703 results about "Quadrupedal robot" patented technology

The invention discloses a covariance self-adaptive control method for a quadruped robot for spine joint adjustment. The method comprises the following steps that 1, a spine quadruped robot kinematics model with spine joints, a robot trunk dynamics model with the spine joints and a robot leg multi-rigid-body dynamics model are established; 2, based on the energy stability margin theorem and the spine joint coupling effect, a posture control method of the spine joint quadruped robot is provided by combining a covariance self-adaptive evolutionary strategy, and the angle, rigidity and damping coefficient of spine joints are estimated and adjusted according to the terrain gradient; 3, extracting the spatial relationship between the trunk mass center and the foot ends of the robot as an expected reference, updating the expected mass center position in real time, and constructing a model prediction control method to control the posture of the spinal joint quadruped robot; 4, the position of the foot falling point of the swing leg is designed in advance in combination with the additional freedom degree provided by the spine joint and MPC prediction state information. The motion stability of the robot in the complex terrain is improved.

The invention discloses a quadruped robot obstacle avoidance control method and system based on path planning, and relates to the technical field of quadruped robot obstacle avoidance control, and the method comprises the steps: obtaining environment point cloud data, image data and quadruped robot motion speed data, and carrying out the terrain semantic segmentation and semantic pixel back projection of the image data, thereby obtaining a quadruped robot obstacle avoidance result; generating semantic point cloud data, combining the semantic point cloud data with the environment point cloud data subjected to motion compensation, and constructing a semantic annotation grid map; performing global path search on the semantic annotation grid map to generate a global smooth path; when the quadruped robot advances along the global smooth path, path nodes on the global smooth path are extracted at a fixed step pitch, the terrain category and the grid occupation state of each path node are judged according to the semantic annotation grid map, and a local reference path is generated; and executing a multi-stage obstacle avoidance strategy on the local reference path, and generating a foot end track sequence.

The invention discloses an intelligent voice recognition and natural language interaction method based on a quadruped robot, and the method comprises the following steps: S1, collecting a user voice instruction, generating a standardized voice text, and extracting a semantic keyword set; s2, collecting multi-source sensing data of the quadruped robot and generating a structured state data tensor; s3, constructing a multi-modal collaborative modeling mechanism, and generating a multi-modal joint semantic embedding vector; s4, constructing a semantic map based on semantic embedding and generating an action chain plan structure; s5, executing each sub-action in the action chain, and performing path analysis and execution monitoring; s6, storing the interaction task as a multi-modal semantic behavior memory unit; and S7, performing similarity retrieval based on current semantic input and historical memory to realize behavior migration and action chain multiplexing. The method has the advantages of accurate semantic understanding, intelligent interaction response, high behavior migration capability and the like.

The invention discloses a quadruped robot robust motion control method based on deep reinforcement learning, and belongs to the technical field of robot motion control, and the method comprises the steps: constructing a deep reinforcement learning model which comprises a state estimation network, a strategy network and a value network; interaction between the quadruped robot and the simulation environment is carried out, and standard observation information, historical observation information and privileged observation information of the quadruped robot at all moments are obtained; inputting the standard observation information, the historical observation information and the privilege observation information of the moment into a deep reinforcement learning model, and training based on a total loss function until convergence is carried out to obtain a trained deep reinforcement learning model; inputting standard observation information and historical observation information at corresponding moments in an actual scene into the trained deep reinforcement learning model to obtain output features of a corresponding strategy network; and the target position of each joint motor is calculated to complete the motion control of the quadruped robot. And efficient training and robust motion on various complex and unstructured terrains can be realized.

The invention discloses a quadruped robot motion control method based on adaptive deep reinforcement learning. The method comprises the following steps: S1, determining a network model, a composite reward function, a state space and a bionic action generation mechanism of a simulation training environment; the state space provides environment information input, the network model processes the input information and generates a decision, the bionic action mechanism executes a specific decision behavior, and the composite reward function evaluates a behavior effect and optimizes a decision direction; s2, constructing a simulation training environment of the quadruped robot, wherein the simulation environment comprises quadruped robot model information and simulation environment information; s3, training the network model by using a deep reinforcement learning algorithm based on robot model information and simulated environment information to obtain a trained motion control strategy; and S4, verifying the feasibility of utilizing the trained motion control strategy by controlling the motion of the quadruped robot in a real environment. According to the invention, the self-adaptive capability to a complex environment is obviously improved.

