1499 results about "Walking gait" patented technology

This invention relates to a gait emotion recognition method, system, storage medium, and computer device based on spatiotemporal graph convolution. The method includes the following steps: S1, data augmentation by reversing the temporal direction of gait; S2, obtaining deep emotion features and prior emotion features respectively through a spatiotemporal graph convolutional network and prior feature statistical methods; S3, performing nonlinear mapping on the prior emotion features using a feature mapping layer; S4, inputting the fused features of the deep emotion features and prior emotion features into an emotion classifier to obtain the emotion category. The feature mapping layer of this invention achieves more effective feature fusion by performing nonlinear mapping on prior features; it also introduces causal temporal convolution to replace general temporal convolution, effectively extracting fine-grained temporal features by enhancing temporal correlation and cross-period feature fusion. Furthermore, a walking direction recognition auxiliary task is designed to accelerate the training and convergence speed of the model, enhancing the ability to extract temporal-dependent features and the performance of emotion recognition.

The invention belongs to the technical field of robot control, and discloses a high-gear-ratio humanoid robot robust walking control method and system based on potential dynamics self-adaption, and the method comprises the steps: designing a control strategy training frame based on deep reinforcement learning, and carrying out the optimization through an Actor-Critic structure and a PPO algorithm; constructing a potential dynamic adaptive network LDAN, and extracting environment and ontology dynamic parameters through a variational auto-encoder; designing a multi-dimensional reward function; constructing a periodic gait library by using von Mises distribution and motion capture data; gradually introducing terrain disturbance and dynamic change through curriculum type simulation training; the trained strategy and the LDAN module are deployed on the high-gear-ratio driven humanoid robot, the problems that the high-gear-ratio humanoid robot is poor in motion stability, poor in adaptability and insufficient in action expression in a variable environment are solved, and the high-gear-ratio driven humanoid robot has the advantages of being high in robustness, high in natural expressivity and high in migration ability.

The invention relates to the technical field of medical rehabilitation, and discloses a lower limb weight-bearing gait rehabilitation training system which comprises a data acquisition module, a data processing and analysis module, a patient individualized modeling module, an intelligent decision and control module, a rehabilitation execution module and a man-machine interaction and medical information interface module which are in communication connection through a network. The data acquisition module is used for acquiring multi-modal data of a patient in real time, and the multi-modal data comprises static sign data, dynamic physiological parameters, kinematics and dynamics parameters and non-motion physiological and psychological state data; and the data processing and analysis module is used for carrying out preprocessing, feature extraction and deep analysis on the original data, and outputting a structured patient individualized feature vector and an evaluation result. According to the invention, a patient three-dimensional skeletal muscle digital twinborn model is constructed through the patient individualized modeling module, and in combination with a continuous learning intelligent model library, body sign differences of different patients can be accurately adapted.

The invention provides a multi-path cooperation method, device and equipment for a cluster quadruped inspection robot, and the method comprises the steps: obtaining gait dynamics data and topographic data, and recognizing a topographic adaptive rhythm based on the spectrum decomposition of a foot end contact force sequence; converting the topographic data into a potential energy gradient field to construct a dynamic potential energy diagram, and generating a natural path network; a cooperative trigger point is set based on the stable support moment, a path ripple field is formed, and a multi-machine cooperative potential well is constructed; decomposing the path network into a dominant task chain and an auxiliary task chain, and weaving to form an initial collaborative path; rigid nodes and flexible sections are identified through topological transformation, and random disturbance is injected to reconstruct a self-adaptive collaborative path; gait phase information is extracted to construct a synchronous network, a potential energy flow vector is used for phase alignment to generate a group resonance frequency, and a resonance cooperation path is formed; and finally, an execution instruction is generated based on the resonance cooperation path and the terrain adaptability rhythm, and multi-path cooperation of the cluster quadruped inspection robot in the complex terrain environment is achieved.

The invention discloses an interaction test method for gait simulation of a humanoid robot, which is applied to a test platform. Comprising the following steps: controlling a current gait simulation model corresponding to a virtual robot to perform a gait simulation test in a virtual test environment in a current preset time period; the virtual test data is sent to the physical robot; controlling the physical robot to execute corresponding gait operation according to the virtual test data in the actual test environment, and sending the actual test data to the virtual robot; updating the current gait simulation model based on the virtual test data and the real test data; and continuing to perform the gait simulation test based on the updated gait simulation model, and ending the updating operation until the gait simulation test is ended, so as to generate a complete gait scheme. Therefore, by constructing a closed-loop interaction mechanism between the virtual robot and the physical robot, dynamic adjustment of the gait simulation model is achieved, the gait of the physical robot is restored, and the stability and precision of the gait of the physical robot are improved.

