Adaptive method for adaptive reconfigurable virtual exoskeleton rehabilitation robot system

Abstract

The application belongs to the technical field of rehabilitation robots, and particularly discloses an adaptive method of a self-adaptive reconfigurable virtual exoskeleton rehabilitation robot system, which is based on the rehabilitation movement of main body joints, such as a shoulder joint, an elbow joint, a wrist joint, a hip joint, a knee joint and an ankle joint, and when a patient is placed in a designated position, the system automatically identifies (such as image recognition) the position of the patient, such as a shoulder joint, according to the selected rehabilitation part, and a corresponding mechanical arm automatically docks the shoulder and upper arm of the patient, and makes rehabilitation preparation, the system internally calls and corrects a corresponding joint movement model according to the physiological structure of the joint and the condition (range of motion, pain level) of the patient, and starts training according to a designated rehabilitation training scheme.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of rehabilitation robots, and particularly relates to an adaptive method of an adaptive reconfigurable virtual exoskeleton rehabilitation robot system. BACKGROUND

[0002] Stroke, commonly known as cerebral apoplexy, is an acute cerebrovascular disease, a common intractable disease that seriously endangers human health and life safety, and has a high incidence, high mortality and high disability rate. According to statistics, the incidence of stroke in China is higher than the global average, with about 2.5 million new stroke cases and 7.5 million stroke survivors each year. Among stroke survivors, 70-80% will have varying degrees of disability, with hemiplegia being the most common, which can easily cause upper and lower limb motor dysfunction and seriously affect the daily life of patients. According to clinical research, actively receiving effective rehabilitation treatment can enable 90% of patients to regain the ability to walk and take care of themselves, and if no rehabilitation treatment is performed, the proportion is only 6%. Therefore, it is particularly important for stroke patients to receive early rehabilitation training, and effective rehabilitation training can help patients maximize the improvement of motor dysfunction and reduce sequelae.

[0003] For stroke patients, the traditional rehabilitation treatment method is mainly that the patient regularly goes to the hospital or rehabilitation center, and the rehabilitation therapist performs "one-to-one" or even "one-to-many" manual auxiliary therapy for the patient, and the rehabilitation effect is largely dependent on the treatment level of the doctor, and the training and training intensity are difficult to guarantee. Domestic and foreign scholars combine robot technology with clinical rehabilitation medicine and propose an auxiliary training scheme based on a rehabilitation robot. The rehabilitation robot can perform high-intensity repetitive work to reduce the burden of medical personnel, increase the opportunity for patients to receive rehabilitation treatment, and ensure the rehabilitation training intensity. At the same time, the rehabilitation robot has high precision and intelligence, can automatically record training data, and provide objective and detailed evaluation parameters, thereby further adjusting the treatment scheme, improving the rehabilitation training effect and patient treatment enthusiasm, and early recovery of health level.

[0004] At present, rehabilitation robots are mainly divided into two types according to their structure: end-effector type and exoskeleton type. Taking upper limb rehabilitation robots as an example, end-effector type robots drive the human hand movement through the end-effector, thus realizing the rehabilitation training of the whole upper limb, which focuses on the movement trajectory of the end hand, but for the training of a specific part of the upper limb, it may introduce unnecessary rehabilitation movement. Exoskeleton type robots can be worn on the upper limb, and their joint rotation axes coincide with the human joint rotation axes one by one, which can accurately control each joint of the upper limb to realize the independent movement of a single joint and the composite movement of multiple joints of the upper limb, and can avoid secondary injury of the affected limb by limiting the movement range of each joint. Considering that 88% of stroke survivors have their upper limb movement function restricted to some extent, compared with lower limb rehabilitation, the complex anatomical structure of the upper limb makes its recovery more complex. Therefore, upper limb exoskeleton rehabilitation robots have gradually become a research hotspot, focus and difficulty in the field of rehabilitation medical engineering.

[0005] With the rapid development of computer, sensor and other technologies, the research of exoskeleton rehabilitation robots has been fully developed. Among the representative researches of upper limb exoskeleton rehabilitation robots, there are MEDARM, a rope-driven exoskeleton developed by Queen's University in Canada, ARMin series of exoskeletons developed by University of Zurich in Switzerland, Armeo Power upper limb rehabilitation exoskeleton commercialized by Hocoma AG company, CADEN-7 seven-degree-of-freedom upper limb power exoskeleton developed by University of Washington, HIT-5 system developed by Harbin Institute of Technology, and ZJUESA system developed by Zhejiang University, etc. Such exoskeleton rehabilitation robots are all designed in the form of typical bionic arm structure, and the shoulder and elbow joints are mostly arranged according to the distribution characteristics of the corresponding joints of the human upper limb, which requires the axis / center of the corresponding joints of man-machine to always keep aligned or coincided during the rehabilitation training process. However, the position of the human shoulder joint (glenohumeral joint) is determined by the acromioclavicular joint, sternoclavicular joint, humeral posture, etc., and it is difficult for the mechanical axis of the exoskeleton to be aligned or coincided with the axis / center of the glenohumeral joint in real time, which may cause the problem of incompatibility between man and machine movement. Moreover, such devices require strict design in structure, transmission and control, and are complex to wear and high in cost.

