the present application. The relevant principles not described can be referred to in detail in the first embodiment, and will not be repeated here.

[0154] Specifically, the processor 81 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0155] Among them, the memory 82 may include a large-capacity memory for data or commands. By way of example and not limitation, the memory 82 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 82 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 82 may be inside or outside the data processing device. In a specific embodiment, the memory 82 is a non-volatile memory. In a specific embodiment, the memory 82 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0156] The memory 82 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program commands executed by the processor 81 .

[0157] The processor 81 implements any one of the robot arm control methods in the above embodiments by reading and executing computer program commands stored in the memory 82 .

[0158] In some embodiments, the computer may further include a communication interface 83 and a bus 80. Figure 3 As shown, the processor 81, the memory 82, and the communication interface 83 are connected via a bus 80 and communicate with each other.

[0159] The communication interface 83 is used to implement communication between the modules, devices, units and / or equipment in the embodiment of the present application. The communication interface 83 can also implement data communication with other components such as: external devices, image / data acquisition equipment, databases, external storage, and image / data processing workstations.

[0160] The bus 80 includes hardware, software or both, and couples the components of the computer to each other. The bus 80 includes but is not limited to at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a local bus. By way of example and not limitation, bus 80 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, bus 80 may include one or more buses. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.

[0161] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0162] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A rehabilitation robot, characterized in that: It includes a robotic arm and a robotic arm control system, wherein the robotic arm control system includes: An acquisition module, used to acquire data collected by a three-dimensional force sensor and a grip force sensor at the end of the robotic arm in real time, so as to acquire hand posture data and finger joint grip force data based on the collected data; A judgment module, used to judge whether the finger joint grip strength data is greater than a grip strength threshold; A calculation module is used for acquiring a plurality of first resultant force data corresponding to each of the posture data in real time within a first preset time based on a preset acquisition frequency if the finger joint grip force data is greater than the grip force threshold and the control mode of the robotic arm is a passive mode, so as to calculate the total resultant force data based on each of the first resultant force data, wherein the calculation formula of the total resultant force data is as follows: ； ； In the formula, is the total force data, represents the bth first resultant force data, n is the number of the first resultant force data, is the first resultant force data, , , They are the collected data of the three-dimensional force sensor in the x, y, and z axes respectively; The first switching module is used to calculate the switching force data required for the current mode switching according to the three-dimensional force calibration value required for the switching mode and the finger joint grip force data, and if the total force data is greater than or equal to the switching force data, the passive mode of the robot arm is switched to the active mode based on the switching force data, wherein the calculation formula of the switching force data is as follows: ； In the formula, is the switching force data, is the finger joint grip strength data, K is the grip strength threshold, is the three-dimensional force calibration value.

2. The rehabilitation robot according to claim 1, characterized in that: The system further comprises: A second switching module is used to acquire a plurality of second combined force data corresponding to each of the posture data in real time based on a preset acquisition frequency if the finger joint grip strength data is greater than the grip strength threshold and the control mode of the robotic arm is an active mode; If each of the second combined force data acquired within the second preset time is smaller than the three-dimensional force calibration value, the active mode of the robot arm is switched to the passive mode.

3. The rehabilitation robot according to claim 2, characterized in that: The second switching module is specifically used for: A software timer is set, and if each of the second combined force data obtained within the second preset time is less than the three-dimensional force calibration value, the software timer is controlled to send a timeout signal to switch the active mode of the robot arm to the passive mode.

4. The rehabilitation robot according to any one of claims 1 to 3, characterized in that: The system further comprises: Passive training modules for: Establishing a spatial rectangular coordinate system with the base of the robotic arm as the origin; According to a preset motion path of the wrist joint in the passive mode of the robotic arm, acquiring a plurality of path points corresponding to the preset motion path based on the spatial rectangular coordinate system; The DH coordinate system of the robot arm is established, and based on the inverse solution algorithm of kinematics, the motor rotation angle corresponding to each path point is calculated in turn.

5. The rehabilitation robot according to any one of claims 1 to 3, characterized in that: The system further comprises: Active training modules for: Calculate the effect of gravity on the target motor torque value; When the control mode of the robot arm is the active mode, the influence value is set to the feedforward torque size of the target motor.

6. The rehabilitation robot according to claim 5, characterized in that: The active training module is specifically used for: According to the kinematics forward solution algorithm, the coordinates of all motors and connecting rods corresponding to the robotic arm are calculated; Obtain the coordinates of the target motor that needs to be gravity compensated in the current posture, and obtain the rotation axis coordinates and the rotation axis vector of the target motor according to the coordinates of the motor corresponding to the shoulder joint; According to the weight of other motors and connecting rods in the robotic arm except the target motor, the gravity vectors of other motors and connecting rods in the rotation plane of the target motor are obtained, so as to calculate the influence value of gravity on the torque value of the target motor based on the gravity vector.

7. The rehabilitation robot according to claim 6, characterized in that: The calculation formulas of the rotation axis coordinates and the rotation axis vector are as follows: ； ； Where Z is the coordinate of the rotation axis, is the coordinate of the motor corresponding to the shoulder joint, is the coordinate of the target motor, is the unit vector of the rotation axis, is the vector of the rotation axis; The calculation formula of the impact value is as follows: ； ； ； ； ； Wherein, T is the impact value, is the vector of the force arm of other motors and connecting rods in the rotation plane of the target motor, for The unit vector of is the weight of other motors and connecting rods in the rotation plane of the target motor, is the weight vector of other motors and connecting rods, are the coordinates of other motors and connecting rods, for Perpendicular to the lever arm Vector.

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