The invention relates to the field of mobile robot motion planning, and discloses a quadruped robot motion planning method and system based on height self-adaption. The method comprises the following steps: constructing 3D and 2D local obstacle maps according to coordinate transformation, and calculating a grid vertical distance through ray casting to generate a 2.5 D local obstacle map; determining an initial control sampling space by combining speed, acceleration and height constraints; discretizing a sampling space and simulation time, deducing a control value sequence and recursively calculating a local trajectory; then calculating a shaft alignment bounding box in a directed bounding box of the robot, screening grid units in the shaft alignment bounding box, comparing the height of a machine body with the allowable height of grids, and rejecting collision trajectories to obtain a feasible trajectory set; and finally selecting an optimal track by the evaluation function, extracting a control value and sending the control value to hardware for execution. According to the method, the 2.5 D local obstacle map containing height information is constructed, the four-dimensional control sampling space is designed, and the robot height self-adaptive adjustment is realized in combination with the collision detection and evaluation function.

The invention relates to a quadruped robot path planning method fusing visual semantic information and a laser radar. The method comprises the following steps: acquiring environment data, wherein the environment data comprises key ground semantic information, a depth image and original laser radar point cloud information; converting the key ground semantic information into laser radar key ground semantic point cloud information based on the depth image, and performing spatial fusion on the laser radar key ground semantic point cloud information and original laser radar point cloud information to obtain multi-source point cloud fusion information under a unified reference system; the multi-source point cloud fusion information is preprocessed, and non-ground interference points are filtered out; constructing a semantic enhanced local path planning model based on a semantic penalty term and a dynamic window path planning algorithm; on the basis of the feasible speed of the quadruped robot, the preprocessed multi-source point cloud fusion information and a semantic enhanced local path planning model, feasible trajectory selection is carried out, and an optimal obstacle avoidance planning path is determined. And the navigation safety and the task execution efficiency of the robot in a high-risk industrial environment are remarkably improved.

The invention belongs to the technical field of robot control, and particularly relates to a self-adaptive whole-body control method suitable for a quadruped robot in an uncertain dynamic environment, and the method integrates an extended state observer (ESO), a model prediction controller (MPC) and a whole-body controller (WBC) based on hierarchical optimization. And a closed-loop control architecture is constructed to improve the robustness and motion coordination ability of the quadruped robot in a complex environment. Firstly, external disturbance is estimated and compensated in real time through ESO; then, the MPC optimizes future system behaviors in a rolling manner under the condition of considering interference terms, and an expected foot end counter-acting force is output; and finally, the WBC is combined with the priorities of the multiple tasks to generate all joint control instructions in a constraint optimization form, and task collaboration and dynamic adaptation are achieved. The method has the advantages of being high in disturbance self-adaptive capacity, high in control precision, suitable for a multi-task environment and the like, and is particularly suitable for quadruped robot control tasks in complex dynamic scenes such as transportation and search and rescue.

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.

The invention discloses a quadruped robot motion control method based on topographic map reinforcement learning. The quadruped robot motion control method comprises the following steps: S1, building a quadruped robot model; s2, constructing a topographic map confidence estimation model; s3, constructing a state estimation model; s4, constructing an actor network and a commentator network to train the quadruped robot model; and S5, acquiring data of the laser radar and the inertial measurement unit by using a sensor fusion technology, generating a height map around the quadruped robot in real time, and updating environmental terrain information. By introducing the topographic map confidence estimation model and combining topographic map generation and topographic uncertainty estimation, the motion strategy of the quadruped robot in a complex terrain can be adjusted in real time, the adaptability of the robot to irregular terrains, obstacles and topographic changes is remarkably improved, and the reliability of the quadruped robot is improved. And control errors and falling risks caused by topographic changes or sensor noise are reduced.

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 quadruped robot, belongs to the field of quadruped robots, solves the problems that the assembly of the quadruped robot is complicated and the efficiency is low, and adopts the technical scheme that the quadruped robot mainly comprises a trunk, four leg modules and a control box for controlling the operation of the quadruped robot, the leg module comprises a first motor, a second motor, a third motor, a thigh mechanism, a shank mechanism and a foot end piece, the first motor is installed on the trunk, an adapter of the first motor is connected with a connecting assembly, the second motor is sleeved with the connecting assembly of the first motor, and the adapter and the third motor are sleeved with the connecting assembly of the second motor; the third motor is fixedly connected with the thigh mechanism through a connecting assembly, an adapter of the third motor is connected with the shank mechanism through a connecting rod, the control box is installed in the trunk, and the first motor, the second motor and the third motor are all electrically connected with the control box. The method is mainly used for improving the assembly efficiency and the maintenance efficiency of the quadruped robot.

