548 results about "Exoskeleton robot" patented technology

The invention discloses a wearable exoskeleton man-machine cooperation real-time control method based on artificial intelligence, and relates to the technical field of exoskeleton robots, and the method comprises the steps: recognizing an exoskeleton scene in power grid maintenance, analyzing the demand degree, and constructing a maintenance demand sequence; determining a collaborative analysis scene based on the maintenance demand sequence; performing quantitative analysis on the man-machine confrontation event in the collaborative analysis scene to obtain a collaborative deviation value and generate a collaborative analysis signal; constructing a data set according to signal extraction features, and predicting man-machine cooperative control behaviors by using a genetic algorithm; finally, the exoskeleton is controlled in real time according to prediction, the man-machine cooperation degree is judged, and the control mode is switched when necessary. The exoskeleton man-machine cooperation performance is improved, the cooperation problem can be found in time, the control strategy is optimized, and it is guaranteed that electric power overhaul work is conducted efficiently and safely.

The invention provides an exoskeleton robot self-adaptive control method and related equipment, and relates to the technical field of exoskeleton robots. The method comprises the following steps: acquiring motion information and position information of each movable joint in the exoskeleton robot; inputting the motion information and the position information into a pre-trained angle prediction model to obtain a target angle of each movable joint; the difference value between the target angle and the actual angle output by the exoskeleton controller serves as an angle error; constructing a virtual control quantity based on the angle error; the virtual control quantity is combined with motion parameters of the exoskeleton robot to be input into a disturbance observer, and estimated disturbance is generated; the angle error, the virtual control quantity and the estimated disturbance are input into an adaptive neural network controller, and the control torque of each movable joint is obtained; and based on the control torque of each movable joint, generating a control signal of each movable joint. The method and the device have good control precision.

The invention discloses an exoskeleton robot power auxiliary control method based on artificial intelligence, and relates to the technical field of intelligent control. The method comprises the steps that a power device of the exoskeleton robot is determined; acquiring a plurality of indexes for power output of the auxiliary power device; obtaining a first motion scene, and performing parameter learning of an index sequence on the plurality of indexes according to the correlation between the first motion scene and the plurality of indexes to obtain auxiliary learning parameters; the power device controls a power motor to adjust power parameters according to the auxiliary learning parameters. The technical problems that in the prior art, power-assisted control of an exoskeleton robot is not intelligent and accurate enough, power output is difficult to dynamically adjust according to a specific motion scene, energy efficiency is low, and user experience is poor are solved. Accurate adjustment of power parameters of the power device is achieved, and therefore the technical effects of improving energy efficiency and enhancing user experience are achieved.

The invention relates to the technical field of robot control and somatosensory interaction, and particularly discloses a knee joint exoskeleton robot control system based on somatosensory interaction. The system comprises a somatosensory signal acquisition and fusion module, a personalized dynamic model construction module, an adaptive intention decision module, a variable impedance compliance control module and a rehabilitation process evaluation and parameter self-tuning module. Through cooperative work of the modules, precise recognition of the motion intention of a wearer, online construction of an individualized biomechanical model and flexible and adaptive motion control are achieved, control parameters can be automatically adjusted according to the rehabilitation process, and therefore the flexibility and individuation level of man-machine interaction and the effectiveness of rehabilitation training are improved.

The invention discloses a multifunctional exoskeleton robot. The robot comprises a basic support; the two groups of lower limb units are used for supporting legs; the lower limb units are connected with the basic support through hip driving units, and the hip driving units are used for driving the lower limb units to rotate around a vertical shaft so as to simulate internal and external rotation actions of hip joints. And when the lower limb unit rotates around the vertical shaft, the position of the rotating shaft is always unchanged, and the rotating shaft coincides with the internal and external rotation axis of the hip joint of the human body. The hip driving unit is used for connecting the lower limb unit and driving the lower limb unit to rotate so as to simulate the internal and external rotation action of the hip joint, the hip driving unit can enable the position of a rotating shaft of the lower limb unit to be unchanged all the time when the lower limb unit rotates around a vertical shaft, and the rotating shaft always coincides with the internal and external rotation axis of the hip joint of the human body. The exoskeleton robot can adjust the lower limb postures more naturally, and it is guaranteed that the motion of the human body during steering has better stability.