The invention discloses a biped robot reinforcement learning control method which comprises the following steps: after a model prediction controller receives a walking instruction, outputting expected angle data of each joint of a robot to a reinforcement learning neural network, and meanwhile, returning joint angle data of an actual strategy of the reinforcement learning neural network to the neural network by a sensing system at a bottom layer; a joint angle taking time as a sequence and planned by model prediction is compared with a joint angle actually generated by a neural network, and model prediction control is fused into a reinforcement learning training process by setting a reward function for punishment, so that the reinforcement learning training efficiency is improved, and a stable gait is more quickly achieved. Excellent gaits planned by the MPC are transplanted into reinforcement learning control, and control robustness can be improved under the condition that the excellent gaits of the MPC are reserved; and meanwhile, the joint angle data which is obtained by taking time as a sequence and is obtained by taking MPC as a planner can accelerate the reinforcement learning training process, so that the training speed and the control effect of reinforcement learning are greatly improved.

The invention discloses a robot gait self-adaptive adjustment method and system combined with topographic feature recognition, and relates to the technical field of robot gaits, and the method comprises the steps: collecting a topographic image in front of a robot advancing path, carrying out the deblurring processing, and extracting ground feature data through a lightweight neural network; matching the ground characteristic data with a preset gait control parameter database to obtain a gait control parameter set, and generating a control signal through a proportional-differential control method according to the gait control parameter set; correcting the control signal based on a reinforcement learning algorithm, and generating a target control instruction for driving a joint actuator; in the process of executing the target control instruction, collecting state feedback data in real time, and comparing and optimizing the feedback data with the currently executed target control instruction to realize feedback adjustment; the problems of insufficient terrain recognition precision caused by image blurring, response lag caused by sensing and control decoupling and lack of adaptive ability are effectively solved.

The invention discloses a poultry behavior abnormity real-time monitoring system based on multi-modal image fusion, particularly relates to the technical field of intelligent breeding behavior recognition, and is used for solving the problem of poor behavior monitoring accuracy under feather shielding. The method comprises the following steps: firstly, through combined perception of a visible light image and an infrared image, extracting a claw track interruption point and an anus temperature gradient direction, and realizing analysis of a motion state of a sheltered area; then, in combination with the heat conduction delay characteristic and the group movement direction, the flexion and extension angle of the covered leg joint is inverted, and a complete gait sequence is generated; thirdly, multi-source features such as gaits, temperature differences and body postures are fused, and a dynamic deviation model of the individuals relative to the mass center of the group is constructed; and finally, generating a stress behavior threshold curve according to the ground temperature and the ammonia gas concentration, outputting an abnormal behavior type and confidence, and realizing intelligent distinguishing of mechanical obstacles and adaptive behaviors.

The invention discloses an old people falling risk assessment and dynamic protection intervention system and method, and relates to the technical field of computer vision, and the system comprises a hardware composition module, a software and algorithm module, an intervention mechanism module, a system integration and optimization module, and an application scene module. The hardware composition module is used for collecting motion postures, gaits and environment data of old people in real time through a multi-mode sensor, hardware support is provided for risk assessment and protection, deep fusion of vision, inertia and environment data is achieved through an attention mechanism, spatial-temporal features are rapidly extracted in combination with a lightweight CNN-LSTM network, and the accuracy of risk assessment and protection is improved. And then the dynamic relationship between the mass center and the supporting surface is accurately calculated based on a biomechanical model, so that the real-time performance of attitude analysis and the accuracy of fall risk assessment are effectively improved, the prediction accuracy of the system on the fall risk of the old people is greatly improved and the false alarm rate is reduced while the consumption of calculation resources is reduced, and a reliable basis is provided for a hierarchical protection strategy.

