355 results about "Exoskeleton robot" patented technology

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 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 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 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 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 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 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.

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.

The invention discloses a lower limb exoskeleton robot layered cooperative walking aid control method. The method comprises the steps of data acquisition and preprocessing; human-machine coupling dynamics modeling is carried out, and required moments tau < hip >, tau < knee > and tau < ankle > of the hip joint, the knee joint and the ankle joint are obtained; low-layer joint control, including hip joint moment, knee / ankle virtual impedance moment and friction compensation calculation; middle-layer multi-axis cooperation and gait phase judgment; high-rise energy is optimally distributed, assistance is distributed to three joints in a phase and a safety boundary, meanwhile, the requirements for labor saving and stability are considered, a minimization problem is written in a unified mode, J serves as the total amount, and torque of the hip joint, torque of the knee joint and torque of the ankle joint are solved in a combined mode; according to the step-by-step self-adaption of myoelectricity driving, in order to cope with human body fatigue changes, step-by-step updating is carried out on the virtual stiffness within the movement distance. The method has the advantages of improving man-machine cooperation level, man-machine adaptability and walking aid efficiency.

The invention belongs to the technical field of robots, and discloses a self-adaptive multi-scene exoskeleton robot control method and system, and the method comprises the steps: carrying out the denoising of collected multi-modal sensor data through employing a scene feature entropy-based Kalman filtering algorithm, carrying out the fusion of the denoised multi-modal sensor data, and carrying out the fusion of the denoised multi-modal sensor data, extracting spatial features and time sequence features in combination with a hybrid deep learning model to identify a current scene type; and according to a current scene type identification result, constructing an emotional state and motion intention mapping model, generating a corresponding power control instruction, and synchronously outputting pulse parameters of active stimulation muscles and passive stimulation muscles based on the power control instruction so as to perform man-machine cooperative control under multiple scenes. The method can adapt to different types of exoskeleton robots, and a more intelligent and accurate solution is provided for multiple application scenes.

The invention discloses a compact mining whole-body power-assisted exoskeleton robot with an anti-explosion shell and a posture monitoring function, and relates to the technical field of operation robots, the compact mining whole-body power-assisted exoskeleton robot comprises a trunk wearing assembly, two driving upper limb assemblies, two driving lower limb assemblies, a flexible connecting frame and a posture detection assembly; the two upper limb driving assemblies are connected with the trunk wearing assembly, and shoulder supporting structures are installed at the positions, close to the trunk wearing assembly, of the two upper limb driving assemblies. The flexible connecting frame can be bent and deformed; the two driving lower limb assemblies are connected with the trunk wearing assembly through a flexible connecting frame; the upper limb driving assembly and the lower limb driving assembly can be quickly connected and matched; the driving upper limb assembly can also be quickly connected and matched with an external execution tool; the posture detection assembly is used for detecting motion postures of the upper limb driving assembly and the lower limb driving assembly. According to the exoskeleton robot, the stooping operation comfort can be improved, various kinds of work assistance can be achieved, and the adaptability is better.

The invention discloses a hip joint power-assisted exoskeleton robot for fire rescue, and relates to the technical field of power-assisted exoskeleton robots. Comprising a back frame, an upper body binding module, power-electric control integrated modules and leg modules, the upper body binding module comprises a waist binding belt and straps, the power-electric control integrated modules are arranged on the two sides of the waist binding belt, each power-electric control integrated module comprises a shell, a mounting plate, a battery pack, a motor and a control assembly, and the shell is divided into an inner shell and an outer shell; the inner shell and the outer shell can be matched to form a containing cavity, the installation plate is installed in the inner shell, the battery pack, the control assembly and the motor are sequentially and fixedly installed on the installation plate from top to bottom, when the inner shell is covered with the outer shell, the installation plate, the battery pack, the motor and the control assembly are all located in the containing cavity, and the leg module comprises a leg rod assembly and a leg bandage. The protection capability of the robot can be effectively improved, and meanwhile, the back frame better fitting the back curve of the human body is adopted, so that the robot is more comfortable to wear.

The invention discloses a multi-protocol fusion exoskeleton robot component collaborative test system and method. The test system comprises a joint motor twin trawling test module, a force sensor test module, an attitude sensor test module and a control module. The control module is designed based on a ZYNQ chip, and the PS part is used for operating a general control upper computer and outputting control signals to the force sensor test module and the attitude sensor test module; the PL part comprises a signal output unit, a signal acquisition unit and a signal storage unit, and is used for outputting a motor control signal and acquiring a sensor signal of the test module in real time; and after a control signal and a sensor signal are stored, the acquired signals are transmitted to the PS part for processing through an AXI bus. According to the invention, three components of the exoskeleton robot can be controlled and driven in a centralized manner, the problem that precise working condition simulation cannot be carried out on the joint motor in the prior art is solved, and reliable support is provided for performance evaluation and calibration under complex working conditions.