Embodiments of the present application set forth a trajectory tracking control method for an exoskeleton robot, comprising: in a first stage, when an exoskeleton robot is not applied to a subject, determining system interference of a power system of the exoskeleton robot, and compensating an output parameter of a power model of the exoskeleton robot; in a second stage, when the exoskeleton robot is applied to a subject, isolating the system interference from a human-machine interaction torque of the second stage, and determining the human-machine interaction torque; inputting the human-machine interaction torque into a controller, and using the controller to determine an assistance of the power system of the exoskeleton robot.

The invention relates to a hip and knee joint walking aid exoskeleton robot. The double-motor hip joint mechanism is composed of a back frame, an adjustable waist mechanism, two bilaterally-symmetrical double-motor hip joint mechanisms, two bilaterally-symmetrical thigh length adjusting mechanisms and two bilaterally-symmetrical knee joint mechanisms. The two double-motor hip joint mechanisms are connected to the two sides of the back frame and the two sides of the adjustable waist mechanism respectively, the thigh length adjusting mechanisms are connected with the double-motor hip joint mechanisms, and the knee joint mechanisms are connected with the thigh length adjusting mechanisms. When the wearer performs low-speed or low-load movement, only a second motor, close to the thigh length adjusting mechanism, of the dual-motor hip joint mechanism is activated, and when the wearer performs high-speed or high-load movement, the first motor and the second motor are activated at the same time. The hip and knee joint combined assisting mode is adopted, the walking assisting performance and the wearing comfort degree of the exoskeleton are balanced, on the premise that the wearing comfort degree is not affected, the maneuverability and the load capacity are enhanced to the maximum degree, and the fatigue degree is greatly reduced.

The invention provides an exoskeleton robot impedance adjustment and trajectory planning method, a medium and equipment, and relates to the technical field of exoskeleton robot control, and the method comprises the steps: obtaining a multi-dimensional original signal related to exoskeleton movement, and carrying out the preprocessing of the multi-dimensional original signal; the exoskeleton complex motion trail is decomposed into a basic motion component and a working condition adaptive component; adjusting the Fourier coefficient of the abnormal harmonic wave to suppress the abnormal harmonic wave, and obtaining an anti-interference basic motion component according to the adjusted Fourier coefficient; performing topographic feature matching and gain control on the working condition adaptive component to obtain a reconstructed working condition adaptive component; the anti-interference basic motion component and the reconstruction working condition adaptive component are recombined to obtain a final motion track; according to the error between the final motion track and the expected track of the exoskeleton, adjusting parameters of the impedance model in real time, so that the exoskeleton tracking expected track is optimized in real time. According to the method, the application scene of the exoskeleton can be widened, and the practicability of the exoskeleton in a complex environment is remarkably improved.

The invention discloses a fine-grained gait sub-phase recognition method and device based on spatio-temporal feature fusion, and aims at accurately recognizing eight types of gait sub-phases when a lower limb rehabilitation exoskeleton robot walks. The method comprises the steps that gait multi-source data are obtained through an integrated multi-source heterogeneous sensor, a gait sub-phase label distribution algorithm is designed, and a gait sub-phase label distribution algorithm is designed; constructing a training data set in combination with a sliding overlapping window technology; designing a feature extraction module based on the heterogeneous parallel convolution architecture, and combining the feature extraction module with BiLSTM to construct a spatial-temporal feature fusion model; introducing a weighted cross entropy loss function to improve the recognition capability of the model on the phase categories of gaits with fewer samples, and carrying out training evaluation through a layered K-fold cross validation strategy; and finally, an end-to-end gait sub-phase online recognition system is constructed based on Simulink, and real-time recognition of gait sub-phases is realized. The method is superior to the prior art in the aspects of recognition precision, fine-grained analysis and real-time performance, and the perception ability of the lower limb rehabilitation exoskeleton robot to the walking mode is remarkably improved.

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

The invention relates to the technical field of data processing, and discloses a gait rehabilitation evaluation method and system based on spatial-temporal characteristics of multi-modal data, a terminal and a storage medium, and the method comprises the steps: synchronously collecting myoelectricity, electroencephalogram and exoskeleton robot IMU signals of a dyskinesia crowd in a certain motion normal form in a specific period interval; a motion function scoring result of a doctor on a patient according to a scale is synchronously recorded, data preprocessing, channel selection and feature extraction are performed on myoelectricity, electroencephalogram and exoskeleton robot IMU signals, a model is established based on a neural network, motion evaluation is performed according to a fused feature array, and a motion function quantitative score is obtained; and the motion function state of the evaluated person is graded according to the score. According to the method, the multi-modal synchronous data rehabilitation evaluation model of the movement function of the dyskinesia crowd is constructed according to multiple information mining, and the movement function state of the evaluated person is rapidly evaluated, predicted and graded.

