481 results about "Quadrupedal robot" patented technology

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 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 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 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.

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 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 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.

The invention relates to the technical field of hybrid intelligence, in particular to a quadruped robot motion control and operation system for an industrial environment, which realizes full-link intelligent management from environment perception to motion control, and remarkably improves the operation precision of a robot and the system efficiency. A three-dimensional map is constructed through a multi-sensor fused environment sensing module, and precise positioning is realized by combining state estimation based on deep learning; a motion control module with a hierarchical control architecture is adopted, and stable motion under a complex terrain is ensured through model prediction and gait optimization; the digital twinning and cloud side end collaboratively constructs a virtual-real mapping system to realize predictive maintenance and remote monitoring; and the multi-agent coordination module realizes task coordination and resource optimization of the multi-robot system through a distributed scheduling algorithm.

The invention discloses a quadruped robot space precise positioning method and system based on deep learning. The quadruped robot space precise positioning method specifically comprises the steps that S1, a laser radar data stream and an inertial measurement unit data stream are aligned to generate a synchronous sensing data sequence in a unified mode; s2, determining a mapping pose sequence through consistency evaluation of poses output by the offline point cloud registration algorithm pool; s3, performing parallel loopback detection to generate a unified loopback constraint set; s4, the mapping pose sequence, the loopback constraint set and the pre-integration constraint are written into the factor graph model to generate a three-dimensional point cloud reference map; s5, the local point cloud frame and the three-dimensional point cloud reference map are registered, and a map matching pose is output; s6, the map matching pose and the pre-integration result are written into a factor graph model to continuously solve and output a continuous-time six-degree-of-freedom pose result; and S7, outputting a six-degree-of-freedom pose result in continuous time. According to the invention, direction constraint consistency point cloud registration and dual-channel loopback optimization are introduced, and stable and high-precision positioning of the closed space is realized.

The invention discloses a decoupling motion control method and system for a four-footed mobile operation robot considering the acting force of a mechanical arm, and belongs to the field of motion control of foot type mobile operation robots. An expected motion track input by a user can be tracked by performing planning through linear model prediction control MPC; on the basis of the tracked motion trail, mechanical arm joint control torque is calculated through a mechanical arm dynamic model and PD feedback; through nonlinear model predictive control NMPC planning, a quadruped robot whole-body motion trail of mechanical arm acting force obtained through calculation according to a mechanical arm dynamic model is considered, and an expected speed trail and an expected force trail input by a tracking user are obtained; and according to an expected speed trajectory and an expected force trajectory input by a tracking user, a whole body controller WBC based on hierarchical quadratic programming calculates a joint driving torque for tracking the expected trajectory according to task priorities. According to the method, the acting force / torque of the mechanical arm to the robot body is considered during motion control of the quadruped robot, and decoupling control over the mechanical arm and the quadruped robot in the quadruped mobile operation robot is achieved.

The leg device comprises a leg structure, a joint assembly, a heat conduction strip and / or a heat dissipation protection sheath wrapping a thigh shell, the joint assembly comprises a knee joint driving mechanism, and the knee joint driving mechanism is provided with a knee joint driving output end arranged in a joint connecting channel; the heat conduction strip is of a sealed cavity structure and is filled with heat conduction liquid, the heat conduction strip comprises a first heat conduction section and a second heat conduction section which are connected with each other, and the first heat conduction section is attached to the side, close to the thigh shell, of the knee joint driving mechanism; the second heat conduction section is attached to the inner side wall of the thigh shell and extends from the first heat conduction section to the shank component. According to the leg device, heat dissipation is achieved through the combination of the heat conduction strips and the leg shell, so that the overall structural arrangement of the leg device is more compact and smaller, the weight is lighter, the energy consumption of the robot can be reduced, and the movement flexibility and the response speed of the robot are ensured.

The invention provides a mechanical arm teleoperation system adaptive to a quadruped robot and a control method, and relates to the technical field of robot control. According to the method, firstly, a far-end main mechanical arm is introduced on the basis of a mechanical arm of the quadruped robot to form a main-end mechanical arm system and a slave-end mechanical arm system, and a remote operator remotely maps an action instruction to the slave-end mechanical arm system by controlling the main-end mechanical arm; then, a clamping jaw module with an image and tail end contact force sensor is installed at the tail end of the slave end mechanical arm, and image data collection and tail end contact force collection on a working site are achieved; secondly, the image information and the tail end contact force are remotely transmitted to a remote operator in real time through a wireless transmission module and a remote man-machine interaction module; and finally, a remote operator controls the main end mechanical arm in real time according to the real-time image information and the tail end contact force, remote operation of the quadruped robot mounting mechanical arm in a complex scene is achieved, and the operation efficiency and the operation precision are improved.