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 belongs to the technical field of legged robot control, and provides a quadruped robot robust motion control method and system based on joint learning, and the method comprises the steps: obtaining the body observation information and privileged observation information of a quadruped robot; extracting key features in the obtained privileged observation information by adopting a privileged encoder to obtain a first potential feature vector; obtaining a second potential feature vector matched with the first potential feature vector based on the obtained historical ontology observation information and an adaptive network; constructing a joint loss function according to the obtained first potential feature vector and the second potential feature vector, and performing strategy network updating training by taking the minimum joint loss function as a target; and robust motion control of the quadruped robot is completed according to the trained strategy network.

The invention discloses a delivery task cooperative processing method, device and equipment based on a quadruped robot and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: analyzing a delivery task through an upper computer, combining the real-time dynamic information of an office scene, and employing an improved binary particle swarm optimization algorithm to optimize the task distribution of multiple robots, and efficient collaborative distribution in an intelligent office environment is realized. The robot receives a distribution scheme sent by the upper computer, a multi-level navigation strategy is started by utilizing environmental perception data, a topological map and a D * Lite algorithm are fused to carry out path planning, and dynamic obstacle avoidance and efficient distribution are ensured. The target path planning information is monitored in real time through the upper computer, dynamic analysis is carried out through the scene perception algorithm, the scene relevance result is obtained, the collaborative distribution strategy is executed to complete the distribution task, the task response speed and the distribution efficiency are improved, interference to the office environment is reduced, and the work efficiency is improved. And automation and intelligentization of robot distribution in an intelligent office scene are realized.

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 robot control, and provides a robot body self-balancing control method applied to a quadruped robot, the robot body self-balancing control method comprises the following steps: S1, detecting a slope angle and attitude offset data of the quadruped robot in real time, the attitude offset data comprising roll angle offset and pitch angle offset; s2, transmitting the slope angle and attitude offset data to a main control system; s3, the main control system executes partition compensation control according to the attitude offset data: when roll angle offset exists, a left and right leg height compensation instruction is generated, and the robot is controlled to stretch out and draw back a left leg and a right leg; when the pitch angle deviation exists, a front and rear leg height compensation instruction is generated, and the robot is controlled to stretch out and draw back a front side leg and a rear side leg; and S4, performing partition compensation control through iteration to enable the fuselage to return to the horizontal position again and maintain the balance state.

The invention relates to a quadruped robot laser odometer method based on ground point optimization, hardware and application, and the method comprises the steps: obtaining an original point cloud collected by a laser radar, carrying out the preprocessing, dividing a polar coordinate region, jointly extracting ground plane feature points and ground geometric edge feature points based on normal vector and local curvature features, and carrying out the calculation of the ground plane feature points and the ground geometric edge feature points; ground geometric edge feature points are utilized to optimize ground model fitting; optimizing a point set of the ground based on a time sequence reference plane of the ground; the optimized ground points are removed, and non-ground point features are extracted from the remaining point clouds; establishing feature association between the feature points in the current frame and the target point cloud, and screening effective matching pairs; a cascade weight optimization objective function associated with the ground point plane constraint, the non-ground point line feature constraint and the surface feature constraint is constructed, and continuous pose transformation of the quadruped robot is solved; hardware is realized based on the method, and the method is applied to real-time pose estimation and environment mapping of the quadruped robot in outdoor unstructured terrains and complex dynamic environments.

According to the quadruped robot, four sets of leg mechanisms are symmetrically distributed on the two sides of a robot body, and each leg mechanism has a first deformation state and a second deformation state; when the four leg mechanisms are in the first deformation state, the quadruped robot is in a robot dog mode; when the four leg mechanisms are in the second deformation state, the positioning shafts of the hub assemblies are combined with the robot body, so that the quadruped robot is in a balance car mode; the driving mechanism is used for driving the leg mechanism to switch between a first deformation state and a second deformation state; the steering mechanism is mounted at the front end of the fuselage body; the handle mechanism is rotatably connected to the steering mechanism and can drive the steering mechanism to rotate so that the quadruped robot can steer in the balance car mode. According to the quadruped robot, the dual requirements of a user for a robot dog and a balance car can be met, in the balance car mode, the hub assemblies and the robot body are combined to form rigid support, and the motors at the joints of the leg mechanisms do not need to continuously provide loads.