The invention provides an exoskeleton mountaineering intention recognition method and system and a storage medium, and belongs to the technical field of exoskeleton robot control, and the method comprises the steps of signal preprocessing and compensation, hierarchical feature extraction, multi-modal fusion recognition, control output, safety protection and the like. The method comprises the following steps: performing 50Hz power frequency filtering and 20-450Hz band-pass filtering on an electromyographic signal, and compensating signal distortion caused by muscle deformation by combining IMU (Inertial Measurement Unit) acceleration data; a dynamic four-dimensional channel is adopted to preferably extract myoelectricity features, and an improved attention mechanism model is utilized to extract multi-scale features; fusing multi-modal information such as myoelectricity and terrains based on a Bayesian fusion framework to realize intention recognition; meanwhile, triple safety protection strategies of joint angle hard limiting, falling detection and protection and signal loss fault-tolerant processing are set; the accuracy and control speed of motion intention recognition are improved, good safety performance is achieved, and the auxiliary effect and reliability of the exoskeleton in the mountaineering motion are effectively improved.

The invention discloses a flexible exoskeleton execution device and a flexible exoskeleton system for upper limb training, and relates to the technical field of robots. The problems that an existing exoskeleton robot is complex in mechanical structure, large in size, heavy in weight and large in inertia due to the fact that rigid driving is adopted for achieving actions such as joint bending and stretching or unfolding and folding are solved. The device comprises a bandage, a forward line wheel, a reverse line wheel, a line wheel driving mechanism and Bowden lines, the bandage is fixed to a limb, one end of one Bowden line is wound around the forward line wheel, the other end of the Bowden line is fixed to the bandage, one end of the other Bowden line is reversely wound around the reverse line wheel, and the other end of the other Bowden line is fixed to the bandage; the line wheel driving mechanism is arranged on the trunk and can drive the forward line wheel and the reverse line wheel to rotate reversely in the pre-tightening stage so as to achieve line winding and pre-tightening, the forward line wheel and the reverse line wheel are driven to rotate synchronously in the same direction in the working stage, and correction force is applied to the force exerting muscle group through the two Bowden lines which are wound and unwound alternately so as to correct the action of a wearer.

The invention relates to a robot track synchronization control method. According to the technical scheme, efficient collection and over-distance transmission of neural signals are achieved through the quantum entanglement technology, the brain movement intention is analyzed in combination with a high-sensitivity sensor and an intelligent algorithm, an instruction is transmitted to a robot power driving system through a low-delay communication architecture, and real-time synchronization of man-machine tracks is achieved through a two-way feedback system. Meanwhile, the movement flexibility of the robot is improved by adopting a lightweight design, the problems of high delay, low precision, poor interaction experience and the like in traditional man-machine synchronous control are solved, and the method can be widely applied to the fields of exoskeleton robots, industrial collaborative robots, rehabilitation medical robots and the like. The method has the advantages of being low in delay, high in precision, capable of improving interaction experience and wide in application range.

The invention relates to the field of exoskeleton robot technology and robot control, in particular to an intelligent self-adaptive exoskeleton robot multi-view sensing obstacle avoidance method and system. A binocular camera is combined with a laser radar to monitor the external environment, and when it is detected that an obstacle exists around a wearer, the obstacle avoidance is performed; and path searching is carried out by using an improved fast expansion random tree star algorithm, a control instruction is generated based on exoskeleton kinematics modeling, and the exoskeleton robot is driven to realize an obstacle avoidance function. The obstacle avoidance method can flexibly deal with various complex conditions in a dynamically changing environment, is high in real-time performance, can help a wearer to complete obstacle avoidance in some emergency conditions, fully reflects the intelligent self-adaptive capability of the system, and enhances the safety and reliability.

The invention discloses an exoskeleton robot fixed time control method based on output constraint and disturbance observation, relates to the field of robot control, constructs a fixed time neural controller (FTNC), and ensures that a system is stable in fixed time by combining a universal obstacle Lyapunov function (UBLF) and a preset performance function (PPF), so as to improve the system stability. The convergence time is irrelevant to the initial state; meanwhile, a radial basis function neural network (RBFNN) and a nonlinear disturbance observer (NDO) are adopted to jointly compensate the uncertainty of the model and the man-machine interaction disturbance; by dynamically adjusting UBLF boundary conditions, joint angle errors are limited, and training safety is guaranteed. According to the method, a fixed time control theory is adopted, fixed time is combined with a preset performance function (PPF) and a universal barrier Lyapunov function (UBLF), and the output of the exoskeleton robot is strictly limited, so that the rapid convergence and transient performance requirements of an exoskeleton system are met at the same time.

