854 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 provides a multi-mode cooperative sensing power station high-risk operation inspection method and system, and relates to the technical field of video recognition, and the method comprises the steps: activating an unmanned plane and a quadruped robot after a power station operation task is started; starting a video acquisition unit, and establishing a synchronous video stream; carrying out fusion alignment with the global reference coordinate system through an external synchronization signal; inputting the video sequence of the fused view angle into a multi-view angle action behavior recognition network, and establishing a dangerous behavior grade score; auditory data and olfactory data of the quadruped robot are obtained, and linkage abnormity is established; and polling abnormity is reported according to linkage abnormity and dangerous behavior grade scores. Through the method and the device, the technical problem of low inspection efficiency caused by difficulty in comprehensively identifying potential risks in a dynamic environment due to limitation of a single sensing mode is solved, and the inspection efficiency of a power station is improved by fusing multi-mode data, timely finding and processing the potential risks and improving the accuracy of video and audio identification.

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 and system based on reinforcement learning, and the method can simulate and analyze the motion behavior of a quadruped robot in a virtual environment through building a simulation model of the quadruped robot, remarkably reduces the research and development cost, remarkably shortens the research and development period, converts a balance control problem into a Markov decision process, defines a state, an action and a reward function, and improves the reliability of the quadruped robot. A more intelligent and adaptive balance strategy can be realized, a deterministic strategy gradient algorithm and a simulation model are adopted for interactive training, an efficient balance control strategy can be obtained, robot behaviors are adjusted in real time to keep balance, a walking task is decomposed into two sub-tasks of foothold selection and gait generation by utilizing a hierarchical control idea, and the walking efficiency is improved. Through reinforcement learning, walking flexibility and stability of the robot on irregular terrains are improved, the irregular terrains are built on a simulation platform for experimental verification, robot performance can be safely and repeatedly evaluated, and a strategy is further optimized.

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 discloses a quadruped robot control method and device.The method comprises the steps that quadruped robot joint angle information is obtained, and the quadruped robot joint angle information comprises hip joint adduction and abduction angles, hip joint flexion and extension angles and knee joint bending angles; calculating a rotation error vector according to the joint angle information of the quadruped robot; according to the rotation error vector, a model prediction control objective function is constructed, and the optimization objective of the model prediction control objective function is that the error between a prediction model and an expected state is minimum; according to the model prediction control objective function, quadratic programming solving processing is carried out, and a foot end force prediction value is obtained; generating a joint torque command by using a virtual force model control method; and robot control is conducted according to the foot end force predicted value and the joint torque command. The quadruped robot control method realizes quadruped robot control, improves environmental fitness and anti-rollover capability, and reduces calculation complexity and response delay. The method can be widely applied to the technical field of robot control.

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 provides a quadruped robot fault-tolerant control method and system based on residual learning, and the method comprises the steps: constructing an ontology mechanism model based on phase information, dividing the phases of a supporting stage and a swinging stage based on a diagonal gait, and designing foot end tracks through combining a Bezier curve and a sine curve; designing a six-dimensional reward function including speed tracking, posture balance, foot movement direction, energy consumption control, body contact constraint and foot end contact excitation; a data-driven model based on a heterogeneous actor-commentator architecture is constructed, an actor network integrates terrain information, ontology sensing data and damage parameter estimation values, a commentator network integrates privilege information for strategy evaluation, and network parameters are optimized based on a near-end strategy optimization algorithm; and on the basis of a residual learning thought, a final motion instruction is generated by coupling the correction output by the data driving model with the ontology mechanism model.

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 provides a centroid estimation and compensation method and system for a variable-configuration quadruped robot with an arm, and relates to the technical field of quadruped robot motion planning, and the method comprises the steps: obtaining the state feedback information of the variable-configuration quadruped robot in the motion process; on the basis of the state feedback information, foot end contact force of the supporting legs is calculated through a whole-body dynamic model of the variable-configuration quadruped robot and a disturbance force observer based on generalized momentum; according to the foot end contact force of the supporting leg, adopting a centroid estimation algorithm of an online recursive strategy to estimate a centroid deviation value after unknown disturbance; according to the estimated centroid deviation value, a motion trail planner and a centroid dynamical model are adjusted, centroid deviation compensation is conducted through a whole body controller of the null space based on the adjusted result, the optimal joint torque is obtained, and real-time servo control is conducted; according to the invention, stable control of the quadruped robot under disturbance of internal force or external force such as double-arm movement, leg configuration change and unknown load is realized.

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 belongs to the technical field of robot control, and provides a quadruped robot fall-down self-recovery control method and system under complex terrains. The method comprises the following steps: extracting a tumble self-recovery strategy feature from privilege information subjected to normalization processing by using a teacher network, so as to provide an optimization reference for a student network; learning strategy characteristics simulating a teacher network by using a student network so as to process the normalized historical observation information of the ontology state information; after the strategy converges, the Actor network is used for processing the output characteristics of the student network and the real-time observation information of the normalized body state information, the next state observation information, the reward signal, the cost feedback and the task end mark are obtained through interaction with the simulation environment, and the action of the quadruped robot is output; evaluating the value of adopting a specific action in the current state; and processing the privilege information subjected to normalization processing by using a Cost network, and evaluating the cost of the current action of the quadruped robot.