[0006] Since 2000, Toth et al. in Hungary developed a series of REHAROB robot systems composed of two industrial robots in series, and through analysis and decomposition of the rehabilitation methods of therapists to patients, 45 different types of three-dimensional shoulder and elbow actions were extracted, so that the robot system could drive the patient to realize continuous passive motion (CPM) action. The series of robots use unmodified industrial robots, and the human-computer interaction compliance and safety required for muscle strength training during rehabilitation treatment still need to be verified, and only the passive training mode for expanding joint range of motion is developed, and the training effect of muscle strength is still unknown; but it is the first to use multiple robots for rehabilitation training, providing a new idea for the research of exoskeleton rehabilitation robots.

[0007] CN201510812527.9 Parallel lower limb rehabilitation robot adaptive training control method and rehabilitation robot, according to the collected electromyographic signal, the activity and contraction force state of lower limb muscle are estimated, and the impedance model parameters of the robot are adaptively adjusted accordingly; at the same time, the interaction force between the patient and the robot is obtained, and the calculation and correction of the robot motion are realized through the identification result and the impedance model; the main control part of the rehabilitation robot provides online trajectory planning and inverse kinematics solution, and sends the control command to the corresponding joint controller and driver, realizing the adaptive training control of the robot. The invention can realize intelligentization in the rehabilitation process, and has very important practical significance and application value for improving the rehabilitation effect of patients and improving the initiative and enthusiasm of patients in rehabilitation.

[0008] CN201610243458.9 Adaptive control method of lower limb rehabilitation robot according to patient's motion needs, through real-time acquisition of joint angle and joint angular velocity signals of patient's lower limbs, robust variable structure control method is used to realize adaptive tracking control of expected trajectory; then, with the help of human-machine dynamics system model, RBF neural network is used to learn the rehabilitation degree and active movement ability of the patient in real time, and then the feedforward assistance of the lower limb rehabilitation robot is estimated; then, based on the trajectory tracking error, the real-time assistance of the robot is adaptively attenuated, realizing continuous adaptive assistance control according to the needs of patient's rehabilitation; finally, the trajectory corrected by the adaptive assistance control according to the needs of patient's rehabilitation is input into the joint motion controller of the lower limb rehabilitation robot, and online motion control is carried out, realizing continuous and seamless adaptive control of the lower limb rehabilitation robot according to the needs of patient's rehabilitation.

[0009] The above-mentioned prior art shows that when the existing robot structure is mainly based on the existing robot structure, the algorithm is improved to achieve adaptive adjustment, but the existing exoskeleton rehabilitation robot still has the limitations of complex structure design, difficult to wear, and insufficient human-computer interaction compliance, which leads to the fact that the adaptive effect is not ideal.

[0010] Based on this, the application develops a brand-new adaptive reconfigurable virtual exoskeleton rehabilitation robot system and provides an adaptive method of the system, so as to overcome the limitations of the existing exoskeleton rehabilitation robot in complex structure design, difficult wearing and insufficient human-computer interaction, and to adaptively adjust the robot system according to the user demand. SUMMARY

[0011] The application solves the purpose of providing an adaptive method of an adaptive reconfigurable virtual exoskeleton rehabilitation robot system, so as to overcome the limitations of the existing exoskeleton rehabilitation robot in complex structure design, difficult wearing and insufficient human-computer interaction, and to reasonably adjust the robot system according to the patient's condition demand, so as to execute the corresponding rehabilitation training scheme.

[0012] In order to achieve the above purpose, the application provides an adaptive method of an adaptive reconfigurable virtual exoskeleton rehabilitation robot system, which adopts the technical scheme as follows:

[0013] An adaptive method of an adaptive reconfigurable virtual exoskeleton rehabilitation robot system, the system comprising:

[0014] A first multi-axis robot, the first multi-axis robot having a first execution end;

[0015] A second multi-axis robot, the second multi-axis robot having a second execution end;

[0016] A third multi-axis robot, the third multi-axis robot having a third execution end;

[0017] A first bearing auxiliary tool, the first bearing auxiliary tool being detachably connected with the first execution end;

[0018] A second bearing auxiliary tool, the second bearing auxiliary tool being detachably connected with the second execution end;

[0019] A third bearing auxiliary tool, the third bearing auxiliary tool being detachably connected with the third execution end;

[0020] A camera assembly for collecting image data;

[0021] The adaptive method comprises:

[0022] According to the image data, the position data of the first bearing auxiliary tool, the position data of the second bearing auxiliary tool and the position data of the third bearing auxiliary tool and the corresponding joint parts of the first bearing auxiliary tool, the second bearing auxiliary tool and the third bearing auxiliary tool are extracted;

[0023] controlling the first multi-axis robot, the second multi-axis robot, or the third multi-axis robot to move according to the position data of the first load assistive tool, the position data of the second load assistive tool, and the position data of the third load assistive tool, so that the first end effector, the second end effector, or the third end effector is connected with the first load assistive tool, the second load assistive tool, or the third load assistive tool;

[0024] determining the movement trajectory of the first end effector, the second end effector, and the third end effector according to the corresponding joint positions of the first load assistive tool, the second load assistive tool, and the third load assistive tool.