The invention discloses a humanoid robot motion control method based on gait planning and reinforcement learning, and belongs to the technical field of humanoid robot motion control. According to the method, the problems of low convergence speed in a training process and poor stability and reliability of a trained model of an existing reinforcement learning method are solved. According to the method, a reinforcement learning model runs in parallel under multiple environment instances, in the running process, a target foothold is generated in real time based on feedback state information, the generated reference foothold and a mass center track serve as an action reference or reward target of a controller, and a continuous and dynamically-adjusted foot end track is constructed in combination with gait phase information. And after the reinforcement learning model is trained by utilizing all the collected trajectory data, motion control can be performed on the humanoid robot through the trained reinforcement learning model, so that the robot realizes stable walking similar to human beings under the condition of not depending on a predefined template. The method can be applied to motion control of the humanoid robot.

The invention discloses a pneumatic-biological signal feedback cooperative control method based on an anti-gravity rehabilitation treadmill, and the method comprises the steps: synchronously collecting sEMG, IMU and plantar pressure signals, and completing the time-space registration through clock synchronization, time warping and extended Kalman filtering; muscle activation degree, pressure center offset and joint angular velocity are extracted to construct multidimensional features, and gait phase switching is predicted through LSTM-CNN; the TS fuzzy system generates a preliminary control instruction according to expert rules, and Q-learning takes pressure balance and motion smoothing as reward functions to optimize the weight reduction proportion and the treadmill speed online; a waist annular air cavity is driven to be inflated to form a pressure supporting field so as to lift a pelvis and guide a lower limb gait mechanical mode, so that the muscle activation degree, the joint angular motion trail and the pressure center migration characteristic are dynamically influenced, a servo motor tracks phase nodes in a feedforward-feedback mode, and risk early warning and training parameter self-adaptive closed-loop rehabilitation are achieved.

The invention discloses a self-adaptive gait control method, system and device for a quadruped robot in a limited space and a medium, and belongs to the technical field of gait control. Identifying terrain categories through a classifier and acquiring initial gait parameters from a gait library; adjusting the gait parameters according to the terrain gradient and the roughness to obtain optimized gait parameters; calculating stability indexes and fusing scores; when the score is lower than a threshold value, an adjustment strategy is determined according to an instability mode to correct gait parameters; a foot end track is generated and control is executed; and the executed information is fed back to terrain perception to form a closed loop. According to the method, the terrain recognition accuracy is improved through multi-modal information fusion, the adaptive mapping from the terrain to the gait is established to achieve automatic gait switching and parameter optimization, the stability is evaluated through multi-index fusion, a targeted adjustment strategy is selected according to an instability mode, the adaptive gait control problem under the limited space and the complex terrain is solved, and the adaptive gait recognition accuracy is improved. And the movement efficiency and the stability are improved.

The invention discloses a quadruped robot inspection method based on multi-modal sensing fusion. The method comprises the following steps of: 1, initializing a global task, loading a basic navigation map, and generating the basic navigation map comprising topographic features, forbidden areas and parking positions; 2, autonomous navigation and dynamic correction of positioning deviation are realized according to laser radar SLAM data, and the positioning deviation is dynamically corrected according to real-time observation data; 3, dynamically switching or adjusting the gait strategy according to the terrain category, training the gait strategy of the quadruped robot to be matched with the terrain category, and generating a self-adaptive motion control instruction to adapt to the terrain in real time; and step 4, synchronously realizing parking area structured data acquisition and dynamic abnormal information perception through multi-sensor fusion, and realizing target state monitoring and abnormal event response in the inspection task. According to the invention, through collaborative innovation of the bionic motion platform and multi-mode intelligent detection, all-terrain coverage, total-factor perception and full-process autonomous intelligent inspection in a complex parking lot environment is realized.

The invention relates to the technical field of quadruped robot control, and discloses a quadruped robot anti-disturbance motion control method based on a cost weight adaptive mechanism, which comprises the following steps: constructing a quadruped robot state equation based on a single rigid body dynamic model, a model prediction control problem with cost weight, friction cone constraint and gait constraint is formed through discretization; a cost weight self-adaptive mechanism based on disturbance observation is introduced on the basis of a model prediction control framework, the disturbance intensity is estimated by taking the attitude error of the fuselage and the corresponding change rate as disturbance observation values, and the cost weight corresponding to the attitude in the model prediction control problem is dynamically adjusted; by solving a model prediction control problem, an expected ground reaction force and a target state are calculated in real time, and motion control of the quadruped robot is realized in combination with a gait planner and a joint controller. According to the invention, the motion stability and anti-disturbance performance of the quadruped robot under external disturbance are improved based on a cost weight adaptive mechanism of disturbance observation.