The invention provides a quadruped robot based on sliding detection and an anti-falling method thereof.The method comprises the steps that a plastic deformation area of soil is analyzed and processed, and sinking depth distribution of foot ends in the soil is obtained; based on attitude data and sinking depth distribution obtained by a foot end inertial measurement unit, the slippage of the foot end relative to the soil is processed, and a motion state estimation value of the foot end in the soil is obtained; inputting the motion state estimation value into a deep reinforcement learning agent to obtain a control action parameter output by the deep reinforcement learning agent; updating an actor network of the deep reinforcement learning agent based on the control state parameters to obtain a control strategy and optimized control action parameters, and judging whether the foot end sinks or slips in the soil or not; if the foot end sinks or slides in the soil, sliding detection is started. The stability and the energy efficiency of the robot in the complex soil environment are effectively improved.

The invention belongs to the technical field of robot motion control, and particularly relates to a quadruped robot control method based on error symbol robust integral feedback, which combines an RISE control mechanism with a constraint-based optimization method, and realizes high-precision trajectory tracking and strong-robustness control under the condition that a system model has relatively large uncertainty. According to the method, RISE controllers are designed in a position subsystem and a posture subsystem respectively, quadratic programming (QP) optimization distribution of ground contact force is fused, control input is dynamically adjusted while the physical feasibility of foot end force is ensured, and closed-loop stable control over the whole-body movement of the quadruped robot is achieved. The control strategy has an asymptotic error convergence characteristic, and can be widely applied to quadruped robot motion tasks with severe load change and frequent interference.

The invention provides a quadruped robot self-adaptive following method and device based on multi-modal space-time fusion, a quadruped robot and a storage medium, and the method comprises the steps: obtaining sensing data in multiple modals based on multiple sensing devices deployed on the quadruped robot, performing time and space alignment on the sensing data in various modes to obtain aligned sensing data; performing feature extraction on the aligned sensing data in the multiple modes to obtain feature data in the multiple modes, and performing attention processing on the feature data in the multiple modes by using a pre-trained multi-head attention network to obtain association degrees among the multiple modes; based on the correlation degrees corresponding to the multiple modes respectively, motion track planning of the automatic following target object is carried out; and under the condition that the target object is lost in the sensing data in any mode, executing motion compensation so as to obtain the target object again in the mode where the target object is lost.

The invention discloses a quadruped robot anti-disturbance motion control method based on reinforcement learning and application, and belongs to the field of robot motion control. Firstly, a self-adaptive propulsion control frame is provided, and a propeller and a leg movement system of the quadruped robot are deeply fused. And secondly, through cooperative training of a double-encoder system (a teacher encoder and a student encoder) and a predictor, hidden state extraction and environment disturbance reconstruction are realized, so that the robot adjusts a motion strategy in real time under complex scenes such as actuator faults and water flow interference. And finally, in combination with multi-stage reinforcement learning training and privilege information utilization, strategy network output is optimized, and a speed and trajectory tracking task is completed. The motion robustness of the robot under unknown interference and damage conditions is remarkably improved through cooperative control of the propellers and the legs, anti-disturbance decision of hidden state driving and dynamic reward function design. The method is suitable for amphibious environments, can adapt to diversified disturbance scenes without additional fine adjustment, and has high practicability and reliability.

The invention discloses a quadruped robot landing planning method based on scene decoupling and risk avoidance. The quadruped robot landing planning method aims at solving the problems that in the prior art, perception is not precise in a complex environment, and the obstacle avoidance capacity is insufficient. The method comprises the following steps: acquiring and fusing multi-source sensor data, and segmenting an original point cloud; generating a scene decoupling elevation map of a multi-layer structure by using the segmentation point cloud; constructing a multi-objective optimization problem taking risk avoidance as a core based on the elevation map, wherein the multi-objective optimization problem comprises a comprehensive cost function and a strict obstacle avoidance constraint; utilizing a reaction formula adjusting module of a capturable region theory to cope with a dynamic instability risk; and solving the multi-target and multi-constraint optimization problem in real time by adopting a hierarchical solving strategy, and finally generating an optimal foot end drop point considering safety and stability. According to the method, the terrain adaptability, the motion stability and the decision intelligence of the quadruped robot in an unstructured environment are remarkably improved through fine decoupling of a scene and quantitative avoidance of multi-source risks.