The invention discloses a rope-driven ankle-foot exoskeleton robot and a control method, the ankle-foot exoskeleton robot comprises a two-way driving module, a rope-driven driving module, a rope-driven driving module, a rope-driven driving module, a rope-driven driving module, a rope-driven driving module and a rope-driven driving module, the lower leg binding module is arranged on the lower leg, and the ankle-foot fixing module is worn on the foot. The two-way driving module transmits power to the shank binding module and the ankle and foot fixing module in sequence through a rope transmission device, and therefore ankle joints are driven to do dorsal flexion or plantar flexion motion. The ankle-foot exoskeleton robot is driven by a rope and has the advantages of being light in weight, easy to deploy, good in flexibility and the like, modular design is adopted for the assemblies, the relative positions of all the modules are easy to adjust, and the ankle-foot exoskeleton robot can adapt to the difference between individuals in the wearing process; the position of the rope transmission device on the shank binding structure is easy to adjust, and rapid conversion between plantar flexion and dorsal flexion can be achieved on the basis that the freedom degree of movement of the lower limbs of the patient is not interfered.

The utility model discloses an exoskeleton robot testing frame, which belongs to the technical field of exoskeleton robot testing, aims at solving the problems of poor stability and inconvenience in height adjustment of exoskeleton robot testing, and comprises a bottom frame and a support frame, the support frame is fixed on the side wall of the top end of the bottom frame, and a plurality of mounting plates are fixed on the side wall of the bottom end of the bottom frame. Universal wheels are fixed on the side wall of the bottom end of the mounting plate; the top frame is fixed to the side wall of one side of the supporting frame, a lifting frame is arranged under the top frame, and a lifting assembly is arranged on one side of the supporting frame; through the arrangement of the lifting assembly, the height position of the exoskeleton can be adjusted according to field application requirements, so that a worker can conveniently mount and dismount the exoskeleton, the working efficiency of the work at the earlier stage of exoskeleton testing is greatly improved, the motion flexibility of the exoskeleton and the use performance of the testing frame are improved, and the testing frame is suitable for popularization and application. And the device is suitable for exoskeletons with different heights, and the adaptability is high.

The invention discloses a real-time detection method and system for the lower limb binding state of an exoskeleton robot. The method comprises the following steps: acquiring a human body gait in a reversing motion state at present, performing feature extraction on the human body gait, and judging a current gait phase through the feature extraction; and then the lower limb binding state of the corresponding gait phase is detected in real time. The system comprises a state observer module, a gait phase recognition module, a binding detection module, an active disturbance rejection controller module and a CMSIS-DSP library module. Through a split-phase dynamic detection strategy, the real-time performance and reliability of binding and falling identification are remarkably improved.

The invention relates to the technical field of exoskeleton robots, in particular to an exoskeleton driving system with multi-mode torque coupling and intelligent-brain cooperative control, which comprises a flexible torque coupling mechanism, a holographic intelligent-brain cooperative control strategy, an energy safety integrated shield module and an ultra-light integrated magic body structure, wherein the flexible torque coupling mechanism is used for adapting to power and flexibility of different scenes and carrying out torque output and rigidity continuous adjustment; a holographic intelligent brain cooperative control strategy is fused with perception information, data are analyzed through quantum federal learning and a bionic algorithm, torque is optimized, and cooperative control is carried out; the energy and safety integrated shield module is used for constructing a double-safety system, and recycling is achieved through magnetorheological fluid kinetic energy recovery and shape memory alloy potential energy storage. The ultra-light integrated magic body structure is matched with the human body size to adjust the exoskeleton, and the man-machine cooperation response is improved through the composite carbon fiber material and the highly integrated design. Therefore, the problems of high energy consumption, complex mechanical structure, insufficient stability and the like in the prior art are solved.