Embodiments can relate to a prosthetic foot system. The system can include an ankle joint housing co-locating a rotation sub-assembly, a torsional shock absorbing sub-assembly, and a vertical shock absorbing sub-assembly. The system can include a foot component attached to the ankle joint housing. The system can be configured as a co-designed architecture to functionally integrate at least two functions of: (i) torsional shock absorption, (ii) multi-axial motion with stiffness modulation in single gait cycle, (iii) active dorsiflexion, and (iv) vertical shock absorption by causing the at least two functions to operate in concert.

The invention provides a shoe sole wear resistance detection device, and belongs to the technical field of wear resistance detection, the shoe sole wear resistance detection device comprises an instrument body and a shoe sole sample formed by punching a shoe sole, and further comprises a simulation detection assembly and a dust removal cleaning assembly; under the guidance of the guide ring, the first support arm can periodically move up and down, the process simulates the dynamic change of the pressure borne by the sole in different gait stages when a person walks, and in combination with the rotation of the sole sample, not only is the vertical pressure change simulated, but also the friction force in different directions can be simulated through relative sliding. The stress state of the shoe sole in the walking process is restored, so that the detection is closer to the actual use scene; the dust removal system is driven by the power of the detection device to operate, so that all-directional coverage of the whole abrasion area is realized, the retention time of dust in the detection area is reduced, dust interference can be avoided, the state of a sole sample before and after detection is only influenced by abrasion of a grinding wheel, and the detection result more truly reflects the abrasion resistance of the sole.

The invention discloses a humanoid robot reinforcement learning gait control method and system, and belongs to the technical field of robot control. Comprising the following steps: constructing a model prediction control optimization problem based on a linear inverted pendulum model, and generating a robot mass center track and a foothold sequence; collecting state-action pair data of model prediction control, and training a neural network model by adopting supervised learning; designing a reinforcement learning strategy network, taking the output of the neural network model as a physical guidance reference, calculating a training reward based on the constructed composite reward function, training the strategy network in a high-concurrency simulation environment by adopting a near-end strategy optimization algorithm, and outputting a joint action instruction; and verifying the trained strategy and the joint action instruction output by the trained strategy in a plurality of simulation environments, and deploying the trained strategy and the joint action instruction to a real machine for dynamic gait control. According to the method, the stability and adaptability of gait control of the humanoid robot are improved through a method of combining model predictive control and reinforcement learning.

The invention discloses a bionic walking feature fused quadruped robot gait reinforcement learning training method, which comprises the steps of S1, bionic gait feature modeling for extracting key features from a motion mode of a natural quadruped animal and constructing a bionic template capable of directly guiding robot gait control; and S2, constructing a reinforcement learning training environment, and simulating diversified actual scenes by constructing a high-fidelity simulation platform. According to the method, key features are extracted from natural four-footed animal gaits, a bionic template library is constructed, and natural features such as nonlinear rhythm and dynamic symmetry of animal movement are fused into robot gait control, so that the problem of action mechanical stiffness caused by dependence on manual design of a track in a traditional method is effectively solved; movement of the robot is closer to natural biological gaits, impact generated when the robot interacts with the environment is reduced while movement energy consumption is reduced, and movement smoothness is improved.

The invention discloses a multi-scene gait monitoring and motion function assessment method and system, a multi-camera cooperation system is deployed in a life scene to accurately identify an identity and extract multi-scene motion features, and an accurate old people gait function assessment scheme is provided through key motion hierarchical modeling, multi-scene data fusion and a risk suppression mechanism. Specifically, 2D human body key points are estimated based on an OpenPose algorithm, and a PoseLifter model is utilized to lift the 2D key points to 3D, so that a three-dimensional motion track of a target is estimated; dynamically adjusting the weights of the face features and the skeleton features during identity recognition according to ambient light; designing an independent long-short-term memory network for different actions to evaluate the motion function score and the fall risk rating of the actions; and designing a risk sensitivity mechanism and fusing multi-scene data to carry out comprehensive scoring. The gait function abnormity of the old people can be found in time, and a basis is provided for health management.