[0025] Further, the controlling the first multi-axis robot, the second multi-axis robot, or the third multi-axis robot to move according to the position data of the first load assistive tool, the position data of the second load assistive tool, and the position data of the third load assistive tool, so that the first end effector, the second end effector, or the third end effector is connected with the first load assistive tool, the second load assistive tool, or the third load assistive tool, specifically includes:

[0026] determining the position data of the first end effector, the second end effector, or the third end effector;

[0027] determining the first trajectory of the first end effector moving to the position connected with the first load assistive tool, the first trajectory of the second end effector moving to the position connected with the second load assistive tool, or the first trajectory of the third end effector moving to the position connected with the third load assistive tool according to the position data of the first end effector, the position data of the second end effector, or the position data of the third end effector, and the position data of the first load assistive tool, the position data of the second load assistive tool, and the position data of the third load assistive tool;

[0028] controlling the first multi-axis robot, the second multi-axis robot, or the third multi-axis robot to move according to the first trajectory, the second trajectory, or the third trajectory, so that the first end effector, the second end effector, or the third end effector is connected with the first load assistive tool, the second load assistive tool, or the third load assistive tool.

[0029] Further, after determining the movement trajectory of the first end effector, the second end effector, and the third end effector according to the corresponding joint positions of the first load assistive tool, the second load assistive tool, and the third load assistive tool, the method further includes:

[0030] determining the movement equation of the first end effector, the second end effector, and the third end effector according to the movement trajectory of the first end effector, the movement trajectory of the second end effector, and the movement trajectory of the third end effector, respectively;

[0031] adjusting the movement equation according to the input instruction.

[0032] Further, the first multi-axis robot includes:

[0033] a first base;

[0034] a first driving assembly, disposed on the first base;

[0035] a first shaft, disposed on an output end of the first driving assembly;

[0036] a second shaft, disposed on the first shaft;

[0037] a second driving assembly, disposed on the second shaft;

[0038] a third shaft, disposed on an output end of the second driving assembly;

[0039] a fourth shaft, one end of which is connected to the third shaft, and the other end of which is connected to the first execution end.

[0040] Further, the second multi-axis robot comprises:

[0041] a second base;

[0042] a third driving assembly, disposed on the second base;

[0043] a fifth shaft, disposed on an output end of the third driving assembly;

[0044] a sixth shaft, disposed on the fifth shaft;

[0045] a fourth driving assembly, disposed on the sixth shaft;

[0046] a seventh shaft, disposed on an output end of the fourth driving assembly;

[0047] an eighth shaft, one end of which is connected to the seventh shaft, and the other end of which is connected to the second execution end.

[0048] Further, the second execution end comprises:

[0049] a mounting assembly, disposed on one end of the eighth shaft;

[0050] a rotary driving assembly, disposed on the mounting assembly;

[0051] a matching assembly, disposed on an output end of the rotary driving assembly.

[0052] Further, the first bearing aid comprises:

[0053] a back strap, used for detachable fixation on a human body;

[0054] A first adapting assembly is arranged on the outer side of the back strap, and cooperates with the first execution end to achieve detachable connection of the first multi-axis robot and the first load assisting tool.

[0055] Further, the second load assisting tool comprises:

[0056] An upper limb strap is used for detachable fixation at a part of the upper limb of a human body;

[0057] A second adapting assembly is arranged on the outer side of the upper limb strap, and cooperates with the second execution end to achieve detachable connection of the second multi-axis robot and the second load assisting tool.

[0058] Further, the third load assisting tool comprises:

[0059] A lower limb strap is used for detachable fixation at a part of the lower limb of a human body;

[0060] A third adapting assembly is arranged on the outer side of the lower limb strap, and cooperates with the third execution end to achieve detachable connection of the third multi-axis robot and the third load assisting tool.

[0061] Further, the first load assisting tool, the second load assisting tool and the third load assisting tool are all provided with a sensor assembly.

[0062] The present application has the following beneficial effects:

[0063] The adaptive method proposed in the present application is based on the innovative adaptive reconfigurable virtual exoskeleton rehabilitation robot system. According to the rehabilitation movement of the main limb joints of the human body, such as the shoulder joint, elbow joint, wrist joint, hip joint, knee joint and ankle joint, when the patient is placed in a designated position, the system automatically identifies (such as image recognition) the position of the patient's shoulder joint according to the selected rehabilitation part. The corresponding mechanical arm automatically docks the patient's shoulder and upper arm, and prepares for rehabilitation. The system internally calls and corrects the corresponding joint motion model according to the physiological structure of the joint and the patient's condition (range of motion, pain level), and starts training according to the specified rehabilitation training scheme. BRIEF DESCRIPTION OF DRAWINGS

[0064] In order to more clearly illustrate the technical solutions in the specific embodiments of the present application or the prior art, the drawings required to be used in the specific embodiments or prior art description will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual proportions.

[0065] Figure 1 A working schematic diagram of a conventional upper limb exoskeleton robot in the prior art is shown.

[0066] Figure 2 A perspective view of an adaptive reconfigurable virtual exoskeleton rehabilitation robot system according to an embodiment of the present application is shown.

[0067] Figure 3 A top view of an adaptive reconfigurable virtual exoskeleton rehabilitation robot system according to an embodiment of the present application is shown.

[0068] Figure 4 A perspective view of an adaptive reconfigurable virtual exoskeleton rehabilitation robot system according to an embodiment of the present application is shown when the system is in a sitting state for rehabilitation.

[0069] Figure 5 A perspective view of an adaptive reconfigurable virtual exoskeleton rehabilitation robot system according to an embodiment of the present application is shown when the system is in a standing state for rehabilitation.