The invention discloses a chain type hybrid deep reinforcement learning quadruped robot control system. The system comprises a sensing module, a data processing module, a chain type hybrid reinforcement learning model module, a feedback module and the like. The invention further discloses a control method of the chain type hybrid deep reinforcement learning quadruped robot control system, and the method comprises the steps: S1, collecting bionic motion data, robot state data and environment data, and training based on an attention mechanism space-time diagram neural network and antagonistic imitation learning to obtain a reference gait model containing motion prior; and S2, constructing a three-level parallel simulation environment, setting a progressive course target, training the progressive course target by adopting an improved PPO algorithm, and optimizing the gait stability, the movement efficiency and the environmental adaptability. By means of high-precision data acquisition with the sampling frequency larger than or equal to 240 Hz and antagonistic imitation learning, motion priori knowledge is introduced, the training period can be shortened by 50% or above, abnormal gaits are avoided, and the training process is controllable.

The invention provides a quadruped robot wet-slippery rugged terrain motion control method based on explicit and implicit estimation, and aims to solve the problem that the motion ability of a quadruped robot under the wet-slippery rugged terrain is limited due to the fact that the existing method cannot fully extract environment and robot motion state information. Historical observation information and a current action are utilized to explicitly estimate an environment friction factor, a four-foot contact state and a robot centroid velocity, and current motion model information of the robot is implicitly estimated through a method of predicting a motion state at a next moment. Environmental information, robot motion information and implicit vectors containing robot motion model information are introduced into input of a motion strategy, higher sensing capacity for the motion state and the environment is provided for a motion network, and a novel control method suitable for the quadruped robot to move in the slippery terrain is obtained.

A vision-based tracking control method for a quadruped robot includes receiving unilateral images captured by multiple cameras pointing different directions and utilizes an image stitching algorithm to synthesize the unilateral images into a panoramic image; based on the panoramic image, determining first and second position coordinates of the moving target and environmental obstacles; and formulating or modifying a navigation path for tracking the moving target based on the first and second position coordinates. The method significantly improves the perception ability, navigation accuracy and robustness of the quadruped robot in dynamic environments, thereby achieving more intelligent and flexible control.

The invention discloses a multi-attitude test platform for a quadruped robot, and relates to the technical field of intelligent robot testing, and the test platform comprises a main pedal, a lifting mechanism assembly, a driving mechanism assembly, a driven mechanism assembly and a pressure sensor assembly. A mounting hole is formed in the surface of the main pedal; the lifting mechanism assembly comprises a lead screw servo motor, a lead screw, a guide rail, a sliding plate, a transmission belt and the like. The lead screw servo motor drives the lead screw to be matched with the driven mechanism to achieve pitching motion of the main pedal. The driving mechanism assembly comprises a servo motor, a worm and gear reduction gearbox, a fixing shaft, a right lifting plate and the like. The servo motor drives the worm and gear reduction gearbox to achieve inclined rotation movement of a main pedal. The driven mechanism assembly comprises a guide rail, a sliding table, a driven shaft, a left lifting plate and the like. And the pressure sensor assembly comprises a small top plate, a spring and a pressure sensor and is arranged in the mounting hole. The test platform can simulate slope environments of different angles according to needs, is used for multi-posture static test of the tested quadruped robot, collects change parameter data of standing postures of the quadruped robot in the slope environments of different gradients through devices such as the pressure sensor and a built-in sensor of the robot, and performs multi-posture static test on the quadruped robot according to the change parameter data. Test preparation is made for optimizing and adjusting the standing posture of the quadruped robot in the later period, so that the stable performance of the robot is guaranteed, and the purpose of testing the multiple postures of the quadruped robot is achieved.

The invention relates to an airport foreign matter detection and cleaning robot, a control method thereof and a dual-redundancy device fault processing method, and aims to solve the technical problems that an existing airport foreign matter detection and cleaning robot is difficult to meet the detection-cleaning integrated requirement of an airport complex scene and is low in working efficiency. The invention provides an airport foreign matter detecting and cleaning robot. The airport foreign matter detecting and cleaning robot comprises a quadruped robot, a mechanical arm assembly, a visual assembly and a clamping jaw assembly. The clamping jaw assembly comprises a clamping jaw driving assembly, two in-place sensors, a clamping jaw and a supporting frame. The clamping jaw driving assembly comprises a metal shell, a clamping jaw control plate arranged in the metal shell, a driving plate and a motor, and an encoder is arranged on the motor; the support frame is arranged at the top of the metal shell through the in-place spring; the two in-place sensors are arranged on the two sides of the supporting face of the supporting frame respectively.