The invention discloses a biped robot gait generation method based on enhanced confrontation motion prior, and belongs to the technical field of robot control. Comprising the following steps: constructing an enhanced PPO-AMP network; establishing an action network model, an evaluation network model and an AMP module; establishing a biped robot motion model, and selecting a simulation experiment environment; performing reinforcement learning on the enhanced PPO-AMP network by using a reward function combining task rewards and style rewards and combining an improved PPO-Clip mechanism with KL divergence constraint on the basis of a simulation experiment environment to obtain optimized network parameters; the optimized enhanced PPO-AMP network is used for generating the biped robot gait meeting the actual walking task. According to the control method, the environment perception capability is optimized, the network structure is deepened, and the reward mechanism and the training strategy are improved, so that the biped robot has high robustness and high natural gaits on various complex terrains at the same time.

The invention discloses a complex terrain-oriented quadruped robot self-adaptive gait generation system and method. The system comprises a sensor system used for collecting terrain data and robot state information; the terrain sensing module identifies terrain types and terrain feature parameters according to sensor data, and quantifies terrain features into parameters; the multi-modal gait library is used for storing a plurality of basic gait modes and corresponding gait parameters; the parameterized gait generation module is used for selecting a basic gait from the gait library and optimizing and generating an optimal gait adaptive to the current environment according to terrain parameters and task parameters; the gait smooth transition algorithm synthesizes all intermediate postures in the time and space interpolation generation transition process to obtain a smooth transition trajectory; the terrain adaptability adjusting module is used for finely adjusting the gait parameters according to real-time feedback; and the execution control module is used for converting the generated gait parameters into joint control instructions for controlling the robot so as to execute corresponding actions. The quadruped robot can adapt to more complex environments.

The invention discloses a humanoid robot walking and running unified control method and system based on a biological rhythm, and belongs to the technical field of humanoid robots. Capturing human walking and running action data, and extracting rhythm features from frequency and phase time domains; constructing a rhythm generator based on the rhythm features extracted in the step 1; according to a strategy gradient method based on a constraint reinforcement learning algorithm, a walking and running unified control strategy is constructed, rhythm information generated based on a rhythm generator is adopted in the walking and running unified control strategy, then action-critic is used for training, a bionic motion control system Walk2Run under speed driving is jointly constructed, natural gait transition and frequency adjustment are achieved, and the walking and running unified control strategy is obtained. And meanwhile, the physical capacity limitation of the robot is met, and motion fluency and energy efficiency are improved. The problems that an existing humanoid robot does not achieve energy optimization of human walking during walking, and mobility of an existing data set has high requirements for the motion ability of the robot and is poor in generalization ability are solved.

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 discloses a robot gait training method and system based on reinforcement learning, and relates to the technical field of electric vehicle charging, and the method comprises the steps: carrying out the simulation learning initialization of a strategy based on a reference video; selecting a plurality of evaluation indexes to design a dynamic reward function so as to guide the initial strategy network to optimize the gait performance; through an environment difficulty scheduling mechanism, the training environment difficulty is automatically adjusted according to strategy performance; performing optimization training on the strategy network by using a PPO algorithm; and judging the optimized gait strategy by constructing a cognitive load index, and outputting a final gait strategy. According to the method, imitation learning, self-adaptive reward modeling, multi-stage scheduling, reinforcement learning optimization and cognitive feedback are organically fused, and a robot gait training framework with generalization ability, stability and social adaptability is constructed. According to the method, the naturalness, the stability and the man-machine friendliness of the gait of the robot can be remarkably improved under complex terrains and interaction scenes, and the method has wide application prospects and popularization value.

The invention discloses a pedestrian re-identification method and system based on cross-modal feature fusion and a medium, and the method comprises the steps: carrying out the human body contour detection of a to-be-detected video stream obtained in real time, and obtaining a first continuous frame set and a second continuous frame set; respectively inputting the first continuous frame set and the second continuous frame set into a preset target recognition model so as to extract target gait features from the first continuous frame set through a first extraction branch and extract appearance features and clothes features from the second continuous frame set through a second extraction branch, mapping the target gait feature to a semantic subspace to obtain a gait identity vector, and performing feature fusion on the gait identity vector and the appearance feature to obtain a target feature vector; and performing similarity matching on the target feature vector to determine a pedestrian re-identification result. According to the invention, the accuracy and robustness of pedestrian re-identification can be improved.