The invention belongs to the technical field of robot control, and discloses an exoskeleton robot control method and system based on a brain-computer interface, and the method comprises the steps: obtaining brain-computer interface data, carrying out the preprocessing of the brain-computer interface data, obtaining the preprocessed brain-computer interface data, extracting motion features, and obtaining a motion feature vector; performing motion intention recognition on the motion feature vector by using an integrated learning model, generating a motion control instruction, and performing motion control according to the motion control instruction; and based on the obtained motion state feedback data, motion state adjustment is carried out by using a dynamic adjustment algorithm, a motion control instruction is dynamically adjusted according to a motion state adjustment result, and motion control optimization is carried out through the adjusted motion control instruction. The problems that in the prior art, the signal processing precision is low, the intention recognition accuracy is insufficient, and the control strategy is rigid are solved, and efficient cooperation of the exoskeleton robot and the motion intention of the user is achieved.

The invention discloses a multi-stage control method for a lower limb exoskeleton, is applied to the field of exoskeleton robots, and aims to solve the problem that the design control precision is reduced due to the uncertainty of a model in the prior art. According to the controller designed by the invention, in a high-level control layer, a human body training mode is determined by a motion intention of an operator so as to generate a reference gait track; in the middle control layer, a variable admittance controller is designed, and three training modes of human exoskeleton cooperative movement, namely a passive mode, an active mode and a passive-to-active mode, are planned. In a low-level control loop, a method with a radial basis function neural network estimation function and a fixed-time convergence controller with input dead zone compensation are provided so as to ensure that an exoskeleton joint position tracks an expected trajectory output by an admittance loop. In order to avoid the Zeno phenomenon of the designed controller, an event trigger mechanism is used to determine the execution time of sampling and transmitting signals. Finally, the effectiveness of the proposed control strategy is verified through simulation and experimental results.

The invention discloses an exoskeleton manipulator control method which comprises the following steps: constructing a hand biomechanical model, defining the degree of freedom of finger joints, and carrying out joint division modeling based on a drive kinetic equation of a Bowden cable; deploying a high-density sensor on the hand; s3, a hand specificity Bouc-Wen model is improved, an inter-finger coupling term is introduced to correct a lag state equation, and micro-lag parameters are identified through a hierarchical identification algorithm; s4, designing a self-adaptive cooperative control strategy, realizing fuzzy mapping from the electromyographic signals to the joint torque, and optimizing a cooperative tracking error through a multi-joint parameter coupling self-adaptive law; s5, dynamically compensating the mechanical resistance of the hand by adopting the corrected double-Kalman filter; and S6, optimizing closed-loop execution and a security policy. According to the exoskeleton manipulator control method, through the improved Bouc-Wen model and the self-adaptive cooperative control strategy, the mapping relation between the electromyographic signal and the action of the manipulator can be effectively processed, and the exoskeleton manipulator control method adapts to different operation environments.

The utility model relates to medical rehabilitation training equipment, in particular to a modularized flexible lower limb exoskeleton robot. Comprising a control part, a driving part, a transmission part, a hip joint body part and an ankle joint body part, the driving part communicates with the control part, the driving part is connected with the hip joint body part and the ankle joint body part through the transmission part, and the hip joint body part is arranged at the hip joint of the human body. The ankle joint body part is arranged at the ankle joint of the human body, and the driving part controls the hip joint body part and the ankle joint body part through the transmission part to assist lower limb movement. The power assisting device is compact in structure, small and exquisite, easy to wear, light in weight, portable, adjustable in height and suitable for being worn by people with different body types, a separated modular design principle is adopted, and a power assisting mode can be configured according to specific requirements so as to meet the use requirements of different users in different environments.

The invention belongs to the technical field of lower limb rehabilitation, and discloses a man-machine coupling system intelligent cooperative control method and device based on deterministic learning. Establishing a state-space equation of the lower limb exoskeleton robot, defining a tracking error, and designing a self-adaptive neural network controller of the lower limb exoskeleton robot; defining an expected position state vector and a real position state vector of the walking robot, solving a position state vector error, and designing a self-adaptive neural network controller of the walking robot; the method comprises the following steps: constructing a time-varying linear matrix, verifying the exponential stable convergence characteristic of the time-varying linear matrix under a continuous excitation condition on the basis of an adaptive neural network controller, storing nonlinear dynamic knowledge of a man-machine coupling system, and constructing a learning controller by using the learned knowledge. According to the method, while the unknown dynamic state of the man-machine coupling system is accurately modeled, the continuous excitation condition is ensured, so that accurate and stable trajectory tracking control is realized.