[0070] Figure 6 A perspective view of a second multi-axis robot of an adaptive reconfigurable virtual exoskeleton rehabilitation robot system according to an embodiment of the present application is shown.

[0071] Figure 7 A flowchart of an adaptive method of an adaptive reconfigurable virtual exoskeleton rehabilitation robot system according to an embodiment of the present application is shown.

[0072] Figure 8 A partial flowchart of an adaptive method of an adaptive reconfigurable virtual exoskeleton rehabilitation robot system according to an embodiment of the present application is shown.

[0073] Figure 9 A partial flowchart of an adaptive method of an adaptive reconfigurable virtual exoskeleton rehabilitation robot system according to an embodiment of the present application is shown.

[0074] In the figure, 1 is a first multi-axis robot, 101 is a first base, 102 is a first driving assembly, 103 is a first shaft, 104 is a second shaft, 105 is a second driving assembly, 106 is a third shaft, 107 is a fourth shaft, 2 is a second multi-axis robot, 201 is a second base, 202 is a third driving assembly, 203 is a fifth shaft, 204 is a sixth shaft, 205 is a fourth driving assembly, 206 is a seventh shaft, 207 is an eighth shaft, 3 is a third multi-axis robot, 4 is a first carrying assistive device, 401 is a back strap, 402 is a first adaptive assembly, 5 is a second carrying assistive device, 501 is an upper limb strap, 502 is a second adaptive assembly, 6 is a third carrying assistive device, 601 is a lower limb strap, 602 is a third adaptive assembly, 7 is a first execution end, 8 is a second execution end, 801 is a mounting assembly, 802 is a rotary driving assembly, 803 is a matching assembly, and 9 is a third execution end. DETAILED DESCRIPTION

[0075] The advantages and effects of the present application can be easily understood by those skilled in the art from the description of the specific embodiments of the present application. The present application can also be implemented or applied in other different specific embodiments, and the details in the description can be modified or changed based on different views and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0076] In the description of the present application, unless otherwise specified, the meaning of "a plurality of" is two or more; the orientations or positional relationships indicated by the terms "upper", "lower", "left", "right", "inner", "outer", "front end", "rear end", "head", "tail" and the like are based on the orientations or positional relationships shown in the drawings, and are only for the purpose of facilitating the description of the present application and simplifying the description, and are not intended to indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second", "third" and the like are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0077] In the description of the present application, it should be noted that, unless otherwise specified and limited, the terms "connected", "connected" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0078] The specific embodiments of the present application will be further described in detail below in conjunction with the drawings and examples.

[0079] Example 1: Adaptive Reconfigurable Virtual Exoskeleton Rehabilitation Robot System

[0080] like Figure 1 The diagram shown illustrates the operation of a conventional upper limb exoskeleton robot in the prior art. In existing technologies, the robot body is attached to two points—the hind arm and forearm—for wearability. The robot is designed to mimic the human arm, exhibiting similar movement characteristics, and can assist patients in performing a wide range of upper limb rehabilitation training tasks.

[0081] Unlike existing technologies, embodiments of the present invention provide an adaptive reconfigurable virtual exoskeleton rehabilitation robot system, such as... Figure 2 and Figure 3 As shown, the adaptive reconfigurable virtual exoskeleton rehabilitation robot system includes:

[0082] A first multi-axis robot 1, the first multi-axis robot 1 having a first end effector 7;

[0083] A second multi-axis robot 2, the second multi-axis robot 2 having a second end effector 8;

[0084] A third multi-axis robot 3, the third multi-axis robot 3 having a third end effector 9;

[0085] The first support bracket 4 is detachably connected to the first execution end 7;

[0086] The second support bracket 5 is detachably connected to the second execution end 8;

[0087] The third support device 6 is detachably connected to the third execution end 9.

[0088] The system designed in this embodiment of the invention employs multi-robot collaborative work. Specifically, it consists of three robots and three support devices. The three support devices are simply fixed to the upper body, upper limbs, and lower limbs of the human body, respectively. According to rehabilitation needs, the joints to be rehabilitated corresponding to each support device can be easily adjusted. After the support devices are adjusted to their corresponding positions, each robot works to connect its end effector with the corresponding support device. The connection method includes magnetic attraction, snap-fit, etc. After the connection is completed, the movement of each robot helps the user achieve rehabilitation.

[0089] Therefore, the embodiment can realize specific rehabilitation training tasks by wearing two collaborative robots on the upper arm and the forearm of the upper limb respectively and then controlling the two robots to cooperate with each other. The system has a structure different from a general exoskeleton, in which multiple collaborative robots are physically connected through arms and can be virtually connected by means of an algorithm, and has the flexibility of an exoskeleton robot and the convenient wearability of an end guiding robot.

[0090] In the embodiment, due to the easy wearability of the auxiliary tool, rehabilitation training in multiple body positions can be realized, such as Figures 2-5 Figure 2 and Figure 4 indicate rehabilitation training in a sitting position, Figure 3 Figure 5 and rehabilitation training in a standing position.

[0091] In addition, in the embodiment, multiple robots are used to cooperate, and there is no connection relationship between the multiple robots. Therefore, in the rehabilitation training process, one or more robots can be selectively used to perform rehabilitation training on the required joint part, that is, the number of configured collaborative robots can be increased or decreased, the configured collaborative robot group can be reconfigured, the system can be quickly reconstructed, and a specified rehabilitation application task can be completed. For example, Figure 3 Figure 5 As shown in the figures, only two multi-axis robots can be used to realize the corresponding functions.