The invention relates to an exoskeleton robot control method and device and an exoskeleton robot. The method comprises the following steps: determining an activity parameter according to a signal collected by a sensor; identifying a plurality of motion intentions according to the activity parameters, judging motion identifiers under the motion intentions, and determining a plurality of motion identifiers; performing state transition according to the plurality of action identifiers by utilizing a finite-state machine to obtain a target sub-state; a plurality of motion modes are preset in the finite-state machine, and each motion mode corresponds to at least one sub-state; and controlling the power unit according to the control instruction corresponding to the target sub-state. According to the method, the motion intention recognition and the finite-state machine are combined to complete the operation state transition, so that the misjudgment rate of state recognition can be reduced, and the robustness of the finite-state machine is improved; the multiple preset motion modes of the finite-state machine are divided into the sub-states, the situation of wrong assistance can be eliminated to a certain degree, misoperation of the exoskeleton robot is avoided, and the assistance control accuracy is improved.

The invention discloses an exoskeleton robot abnormal working condition automatic response and risk avoiding decision-making method, and relates to the technical field of exoskeleton, and the method comprises the steps: deploying a plurality of types of sensors to synchronously collect environment data, human physiological data and exoskeleton operation data, and carrying out the preprocessing to remove interference; through fusion of abnormal harmonic detection and a finite-state machine, dangerous working conditions such as wind speed abrupt change are identified, and grades are judged; monitoring eye movement and heart rate variability data in real time, quantifying cognitive load and grading; a decision rule base is constructed, and joint locking and other adaptive risk avoiding decisions are generated in combination with working condition types, grades and cognitive loads; the controller drives the execution mechanism to execute a decision, continuously monitors data at a frequency, dynamically adjusts the decision or recovers a normal mode. According to the method, dangerous working conditions are accurately identified through multi-source data fusion, adaptive risk avoiding measures are automatically triggered, a power assisting strategy is dynamically adjusted in combination with cognitive loads, and operation flexibility and physical output are balanced.

The invention discloses an exoskeleton robot control parameter determination method and system, and particularly relates to the technical field of robot control, and the method comprises the steps: synchronously collecting rigid joint torque, flexible cable tension and inertia signals, generating a dual-channel response spectrum through Fourier transform, and obtaining a frequency ratio vector; a kinetic energy coupling index and a viscosity lag index are calculated, and a bandwidth difference index is generated through the cooperation phase model in combination with the inertia-damping signature vector; a rigid gain matrix and a flexible gain matrix are obtained in a self-adaptive mode under the energy consumption constraint through sparse Bayesian optimization; constructing a weighting function according to the motion prediction phase and the energy consumption constraint, and synthesizing the two gain matrixes into a composite control instruction stream; and expected and actually measured moment residual errors are monitored, a gain increment is generated according to gradient perception when the threshold is exceeded, and a coupling feature extraction unit is written back, so that millisecond-level gain correction is realized, and the problem that coupling oscillation is caused by unified gain mismatch due to the fact that energy mutual feedback and phase delay cannot be accurately quantified in the prior art is solved.

The invention, which relates to the technical field of medical training, discloses a multi-sensory feedback fusion stroke rehabilitation exoskeleton robot training system comprising an exoskeleton robot main body, a multi-sensory feedback module, and a motion state monitoring module. By integrating a multi-sensory feedback module, a visual, auditory, tactile and motion state monitoring module and a central control module, multi-dimensional sensory stimulation and real-time data acquisition are realized when a patient is trained, and the central control module adapts parameters in the module according to the real-time motion precision of the patient and personalization. The strength and the type of multi-sensory feedback are dynamically adjusted, meanwhile, motion parameters of an exoskeleton robot body are controlled, a dynamic adjustment mechanism ensures pertinence and effectiveness of training, forward feedback is provided to strengthen a correct motion mode when the motion precision of a patient is high, and correction prompt is provided and assistance or resistance of the robot is adjusted when the motion deviation is large. Therefore, nerve function remodeling and motor ability recovery are effectively promoted.