The invention discloses an irregular action recognition method, system and device for short-time sequence electroencephalogram and electromyography fusion, and the method comprises the steps: collecting electroencephalogram and electromyography signals from motor imagery to execution in an incomplete asynchronous manner through a wireless electroencephalogram signal collection device and a distributed wireless surface electromyography signal collection device; processing noise in the electroencephalogram signals and the surface electromyogram signals; in combination with electroencephalogram signals in a motor imagery period, extracting electroencephalogram signal space domain features in a motor execution period, extracting time domain features of surface electromyogram signals, and in combination with a mixed fusion mode of feature layer fusion and decision-making layer fusion, fusing short-time sequence electroencephalogram and electromyogram signals in the motor execution period; according to the method, the motion imagination period recognition result and the motion execution period recognition result are subjected to confidence analysis, irregular actions of the user are quickly and accurately recognized and serve as control instructions to be input into the exoskeleton robot, the exoskeleton robot is controlled to assist the user in completing the target actions, and the initiative of the user is effectively improved.

The invention discloses an exoskeleton decoupling control method based on a fixed time expansion state observer, and the method comprises the following steps: 1), building a dynamic model of a multi-input-multi-output exoskeleton robot, and extracting an unknown dynamic coupling part; 2) designing a fixed time extended state observer to estimate uncertain dynamic coupling and unknown external disturbance in the system, and performing compensation by adopting a feedforward mode; and 3) in combination with the observer, designing a fixed-time nonsingular terminal sliding mode controller to quickly track the expected trajectory of the exoskeleton so as to realize decoupling control of the exoskeleton system. According to the invention, for a multi-input multi-output exoskeleton robot system with model uncertainty, a fixed-time convergence decoupling control method is designed in combination with an extended state observer and a nonsingular terminal sliding mode controller; the robustness and the stability of the system are improved, and meanwhile, the tracking error of the system can be controlled at a low level within fixed time.

The invention discloses a high-compatibility self-calibration lower limb exoskeleton rehabilitation robot which comprises a first lower limb rehabilitation device, a second lower limb rehabilitation device and a hip joint adjusting device. The hip joint exoskeleton assembly comprises three active driving units, and the active driving units with the rotating surfaces corresponding to the horizontal plane are aligned with the rotating center of the hip joint through the remote motion center mechanism with the virtual axis. According to the high-compatibility self-calibration lower limb exoskeleton rehabilitation robot, rotation of a horizontal plane, a coronal plane and a sagittal plane can be completed at the position corresponding to the hip joint of a user, three degrees of freedom of forward flexion / backward extension, adduction / abduction and inward rotation / outward rotation of the hip joint of the human body are achieved respectively, and the exoskeleton robot is aligned to the rotation center of the hip joint of the human body; therefore, the rehabilitation effect of the high-compatibility self-calibration lower limb exoskeleton rehabilitation robot can be improved.

A kind of walking power-assisted exoskeleton robot, comprising: waist support structure module, waist rod module, joint drive module and binding system.Waist rod module includes waist rod module assembly, and waist rod module assembly is provided with two groups and is symmetrically arranged relative to waist support structure module.The design scheme proposed in the embodiment of the application can not only simplify the structure of the whole machine by reducing assembly process, but also reduce the complexity of the structure and improve the stability of the machine.By applying symmetric telescopic adjustment module, the machine can provide better wearing adaptability and comfort for people of different body shapes.Compared with the existing light walking exoskeleton robot, the present application has the advantages of wearing comfort and convenience, easy operation, high whole machine protection ability and strong power-assisted ability.In addition, the present application has small size and light weight, so it has strong market competitiveness in the same type of exoskeleton robot products.

The invention discloses a pathology adaptive prediction control method for a lower limb rehabilitation exoskeleton robot, and belongs to the technical field of rehabilitation robot control. The method comprises the steps that the joint angle, the angular speed and the man-machine interaction torque of the lower limb rehabilitation exoskeleton robot are collected in real time, and a kinetic equation of the lower limb rehabilitation exoskeleton robot is constructed; on the basis of the kinetic equation of the lower limb rehabilitation exoskeleton robot, parameter vectors representing the current pathological features of the patient are estimated online, so that a prediction model in the lower limb rehabilitation exoskeleton robot is updated, and the optimal auxiliary torque is solved. The intelligent brain of the exoskeleton robot can be deeply fused with the real-time state of the body of the patient, and rehabilitation training which is really personalized and self-adaptive and can effectively promote neural function recovery is provided for the patient.