Belongs to the technical field of motion control of foot-type robots, and particularly relates to an anti-interference whole-body control method and system for a quadruped robot based on stability margin perception. The expected joint position, speed and foot end contact force are obtained through nonlinear model predictive control NMPC; a whole body controller WBC based on hierarchical quadratic programming calculates a joint driving torque according to the task priority; according to the joint position, the speed and the torque fed back by the driver and a robot dynamic model, estimating the foot end contact force; calculating resultant force which causes instability of the robot relative to each support polygon side line of the robot; projecting the resultant force, and calculating an included angle between the projection force and the vertical direction; calculating a compensation acceleration through a PI controller; and the compensation acceleration is used as a part of a fuselage linear acceleration task in the WBC, so that the anti-interference capability of the robot is improved. The method is used for solving the problems that in the prior art, the stable state of the quadruped robot cannot be effectively sensed, and an anti-interference control strategy cannot be rapidly generated according to the stable state.

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 discloses a quadruped robot reinforcement learning motion planning method and system based on depth vision, and relates to the technical field of quadruped robot motion planning. The method comprises the steps that a simulation environment is built, modeling is carried out on stair terrains with different gradients and robot kinetic parameters, and a stair terrain model comprises stair terrains with different difficulty differences; the robot model is loaded to move in the stair terrain model, the movement condition of the robot is tracked, the speed tracking effect is obtained, and the speed tracking effect is obtained by conducting strategy evaluation on the joint torque value output by the teacher strategy network and the corresponding movement linear speed in the movement process of the robot; the speed tracking effect is evaluated, and environment parameters in the stair terrain model are adaptively adjusted according to the evaluation result. Through the reinforcement learning framework based on the depth visual information, the irregular stair terrain can be effectively learned, and the quadruped robot can stably work in a complex environment in a self-adaptive mode.

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 knee / elbow wheel type quadruped robot, and relates to the technical field of quadruped bionic robots, the knee / elbow wheel type quadruped robot comprises a robot body and four legs arranged at four corners around the robot body, each leg comprises a thigh body and a shank body, and a knee / elbow joint is formed at the joint of the thigh body and the shank body; mounting seats are arranged at the knee / elbow joints, driving wheels are rotationally connected to the mounting seats, and the driving wheels are located on the outer sides, relative to the robot body, of the knee / elbow joints; when the driving wheel needs to be used for walking, the shank body is controlled to be bent towards the thigh body around the knee / elbow joint till the driving wheel makes contact with the ground and the foot of the shank body leaves the ground, and then the machine body can be driven to advance through the driving wheel. According to the robot, the driving wheels are arranged at the knee / elbow joints, the flexibility and adaptability of a leg structure of a traditional quadruped robot are reserved, rapid and efficient movement on the flat terrain is achieved through the driving wheels, and the application range of the robot is widened.

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.

A quadruped robot with multimodal capabilities and embodied large-scale models for use in a 5G nuclear power plant, characterized in that it comprises: a control module, a power module, a robot arm module, a power supply module, a sensor module, a heat sink, an audio module and a housing structure, wherein the control module, the power module, the robot arm module, the power supply module, the sensor module, the heat sink and the audio module are mounted on the housing structure, wherein the control module is respectively connected to the individual modules, iethe power module, the robot arm module, the power supply module, the sensor module, the heat sink and the audio module to control and communicate with the robot, wherein the sensor module comprises a depth camera, a laser sensor, a mobile phone recognition device, an infrared sensor, an ultrasonic sensor and a radiation dose chip, wherein the power module consists of a motor and an electronic speed controller and is thus responsible for the movement of the robot, wherein the robot arm module comprises a motor and a robot arm controller as well as an integrated NFC module on the robot arm, and wherein the power supply module comprises a battery, a power supply manager and a power supply display screen, while the audio module provides remote voice interaction.

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 discloses a quadruped robot system for indoor fire rescue. The quadruped robot system comprises a quadruped robot body, a main control unit carried by the robot, a sensing unit, a navigation unit, an execution unit and a motion control unit. The main control unit comprises two high-performance industrial personal computers which respectively process a motion control algorithm and a visual perception algorithm. The industrial personal computer communicates with other sensors through an IP and a serial port, and wirelessly communicates with the rear end of the fire-fighting command center through a TCP. After the fire-fighting command center issues a rescue task to the quadruped robot, the robot constructs a global map through the laser radar, and searches a fire source and trapped persons through the depth camera. When the robot recognizes the target, the robot sends the target position to the rear end of the fire-fighting command center, automatically avoids obstacles, and navigates to the target position to execute fire extinguishing and rescue work. The firefighter can be helped to quickly find a fire source and trapped persons, determine the positions of the trapped persons in the map, and guide the firefighter to reach the area to carry out rescue.

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.