[0092] In some embodiments, the specific structure of the first multi-axis robot is provided, as shown in Figure 4 The first multi-axis robot 1 comprises:

[0093] a first base 101;

[0094] a first driving assembly 102, which is arranged on the first base 101;

[0095] a first shaft 103, which is arranged on the output end of the first driving assembly 102;

[0096] a second shaft 104, which is arranged on the first shaft 103;

[0097] a second driving assembly 105, which is arranged on the second shaft 104;

[0098] a third shaft 106, which is arranged on the output end of the second driving assembly 105;

[0099] ​​​A fourth shaft 107, one end of the fourth shaft 107 is connected to the third shaft 106, and the other end is connected to the first execution end 7.

[0100] The working principle of the first multi-axis robot 1 is as follows:

[0101] The first driving assembly 102 drives the first shaft 103 to rotate. During the movement of the first shaft 103, one end of the second shaft 104 rotates around the axis of the first shaft 103, so that the second driving assembly 105 also moves. Similarly, during the operation of the second driving assembly 105, one end of the fourth shaft 107 also rotates around the axis of the third shaft 106, so that the first execution end 7 has multiple degrees of freedom, that is, its movement trajectory in space is along the x, y and z axes. Three direction movement, so that the first multi-axis robot 1 can find the first bearing auxiliary tool 4 and connect it, and then drive the first bearing auxiliary tool 4 to move through the first execution end 7 to realize the upper body auxiliary rehabilitation training function.

[0102] In some embodiments, the specific structure of the second multi-axis robot is provided, as shown in Figure 6 The second multi-axis robot 2 includes:

[0103] A second base 201;

[0104] A third driving assembly 202 is arranged on the second base 201;

[0105] A fifth shaft 203 is arranged on the output end of the third driving assembly 202;

[0106] A sixth shaft 204 is arranged on the fifth shaft 203;

[0107] A fourth driving assembly 205 is arranged on the sixth shaft 204;

[0108] A seventh shaft 206 is arranged on the output end of the fourth driving assembly 205;

[0109] An eighth shaft 207, one end of the eighth shaft 207 is connected to the seventh shaft 206, and the other end is connected to the second execution end 8.

[0110] The working principle of the second multi-axis robot 2 is as follows:

[0111] The third driving assembly 202 drives the fifth shaft 203 to rotate, and in the movement of the fifth shaft 203, one end of the sixth shaft 204 rotates around the axis of the fifth shaft 203, so that the fourth driving assembly 205 also moves, and in the same way, when the fourth driving assembly 205 works, one end of the eighth shaft 207 also rotates around the axis of the seventh shaft 206, so that in this structure, the second execution end 8 has multiple degrees of freedom, that is, its movement trajectory in space is along the x, y and z axes, so that the second execution end 8 of the second multi-axis robot 2 can find the second bearing auxiliary tool 5 and connect with it, and then drive the second bearing auxiliary tool 5 to move through the first execution end 8 to realize the upper body auxiliary rehabilitation training function.

[0112] The structure of the third multi-axis robot 3 can be consistent with that of the second multi-axis robot 2, which will not be described in detail here. It should be noted that although the structure principle of the third multi-axis robot 3 is consistent with that of the second multi-axis robot 2, the stroke and range of movement of the third execution end 9 are not consistent, and the stroke and range of movement of the third execution end 9 can be determined according to actual conditions, for example, the length range of the lower limbs of an average person can be used to determine the stroke and range of movement.

[0113] In some embodiments, the structure principles of each execution end can be consistent, and it should be noted that the consistent structure principles do not mean that the sizes are consistent, and the sizes of each execution end can be inconsistent. For example, as shown in the figure, the second execution end 8 includes: Figure 6

[0114] The mounting assembly 801 is arranged at one end of the eighth shaft 207.

[0115] The rotary driving assembly 802 is arranged at the mounting assembly 801.

[0116] The cooperation assembly 803 is arranged at the output end of the rotary driving assembly 802.

[0117] The specific selection of the cooperation assembly 803 is determined according to the selection of the second bearing auxiliary tool, for example, the second bearing auxiliary tool can be selected as an object that can be magnetically attracted, and then the cooperation assembly 803 can be selected as an electromagnet. For example, the second bearing auxiliary tool is selected as a card slot type assembly, and then the cooperation assembly 803 is an assembly (such as a plug rod) matched with the card slot, and under the action of the movement of the multi-axis robot and the rotary driving assembly 802, the combination with the card slot type assembly is realized.

[0118] The rotary driving assembly 802 can be specifically implemented as a rotary motor.

[0119] The mounting assembly 801 can be specifically implemented as a rod. ​

[0120] In some embodiments, as shown in Figure 3 The first carrying aid 4 comprises:

[0121] a back strap 401 for detachably fixing to a human body;

[0122] a first adapting assembly 402 disposed on the outer side of the back strap 401, which cooperates with the first execution end 7 to realize detachable connection of the first multi-axis robot 1 and the first carrying aid 4.