The invention discloses a dual-drive self-switching strong-adaptability quadruped robot motion control method, which comprises the following steps of: estimating current topographic features by using sensing signals such as body state feedback, IMU (Inertial Measurement Unit) and sole force, and predicting a falling probability according to the topographic features, so that a quadruped robot is intelligently switched between two driving modes of MPC (Moving Picture Control) independent control and MPC-DRL (Moving Picture Control-Digital Regulation Language) cooperative control; dynamic behavior parameters are introduced to influence the gait and step height of robot movement, the optimization efficiency and smoothness of MPC and the disturbance adaptive capacity of DRL are efficiently fused, and the high-robustness, high-efficiency and self-adaptive movement capacity of the quadruped robot in various terrains under the condition that external sensing information is not relied on is enhanced.

The invention provides a quadruped robot cooperative mooring unmanned aerial vehicle photovoltaic inspection method and device, and the method comprises the steps: obtaining a global image through a photovoltaic power station fixed monitoring network, and positioning a space coordinate set of a suspected fault region based on a visual analysis algorithm; decomposing the space coordinate set into an ordered inspection sequence by using a ground control station, and issuing path planning parameters to a quadruped robot; the quadruped robot is controlled to autonomously navigate to target coordinates according to the path planning parameters, and the mooring unmanned aerial vehicle is released after the posture is stabilized; executing three-dimensional scanning and multispectral imaging by using the mooring unmanned aerial vehicle, returning a fused sensing data packet in real time, and marking abnormal confidence; and after the diagnosis is completed, controlling the mooring unmanned aerial vehicle to autonomously land to the back of the quadruped robot, and dynamically updating an inspection sequence based on the abnormal confidence until the inspection is completed. And the tethered unmanned aerial vehicle is transported through the quadruped robot, so that the operation in a no-fly area is effectively solved, and the inspection efficiency of the unmanned aerial vehicle on the photovoltaic power station is improved.

The invention relates to a quadruped robot motion information determination method and device and a quadruped robot. The method comprises the steps that data collected by a multi-modal sensor is obtained, the multi-modal sensor comprises a binocular camera and a depth camera, the binocular camera is arranged on the front side of the quadruped robot, and the depth camera is arranged on the front side of the quadruped robot; the depth cameras are symmetrically arranged on the front side and the rear side of the quadruped robot; determining BEV features corresponding to the current moment according to the data collected by the multi-mode sensor; bEV features corresponding to historical moments are obtained; determining a time sequence BEV feature according to the BEV feature corresponding to the historical moment and the BEV feature corresponding to the current moment; according to the data collected by the multi-modal sensor, determining multi-modal fusion features after spatial alignment at the current moment; and determining an obstacle avoidance path and gait parameters of the quadruped robot according to the time sequence BEV features and the multi-modal fusion features after spatial alignment at the current moment.

The invention discloses a quadruped robot joint zero point progressive self-calibration method based on an incremental encoder. The quadruped robot joint zero point progressive self-calibration method comprises the following steps that an operator manually adjusts a robot to a squatting posture; after the encoder is powered on, a relative zero point is recorded and cleared, a key zero point offset vector containing errors is output, and meanwhile a multi-sensor fusion state estimator is initialized; through three stages of hip abduction coarse calibration, fine calibration and hip pitching knee joint combined calibration, a global optimal zero vector is output through weighted least square, nonlinear optimization and Newton method solving in sequence; and storing the vector into a global variable, and calculating a real joint angle through unified correction logic. Accurate adjustment or a special tool is not needed, and the operation threshold is lowered; the hardware cost is greatly reduced by adopting an incremental encoder, and the robot is assisted to be light; the method does not depend on mechanical limiting, stability is maintained through active lifting and double-thread control, mechanical abrasion is avoided, and the large-scale commercialization requirement is met.

The invention relates to a quadruped robot parkour navigation method and system based on multi-modal feature fusion, and the method comprises the steps: constructing a multi-modal parkour topographic data set which comprises a depth image, body feeling information and a control instruction; encoding the depth image sequence and the body sensing sequence of the historical time step to obtain space-time fusion features; encoding the control instruction and the corresponding target position vector to obtain a historical decision feature; encoding a depth image collected at the current moment to obtain fine spatial features; fusing the space-time fusion feature, the historical decision feature and the fine space feature to form a conditional context; and based on the diffusion strategy model, by taking the conditional context as a condition, carrying out de-noising processing, and generating a navigation control instruction so as to complete a parkour navigation task. Compared with the prior art, the method has the advantages that the navigation success rate is high, path planning is diversified, complex 3D terrains can be effectively processed, and the problem that high-level planning and bottom-level motion control are disjointed is solved.