The invention discloses a weeding method of a quadruped robot and a control system of the quadruped robot. The method comprises the steps that the quadruped robot is controlled to enter a weeding operation area based on a preset advancing path, and to-be-removed weeds located in front of a robot body of the quadruped robot are determined according to sensing data in the weeding operation area; determining a weeding phase according to the current gait cycle of the robot body; according to the current stopping posture of the robot body, the space coordinates corresponding to the to-be-removed weeds and the gravity center shift predictive quantity of the robot body, track interpolation is conducted on the track at the tail end of a footprint of the robot body, and a weeding track is obtained, the weeding track represents a track from the current foot stopping position of the robot body to the weed position of the weed to be removed; and using a weeding cutter to cut off weeds to be removed within the weeding phase based on the weeding trajectory.

According to the quadruped robot with the automatic charging device and the charging device design method, the automatic charging device is installed on the lower portion of a robot body of the quadruped robot, and the automatic charging device is composed of a charging connecting base connected with the lower portion of the robot body and a flexible protection piece; a mounting groove is formed in the side, close to the machine body, of the flexible protection part, the mounting groove and the side wall of the machine body define a protection cavity for containing the charging connecting base, the flexible protection part can deform to be in a contraction state after being extruded in the direction from an external charging pile, and the charging interface part can be in butt joint with the external charging pile in the contraction state. The charging operation is performed. The flexible protection piece is used for protecting the charging interface piece, the charging interface piece can be prevented from being scratched by other devices to be damaged when charging is not carried out, the flexible protection piece can prevent the charging interface piece from being directly impacted when charging is carried out, the charging interface piece is effectively protected, and the service life of the charging interface piece is prolonged.

The invention belongs to the field of robot control, and particularly relates to a quadruped robot accurate gait control method based on dynamic optimization. The method comprises the steps that phase states of four legs of the quadruped robot are judged, supporting phase foot end force is calculated for the legs in a supporting phase according to a real-time motion state and an expected motion state, and swinging phase correction force is calculated for the legs in a swinging phase; an expansion state observer is constructed based on the leg Cartesian space dynamics model, and the expansion state observer is used for observing the total disturbance term of the four legs of the quadruped robot in real time; calculating to obtain a disturbance value to be compensated based on the total disturbance term, obtaining an uncompensated joint torque based on the support phase foot end force or the swing phase correction force, and superposing to obtain a joint torque instruction; and driving the quadruped robot according to the joint torque instruction. According to the method, an advanced observation and compensation mechanism is introduced in a bottom layer execution link, accurate implementation of an upper layer optimization target is ensured, and finally accurate gait control is achieved.

The invention discloses a multi-source information fused intelligent inspection system, method and equipment with a target guiding function. The system comprises an inspection management platform, a data base station, a wireless routing base station, a quadruped robot, a pose sensor and a multi-mode sensor unit, wherein the inspection management platform, the data base station, the wireless routing base station, the quadruped robot, the pose sensor and the multi-mode sensor unit comprise a laser radar, a visible light camera and an infrared thermal imager; a control processing terminal is arranged on the quadruped robot and is used for acquiring data acquired by the multi-mode sensor unit and the pose sensor, processing the acquired data and outputting a control signal according to external instruction data, so that the quadruped robot moves according to an inspection planning path; the multi-mode sensor unit and the pose sensor are controlled to work; and the inspection management platform is used for analyzing and processing the data acquired and processed by the control processing terminal, and outputting instruction data including an inspection planning path to the control processing terminal. According to the invention, the inspection safety of engineering scenes in dangerous environments such as underground spaces and tailings ponds can be improved.

The invention discloses a complex soft obstacle environment-oriented quadruped robot crossing advancing method, which comprises the following steps of S1, actively interacting with the terrain under the condition of no prior environment perception, and obtaining terrain mechanical property information; s2, based on the obtained terrain mechanical property information, deeply analyzing the terrain, evaluating the risk of the robot advancing in the terrain, and adjusting the gait according to the risk; and S3, comprehensively evaluating the traveling safety of the robot on the current terrain, and deciding whether to pass safely or take recovery measures according to the risk level. The method gets rid of the dependence of the traditional technology on geometric contour perception of an optical sensor, and can directly obtain key mechanical parameters of subsidence, shearing and the like of soft terrains such as sludge and thick snow on line; meanwhile, in combination with a parameter estimation method for simplifying a Bekker model, the problem of perception deviation caused by large mechanical property difference of visual similar areas is solved, and reliable environmental data support is provided for subsequent planning and control.