[0123] In some embodiments, as shown in Figure 2 The second carrying aid 5 comprises:

[0124] an upper limb strap 501 for detachably fixing to a part of the upper limb of a human body;

[0125] a second adapting assembly 502 disposed on the outer side of the upper limb strap 501, which cooperates with the second execution end 8 to realize detachable connection of the second multi-axis robot 2 and the second carrying aid 5.

[0126] In some embodiments, as shown in Figure 2 The third carrying aid 6 comprises:

[0127] a lower limb strap 601 for detachably fixing to a part of the lower limb of a human body;

[0128] a third adapting assembly 602 disposed on the outer side of the lower limb strap 601, which cooperates with the third execution end 9 to realize detachable connection of the third multi-axis robot 3 and the third carrying aid 6.

[0129] It should be noted that the specific structure of the first adapting assembly 402, the second adapting assembly 502 and the third adapting assembly 602 should be matched with the first execution end 7, the second execution end 8 and the third execution end 9, for example, when the first execution end 7, the second execution end 8 and the third execution end 9 are selected as electromagnets, the first adapting assembly 402, the second adapting assembly 502 and the third adapting assembly 602 can be selected as magnetic suction blocks, and when the first execution end 7, the second execution end 8 and the third execution end 9 are selected as insertion rods, the first adapting assembly 402, the second adapting assembly 502 and the third adapting assembly 602 can be selected as card slot type assemblies matched with the insertion rods.

[0130] In some embodiments, the first bearing aid 4, the second bearing aid 5 and the third bearing aid 6 are each provided with a sensor assembly.

[0131] Specifically, the sensor assembly includes one of a pressure sensor and a pose sensor and a combination thereof.

[0132] It should be noted that the types of sensors included in the sensor assembly include but are not limited to pressure sensors and pose sensors, and the number of each sensor type can be one or more, used to collect corresponding rehabilitation parameters such as pressure, joint posture, etc. to guide rehabilitation training.

[0133] In some embodiments, the adaptive reconfigurable virtual exoskeleton rehabilitation robot system further comprises a camera assembly. The camera assembly is used to shoot images of the rehabilitation training, which can be used as a data source for subsequent extraction of the motion trajectory of the user and the motion trajectory of each robot, to ensure effective cooperation of multiple robots through the intelligent control system.

[0134] It should be noted that the above-mentioned various drive assemblies (first drive assembly to fourth drive assembly and rotating drive assembly) can be implemented as servo motors.

[0135] Embodiment 2: Adaptive method of adaptive reconfigurable virtual exoskeleton rehabilitation robot system

[0136] The embodiment of the present application proposes an adaptive method of an adaptive reconfigurable virtual exoskeleton rehabilitation robot system, which is based on the adaptive reconfigurable virtual exoskeleton rehabilitation robot system as described in any of the embodiments of embodiment 1, as shown in the figure, the adaptive method specifically includes the following steps: Figure 5

[0137] Step S100, according to the image data, the position data of the first bearing aid, the position data of the second bearing aid and the position data of the third bearing aid and the corresponding joint parts of the first bearing aid, the second bearing aid and the third bearing aid are extracted.

[0138] ​Exemplarily, based on the existing image recognition method, the first bearing auxiliary tool, the second bearing auxiliary tool, the third bearing auxiliary tool and the human body are recognized according to the image data, an original point coordinate is determined, so as to determine the position data of the first bearing auxiliary tool, the position data of the second bearing auxiliary tool and the position data of the third bearing auxiliary tool, and the first bearing auxiliary tool, the second bearing auxiliary tool and the third bearing auxiliary tool are correspondingly arranged on the human body. In combination with the basic joint structure of the human body, each joint region of each human body is determined, and according to the positions of the first bearing auxiliary tool, the second bearing auxiliary tool and the third bearing auxiliary tool on the human body, the joint parts corresponding to the first bearing auxiliary tool, the second bearing auxiliary tool and the third bearing auxiliary tool can be determined. The joints corresponding to the second bearing auxiliary tool and the third bearing auxiliary tool generally include shoulder joint, elbow joint, wrist joint, hip joint, knee joint and ankle joint, and the first bearing auxiliary tool plays a role of fixed support and generally corresponds to the position of the spine.

[0139] In step S200, the first multi-axis robot, the second multi-axis robot or the third multi-axis robot is controlled to move according to the position data of the first bearing auxiliary tool, the position data of the second bearing auxiliary tool and the position data of the third bearing auxiliary tool, so that the first execution end, the second execution end or the third execution end is connected with the first bearing auxiliary tool, the second bearing auxiliary tool or the third bearing auxiliary tool.

[0140] This step can be realized by a processor which is in signal connection with the first multi-axis robot, the second multi-axis robot or the third multi-axis robot to send a control signal so that the first multi-axis robot, the second multi-axis robot or the third multi-axis robot can move under the control of the processor.

[0141] In some embodiments, as shown in the figure, the control of the movement of the first multi-axis robot, the second multi-axis robot or the third multi-axis robot according to the position data of the first bearing auxiliary tool, the position data of the second bearing auxiliary tool and the position data of the third bearing auxiliary tool so that the first execution end, the second execution end or the third execution end is connected with the first bearing auxiliary tool, the second bearing auxiliary tool or the third bearing auxiliary tool specifically includes: Figure 6

[0142] In step S201, the position data of the first execution end, the second execution end or the third execution end is determined.