The invention provides a cross-individual human body behavior recognition method based on self-training and active inquiry, and the method comprises the steps: obtaining a source domain data set with a label and a target domain data set without a label; processing acceleration data in the source domain data set with the label and the target domain data set without the label; dividing the target domain data into a label-free training set and a test set in proportion; establishing a feature extractor based on a dual-channel convolutional network, and extracting features of time and space dimensions from the input two-dimensional acceleration data at the same time; a cross-individual adaptation algorithm based on a confidence threshold value, sparse query and label propagation is adopted, a model is trained on a non-label training set on a target domain, and the adaptation problem caused by cross-individual data distribution difference is relieved. According to the method, the behavior recognition accuracy of the model in a cross-individual scene can be improved, the development cost and the user burden are reduced, and a more intelligent and adaptive solution is provided for application of wearable equipment such as exoskeleton robots.

The invention provides a hip joint exoskeleton robot multi-scene adaptive power-assisted control method, and the method comprises the steps: achieving the motion scene recognition and motion phase estimation through a deep learning perception model; motion scenes such as standing, walking on the flat ground, going upstairs, going downstairs and squatting up are recognized according to the angles and angular speeds of inertial sensors of the left leg and the right leg of a wearer, and continuous action phases are estimated, and a force trajectory planner plans corresponding force trajectories for different action states according to sensing results. A sine moment trajectory planning strategy of a self-adaptive gait cycle is adopted for providing assistance for a walking state in the scenes of walking on the flat ground, going upstairs and going downstairs, an impedance moment planning strategy is adopted for providing assistance for a rising state in the squatting scene, and direct force control or impedance force control over an exoskeleton joint motor can be achieved through a motor force position switching controller. The method is suitable for various task scenes of the hip joint exoskeleton in the carrying process, walking power-assisted control and squatting and rising impedance power-assisted control of the self-adaptive gait cycle are achieved, and the self-adaptive and compliant effects are achieved.

The invention discloses an exoskeleton robot rehabilitation parameter dynamic evaluation method and system based on gait quantitative analysis, and the method achieves the precise matching of a closest reference standard for a patient through an intelligent reference matching algorithm based on the dynamic reference matching technology of height and weight, and achieves the personalized adaptation of an evaluation reference. Misjudgment caused by an evaluation mode only depending on doctor experience is avoided, rehabilitation training parameters better fit actual physiological features of a patient, meanwhile, through a multi-dimensional gait scoring model, angle deviation analysis and bilateral coordination evaluation are fused, left and right joint activities are paired and defined, and a bilateral limb activity difference value is calculated, so that the rehabilitation training accuracy is improved. The asymmetric dyskinesia of a hemiplegic patient is accurately identified, a quantitative basis is provided for making a rehabilitation scheme, accurate judgment is performed according to own data characteristics of different users, and a rehabilitation treatment scheme is customized for the users by using quantitative data reference, so that the consistency and accuracy of the rehabilitation effect are ensured.

A mobility assistance exoskeleton robot, comprising: a waist support structure module (1), a waist rod module (2), a joint drive module (3), a leg rod module (4), and a binding system (5). The waist rod module (2) comprises waist rod module assemblies; two waist rod module assemblies are provided, said two assemblies being symmetrically arranged relative to the waist support structure module (1).

The utility model relates to the technical field of exoskeleton robots, in particular to a binding mechanism of a walking aid robot, which comprises a shoulder and back binding belt, a waist binding belt and a leg binding belt, the shoulder and back binding belt is provided with a plurality of shoulder and back connecting ends used for being connected with a main body frame; connecting belts are arranged on the shoulder and back connecting ends in a one-to-one correspondence mode and detachably connected with the shoulder and back connecting ends, buckles are arranged on the connecting belts, and buckling bases used for being matched with the buckles are arranged on the main body frame; and the waist bridle and the leg bridles are detachably connected to the main body frame. The fixing structure has the effect of solving the problem that the fixed connection between the bridle and the main body frame or the joint part is inconvenient to disassemble and replace.

The application relates to the technical field of medical rehabilitation robots, in particular to a safe upper limb rehabilitation exoskeleton robot, which comprises a fixing frame and a mechanical arm arranged on the fixing frame and used for assisting the movement of a patient's arm, the mechanical arm comprises, in sequence, a shoulder joint vertical folding and unfolding module, a shoulder joint horizontal folding and unfolding module, a shoulder joint flexion and extension module, an elbow joint flexion and extension module, a wrist joint flexion and extension module and a lower arm rotating module, the mechanical arm can be suitable for a left arm or a right arm by switching left and right states. Each module of the mechanical arm is provided with a mechanical limiting structure used for limiting the activity range of the corresponding module, and the mechanical limiting structure can switch left and right states, and is used for synchronously switching when the mechanical arm switches left and right states to ensure the safety of the patient. The spring positioning pin is used for adjustably limiting, the application can realize the left and right exchange function of the mechanical arm, ensure the mechanical safety limiting of the limit angle of the two arms and improve the safety of the equipment.