The invention relates to a quadruped robot weld crack detection method based on knowledge distillation and transfer learning. The quadruped robot weld crack detection method comprises the steps that weld images are collected to construct a weld image data set; constructing and training a teacher model at the cloud based on an RT-DETR architecture; constructing a lightweight student model at the edge end based on the teacher model; introducing a channel projection operator, and respectively mapping the multi-scale intermediate features and the multi-scale fusion intermediate features extracted by the teacher model to the same dimension as the multi-scale features and the multi-scale fusion output feature map extracted by the lightweight student model; establishing feature alignment loss and comprehensive distillation loss of the student model based on the distance between the features, and training a lightweight student model in combination with student detection loss; designing transfer learning based on feature alignment and adversarial training for the student model; and carrying out model deployment and increment optimization. According to the method, lightweight deployment and continuous optimization of the high-precision detection model are realized, and the detection reliability of the quadruped robot in a complex industrial environment is improved.

The invention provides a quadruped robot autonomous navigation method and system based on multi-sensor fusion, and relates to the technical field of robot navigation, and the method comprises the steps: carrying out the calibration, feature extraction and alignment of multi-sensor data, and constructing a three-dimensional environment semantic map; generating a navigation path by considering kinematics and dynamics constraints; dynamically selecting foot end drop points and tracks according to topographic features; and feeding back and correcting the map model in real time. According to the method, the sensing precision, the path planning rationality and the motion stability of the quadruped robot in a complex environment are improved, and the self-adaptive navigation capability of the robot is enhanced.

The invention discloses a robust self-adaptive multi-skill learning method for a quadruped robot based on key frame guidance. The robust self-adaptive multi-skill learning method comprises the following steps: determining a target key frame set of multi-skill learning; in a flat terrain environment, probability sampling is carried out on the key frames according to skill task values to generate skill instructions, the quadruped robot is controlled to execute actions, and training tracks are collected and stored in a strategy learning experience playback pool and a self-simulation learning experience playback pool respectively. Training the strategy network and the value function network through strategy learning circulation; a self-mimicking learning discriminator network is trained through a self-mimicking learning loop to extract a high quality trajectory. And after the flat terrain training is completed, entering a complex terrain environment, and carrying out adaptive training under the guidance of the trained strategy network and the self-simulation learning discriminator network so as to realize the migration of the skill primitives to the complex environment. According to the method, multi-skill learning and terrain adaptive capacity can be effectively considered, and the robustness, flexibility and multi-task execution performance of the quadruped robot are improved.

The invention discloses a quadruped robot navigation method and system based on three-dimensional environment mapping and navigation, and belongs to the technical field of mobile robot navigation. The method comprises the following steps: acquiring a non-repeated scanning three-dimensional point cloud and a high-frequency pose to carry out pre-integration distortion removal; extracting features for joint optimization, and constructing a global three-dimensional point cloud topological map; stripping ground point cloud by using a double-track map mapping mechanism, and performing dimensionality reduction to generate a two-dimensional multi-dimensional cost navigation map; column cycle matching is carried out by using two-dimensional polar coordinate context features in a positioning degradation environment, and continuous poses are forcibly corrected; and forward sampling and evaluation are carried out based on the navigation map and the multi-dimensional trajectory evaluation cost function model, and closed-loop motion of the robot is controlled. According to the method, the problems of high computing power consumption of high-dimensional space calculation and easy failure of positioning in a degraded environment are effectively solved, and the safety navigation capability of the quadruped robot in a complex environment is remarkably improved.

The invention discloses a quadruped robot advancing path selection method and system based on Kalman filtering, and belongs to the technical field of robot movement control. The method is used for solving the technical problems that sudden change of foot end contact force of a quadruped robot or terrain slope change can cause model mismatch, and then filtering divergence or estimation deviation is caused. A kinematic model is corrected in real time through a slope angle, so that system state prediction is more fit with an actual terrain, noise covariance is dynamically enhanced when contact stiffness is suddenly changed, and the situation that small-probability large sudden change is excessively suppressed by filtering is avoided; the method can dynamically adapt to changes of terrain gradient and contact rigidity, according to the output filtering optimal state estimation and in combination with a preset foot end landing point stability evaluation function, an advancing landing point meeting force-pose constraint is generated, the robot is driven to execute, and the quadruped robot can autonomously select a stable landing point in an unknown complex terrain.