[0143] Exemplarily, the position data of the first execution end, the second execution end or the third execution end is set as a fixed initial position, that is, after the first multi-axis robot, the second multi-axis robot or the third multi-axis robot completes the rehabilitation training, it will automatically return to the original position (i.e. the fixed initial position), so that the position data of each execution end can be conveniently obtained in a new round of configuration transformation, so as to better execute the subsequent steps S202 and S203.

[0144] ​Step S202, according to the position data of the first execution end, the second execution end or the third execution end and the position data of the first bearing auxiliary tool, the second bearing auxiliary tool and the third bearing auxiliary tool, determining the first trajectory of the first execution end moving to the position connected with the first bearing auxiliary tool, the first trajectory of the second execution end moving to the position connected with the second bearing auxiliary tool or the first trajectory of the third execution end moving to the position connected with the third bearing auxiliary tool.

[0145] In the case that the position data of the first execution end, the second execution end or the third execution end has been determined in step S201, the position data of the first bearing auxiliary tool, the position data of the second bearing auxiliary tool and the position data of the third bearing auxiliary tool determined previously are combined. Only as an example, the position data is selected as a coordinate point, according to the two coordinate points determined, combined with the motion characteristics of the robot, the first trajectory is set.

[0146] Step S203, according to the first trajectory, the second trajectory or the third trajectory, controlling the first multi-axis robot, the second multi-axis robot or the third multi-axis robot to move, so that the first execution end, the second execution end or the third execution end is connected with the first bearing auxiliary tool, the second bearing auxiliary tool or the third bearing auxiliary tool.

[0147] In the case that the first trajectory has been determined, the corresponding multi-axis robot is controlled to move according to the preset first trajectory, so as to realize the connection between the multi-axis robot and the corresponding bearing auxiliary tool, thereby completing the reconstruction.

[0148] Step S300, according to the corresponding joint parts of the first bearing auxiliary tool, the second bearing auxiliary tool and the third bearing auxiliary tool, determining the motion trajectory of the first execution end, the second execution end and the third execution end.

[0149] This step is used to determine the training scheme, that is, according to the joint part to be rehabilitated, the corresponding motion trajectory is determined. Taking the shoulder joint as an example, the corresponding is the second bearing auxiliary tool and the second execution end, that is, the motion trajectory of the second execution end is the corresponding shoulder joint rehabilitation scheme. The motion trajectory corresponding to the rehabilitation training of the shoulder joint is generally elliptical, that is, the shoulder joint is continuously driven to rotate for rehabilitation. The specific activity range is determined according to different conditions. Therefore, after the joint part is determined, the motion trajectory can be determined according to the rehabilitation scheme, so as to realize the rehabilitation training scheme through the robot.

[0150] In some embodiments, after determining the motion trajectory of the first execution end, the second execution end and the third execution end according to the corresponding joint parts of the first bearing auxiliary tool, the second bearing auxiliary tool and the third bearing auxiliary tool, as shown in Figure 7 The method further comprises:

[0151] Step S400, according to the motion trajectory of the first, second and third execution ends, the motion equation of the first, second and third execution ends is determined respectively.

[0152] For example, according to different joint positions, the motion trajectory of the corresponding first, second and third execution ends is different, and according to the motion trajectory, the corresponding motion equation is determined by using parameter fitting.

[0153] For example, the first execution end corresponds to the back, which generally realizes push-pull action, and can be expressed as y=b, wherein b is the push-pull stroke, and y is the position of the first execution end. If expressed in three-dimensional coordinates, at least two-dimensional coordinates of b are fixed, and the remaining one-dimensional coordinate is active. For example, expressed as (x1, y1, z1), wherein x1 and y1 are fixed values, and y1 is active. The maximum value of y1 is determined by the push-pull stroke, which can be expressed as the accumulation of push-pull speed over time. The push-pull speed alternates between positive and negative within a certain period, i.e. the push action is positive and the pull action is negative, so that the maximum value of y1 is within the push-pull stroke.

[0154] For example, when the shoulder joint is an elliptical trajectory, it can be expressed as x 2 / a 2 +y 2 / b 2 =1, wherein x and y are coordinates of the second execution end, and a and b are control parameters of the motion trajectory.

[0155] Step S500, according to the input instruction, the motion equation is adjusted.

[0156] For example, the input instruction is determined according to the user's response during the rehabilitation training process. If the user feels that the training trajectory causes the range to be too large, the corresponding parameters of the motion equation can be adjusted to change the motion trajectory and reduce the activity range, so as to realize the fine adjustment of the rehabilitation training trajectory during the training process. The way to obtain the input instruction can be manual adjustment by medical staff, or automatic adjustment based on voice signal. For example, if the signal of "hand is too tight" is collected, the motion trajectory range of the second execution end is reduced. If the signal of "hand is too loose" is collected, the motion trajectory range of the second execution end is increased. If the motion trajectory is an ellipse, the parameters a and b are fine-tuned to achieve this. The principle of fine-tuning is that the motion trajectory cannot exceed the scope of the safe motion trajectory, which is the motion trajectory corresponding to the maximum motion range without harming the human body. Of course, if the signal of "leg is too tight" is collected, the motion trajectory range of the third execution end is reduced, and so on. The present embodiment is not described in detail here.

[0157] The above embodiments are only used for illustrating the present application, and are not intended to limit the present application. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application. Therefore, all equivalent technical solutions belong to the scope of the present application, and the patent protection scope of the present application should be defined by the claims.