The invention discloses a lower limb multi-joint torque intelligent prediction method and related equipment, and relates to the technical field of joint torque prediction.The method comprises the steps that original surface electromyography sEMG, kinematics data and ground reaction data of a subject under different lower limb movements are collected; kinematics data and ground reaction force data are used as input, and multi-degree-of-freedom angle data and moment data of three joints of the lower limb are obtained through inverse kinematics and inverse dynamics calculation; constructing a lower limb multi-joint torque prediction model through a deep learning technology, and performing model training by using the training set; and inputting the test sample set into the trained lower limb multi-joint torque prediction model to obtain a joint torque prediction result of the subject. According to the method, the problems of low joint torque prediction precision, high calculation cost and the like in the prior art are solved, the method has the advantages of being easy to implement, high in generalization, capable of processing large-scale data and the like, and prediction results are expected to be applied to three-dimensional compliance and modular control of the exoskeleton robot.

The invention discloses a low-power-consumption and high-precision exoskeleton body area network energy consumption management method. The method comprises the following steps: (1) constructing a three-layer heterogeneous communication architecture and initializing the three-layer heterogeneous communication architecture; (2) starting the exoskeleton robot; (3) acquiring data in real time by a sensor; (3) the control center analyzes the collected data and cooperates with the human body to complete the action, and meanwhile, the regional aggregation nodes calibrate the QoS level of the collected data; (4) the controller predicts future human motion intensity, regulates and controls a data acquisition mode, carries out dynamic QoS grading LSTM model parameter adjustment, and carries out cross-modal data alignment; (5) the QoS strategy executor calculates a comprehensive QoS label; and (6) the sensor adjusts a data transmission strategy of the sensor based on the comprehensive QoS label. According to the method, a strategy for collecting and transmitting the sensor data is adjusted based on the integrated QoS grade and the motion state, automatic network energy consumption management is carried out, and concurrent access of the sensor data is ensured while low calculation energy consumption is ensured.

The invention discloses a robot motion mode generation method and system, and the method comprises the steps: constructing an optimized adaptive CPG model, and carrying out the learning of gait signal data, and obtaining the frequency, amplitude and phase parameters; frequency, amplitude and phase parameters are input into a master-slave oscillator, and gait signals with single-joint and multi-joint coordination are generated; constructing a transition gait generation framework, and adjusting the transition weight of the gait signal through a variational controller to obtain a transition gait signal; and outputting the transition gait signal to a joint driver of the lower limb exoskeleton robot, and controlling the exoskeleton robot to execute corresponding gait actions to realize rehabilitation training of the patient. According to the invention, the dependence on the data quality and sampling rate of the sensor can be reduced, and the control precision of the joint driver is realized. The robot motion mode generation method and system can be widely applied to the technical field of robot control.

The invention relates to the technical field of medical rehabilitation instruments, and discloses an exoskeleton robot for fracture auxiliary rehabilitation. The robot comprises a shear deformation sensing assembly, a shape memory alloy locking assembly and a joint driving module. The shear deformation sensing assembly comprises a fixed shell, a flexible liner arranged on the inner side of the shell and a sensing beam connected between the shell and the liner. The shape memory alloy locking assembly comprises a locking pin arranged in the connecting rod structure, a shape memory alloy driving piece in linkage with the locking pin and a locking part located on the connecting rod structure and corresponding to the locking pin in position. And the strain gauge is electrically connected with the shape memory alloy driving piece. Transverse micro-slippage representing shearing risks is directly sensed through a mechanical structure, rigid mechanical locking is triggered in a linkage mode, and therefore rapid and reliable physical protection is provided for a fracture part, and secondary injury in rehabilitation training is effectively prevented.

The present invention relates to a lower limb auxiliary assembly of a wearable robot, and a wearable robot equipped with same. In an exoskeleton robot having a driver mounted on a knee joint, a lower limb auxiliary assembly is provided with a coil spring-equipped damping unit below the knee joint to absorb impact transmitted via same and provide flexibility, and to provide freedoms of movement similar to those of the knee joints of the body of the wearer, thereby minimizing pain, discomfort, or feelings of strangeness in the wearer due to the exoskeleton robot.