The invention belongs to the field of robot motion control, and particularly relates to a humanoid robot multi-terrain gait control method and system fused with visual perception. The method comprises the following steps: acquiring multi-modal sensing data of the humanoid robot, and preprocessing the multi-modal sensing data; the preprocessed multi-modal sensing data are input into a pre-trained world model, and the world model updates and outputs a potential state at the current moment based on a historical recursive state, a historical random posteriori state and an action sequence in a historical updating interval; inputting the potential state at the current moment into a pre-trained strategy network, and outputting an action at the current moment; and based on the action at the current moment, the driving torque of each joint is calculated through a PD controller, and the humanoid robot is driven to move. According to the method, a world model structure is introduced, so that the robot can realize more stable and more efficient gait control and terrain adaptation of the humanoid robot under the condition that the robot only depends on perception information which can be acquired by the robot.

The embodiment of the invention discloses a skeleton health dynamic tracking and intervention method and system based on movement and nutrition. The method comprises the following steps: acquiring gait information, bone metabolite information and bone state measurement parameters of a target user by adopting wearable equipment; processing the gait information, the bone metabolite information and the bone state measurement parameters based on an artificial intelligence (AI) model to obtain bone state prediction parameters; according to the bone state prediction parameter and the health data of the target user, performing bone health state evaluation of the target user to obtain a health state prediction parameter; the health state prediction parameters comprise a bone health state instant evaluation index and bone health dynamic trend data; generating a targeted exercise plan and a diet plan according to the health state prediction parameters; wherein the target motion planning is used for guiding the motion of the target user; the diet planning is used for guiding the diet of the target user.

The invention discloses a wearable plantar pressure and three-dimensional gait analysis system, and relates to the technical field of gait analysis, the system comprises a wearable in-shoe plantar pressure analysis subsystem and a three-dimensional gait analysis subsystem; the wearable in-shoe plantar pressure analysis subsystem comprises a pressure sensor module, a pressure integration module, an intelligent transmission module, a detection software module, a power supply module, an instantaneous pressure and balance detection module, a standing balance detection module, a balance and gait detection module and a cloud server; the three-dimensional gait analysis subsystem comprises a hardware unit and a software unit; according to the invention, a wearable plantar pressure and three-dimensional gait analysis collaborative system is constructed, bidirectional verification compensation is realized by means of data fusion of two subsystems, and traditional single-dimensional limitation is broken; pressure sensing and a double-view reconstruction technology are fused, data are dynamically captured, and bidirectional error correction is carried out; the limitation of traditional equipment is solved, the evaluation accuracy and robustness are improved, and a quantitative basis is provided for clinical diagnosis, rehabilitation and the like.

The invention discloses a quadruped vehicle sensing and decision-making system based on reinforcement learning. The quadruped vehicle sensing and decision-making system comprises a multi-mode sensing module, a dynamic fusion module, a reinforcement learning decision-making module and an execution module. The multi-modal sensing module is used for collecting multi-modal environment information including visual, tactile and inertial data; the dynamic fusion module carries out dynamic fusion on the multi-modal data by using a Transform-based feature extraction and time sequence alignment method to generate a unified environment representation; the reinforcement learning decision-making module is used for analyzing the multi-modal sensing result on the basis of a Proximal Policy Optimization (PPO) algorithm, and generating an optimized path planning and dynamic obstacle avoidance instruction; and the execution module generates a gait and motion control command according to the decision instruction to ensure the autonomous navigation and environment adaptability of the quadruped robot. According to the system, the sensing precision and the decision-making efficiency of the quadruped robot in a complex scene are remarkably improved, and the system has a wide application prospect.

The invention relates to the technical field of rehabilitation robot control, and particularly discloses a gait prediction compensation admittance control method and system for lower limb rehabilitation exoskeleton, and the method comprises the steps: inputting a multi-dimensional motion vector into a time sequence prediction model, and outputting a prediction result of a gait phase at a future moment; if the maximum probability output by the time sequence prediction model is lower than a preset threshold value, automatically switching to a pre-constructed rule criterion to carry out gait phase recognition; an admittance control parameter matched with the gait phase prediction result is called from an admittance parameter mapping table, and a main control torque is calculated based on the called admittance control parameter; calculating a compensation torque based on the deviation between the actual human-computer interaction force and the expected interaction force; and the main control torque and the compensation torque are superposed to serve as a final torque driving instruction of the exoskeleton motor. Real-time gait prediction, admittance parameter self-adaptive adjustment and interaction force error dynamic compensation can be fused, and high-flexibility and high-safety man-machine collaborative rehabilitation training is achieved.