Claims

1. An adaptive method for an adaptive reconfigurable virtual exoskeleton rehabilitation robot system, the method comprising: The system comprises: a first multi-axis robot having a first execution end; a second multi-axis robot having a second execution end; a third multi-axis robot having a third execution end; a first bearing aid detachably connected with the first execution end; a second bearing aid detachably connected with the second execution end; a third bearing aid detachably connected with the third execution end; a camera assembly for collecting image data; The adaptive method comprises: According to the image data, the position data of the first bearing aid, the position data of the second bearing aid and the position data of the third bearing aid and the corresponding joint positions of the first bearing aid, the second bearing aid and the third bearing aid are extracted; According to the position data of the first bearing aid, the position data of the second bearing aid and the position data of the third bearing aid, the movement of the first multi-axis robot, the second multi-axis robot or the third multi-axis robot is controlled to make the first execution end, the second execution end or the third execution end connected with the first bearing aid, the second bearing aid or the third bearing aid; According to the corresponding joint positions of the first bearing aid, the second bearing aid and the third bearing aid, the movement trajectory of the first execution end, the second execution end and the third execution end is determined; After determining the movement trajectory of the first execution end, the second execution end and the third execution end according to the corresponding joint positions of the first bearing aid, the second bearing aid and the third bearing aid, the method further comprises: According to the movement trajectory of the first execution end, the second execution end and the third execution end, the movement equation of the first execution end, the second execution end and the third execution end is respectively determined; According to the input instruction, the movement equation is adjusted.

2. The adaptive method of the adaptive reconfigurable virtual exoskeleton rehabilitation robot system according to claim 1, wherein, According to the position data of the first bearing aid, the position data of the second bearing aid and the position data of the third bearing aid, the movement of the first multi-axis robot, the second multi-axis robot or the third multi-axis robot is controlled to make the first execution end, the second execution end or the third execution end connected with the first bearing aid, the second bearing aid or the third bearing aid, specifically comprising: The position data of the first execution end, the second execution end or the third execution end is determined; According to the position data of the first execution end, the second execution end or the third execution end and the position data of the first bearing aid, the position data of the second bearing aid and the position data of the third bearing aid, the first trajectory of the first execution end moving to the position connected with the first bearing aid, the first trajectory of the second execution end moving to the position connected with the second bearing aid or the first trajectory of the third execution end moving to the position connected with the third bearing aid is determined; Controlling the first multi-axis robot, the second multi-axis robot or the third multi-axis robot to move according to the first trajectory, the second trajectory or the third trajectory, so that the first end effector, the second end effector or the third end effector is connected with the first load assisting aid, the second load assisting aid or the third load assisting aid.

3. The adaptive method of the adaptive reconfigurable virtual exoskeleton rehabilitation robot system according to claim 1, wherein, The first multi-axis robot comprises: a first base; a first driving assembly arranged on the first base; a first shaft arranged on an output end of the first driving assembly; a second shaft arranged on the first shaft; a second driving assembly arranged on the second shaft; a third shaft arranged on an output end of the second driving assembly; a fourth shaft, one end of which is connected with the third shaft, and the other end of which is connected with the first end effector.

4. The adaptive method of the adaptive reconfigurable virtual exoskeleton rehabilitation robot system according to claim 1, wherein, The second multi-axis robot comprises: a second base; a third driving assembly arranged on the second base; a fifth shaft arranged on an output end of the third driving assembly; a sixth shaft arranged on the fifth shaft; a fourth driving assembly arranged on the sixth shaft; a seventh shaft arranged on an output end of the fourth driving assembly; an eighth shaft, one end of which is connected with the seventh shaft, and the other end of which is connected with the second end effector.

5. The adaptive method of the adaptive reconfigurable virtual exoskeleton rehabilitation robot system according to claim 4, wherein, The second end effector comprises: a mounting assembly arranged on one end of the eighth shaft; a rotary driving assembly arranged on the mounting assembly; a matching assembly arranged on an output end of the rotary driving assembly.

6. The adaptive method of the adaptive reconfigurable virtual exoskeleton rehabilitation robot system according to claim 1, wherein, The first load assisting aid comprises: a back strap for detachably fixing on a human body; a first matching assembly arranged on an outer side surface of the back strap, the first matching assembly being matched with the first end effector to realize detachable connection between the first multi-axis robot and the first load assisting aid.

7. The adaptive method of the adaptive reconfigurable virtual exoskeleton rehabilitation robot system according to claim 1, wherein, The second load assisting aid comprises: an upper limb strap for detachably fixing on a part of a human upper limb; a second matching assembly arranged on an outer side surface of the upper limb strap, the second matching assembly being matched with the second end effector to realize detachable connection between the second multi-axis robot and the second load assisting aid.

8. The adaptive method of the adaptive reconfigurable virtual exoskeleton rehabilitation robot system according to claim 1, wherein, The third load assisting aid comprises: a lower limb strap for detachably fixing on a part of a human lower limb; a third matching assembly arranged on an outer side surface of the lower limb strap, the third matching assembly being matched with the third end effector to realize detachable connection between the third multi-axis robot and the third load assisting aid.

9. The adaptive method of the adaptive reconfigurable virtual exoskeleton rehabilitation robot system according to claim 1, wherein, The first load assisting aid, the second load assisting aid and the third load assisting aid are all provided with a sensor assembly.

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