166 results about "Elevation map" patented technology

The invention provides an elevation map path planning method and system based on a terrain adaptive potential energy field, and relates to the technical field of path planning. Comprising the steps of calculating the gradient and roughness of the position of an intelligent agent, setting a terrain adaptive gain, dynamically adjusting the gradient and roughness, and obtaining a terrain adaptive repulsive force field and a composite potential energy field in combination with a basic repulsive force field; determining the motion direction of the agent based on the composite potential energy field, and determining the next position of the agent; in the process that the intelligent agent moves to the next position, whether local re-planning needs to be carried out on the next position of the intelligent agent or not is judged based on a preset environment change judgment rule; and when re-planning is needed, recalculating the terrain adaptive repulsive force field and the dynamic obstacle repulsive force field, updating the composite potential energy field, and re-determining the next position of the intelligent body. According to the invention, the terrain can be effectively sensed, the planning strategy is adaptively adjusted, and high precision, high efficiency and strong robustness are considered.

The invention discloses an image recognition system for industrial part defect detection, and particularly relates to the field of part defect detection, which comprises a multi-axis controllable light source array module, a high-speed polarization camera module, an edge computing node module, a double-branch semantic segmentation network module and a physical constraint post-processing module, according to the invention, through combination of time-sharing stroboscopic illumination and polarization image sequence acquisition, multi-dimensional perception of surface topography and material differences is realized; generating an elevation map and a normal map by using photometric stereo solution, constructing a differential rendering layer reverse matching CAD model, and extracting flash sensitive features; a double-branch U-Net network is adopted to fuse geometric and polarization characteristics, the characterization capability is enhanced through a trans-attention mechanism, and a pixel-level mask is output; and finally, mapping a two-dimensional result to a three-dimensional coordinate system by means of calibration parameters, carrying out geometric verification in combination with a tolerance zone and a height threshold value, and automatically generating a structured defect report containing position, size, grade and visual information.

The invention discloses a ground element labeling method and device, equipment and a storage medium, and the method comprises the steps: determining time sequence data corresponding to a target scene, and constructing three-dimensional point cloud data of the target scene based on the time sequence data; based on the three-dimensional point cloud data, determining a bird's-eye view and an elevation map corresponding to the three-dimensional point cloud data, and displaying the bird's-eye view on a user interface; in response to the detected labeling operation for the ground elements in the aerial view, determining first labeling information of the ground elements; and determining a labeling result of the ground element based on the first labeling information and the elevation map. According to the technical scheme, the three-dimensional point cloud data of the target scene is constructed, and the aerial view and the elevation map are determined based on the three-dimensional point cloud data, so that semi-automatic manual labeling is performed at the BEV perspective, and meanwhile, the 3D labeling result of the ground element is obtained in combination with the elevation map, so that the high-quality ground element labeling true value can be conveniently and quickly obtained.

The invention provides a mechanical arm real-time stacking model generation method and device based on a 2.5 D elevation map. The method comprises the steps that depth point cloud data of a stacking area is obtained; preprocessing the depth point cloud data to obtain de-noised point cloud data; performing external parameter calibration by adopting Eye-to-hand to obtain an accurate conversion relation between a camera coordinate system and a mechanical arm flange coordinate system, and performing coordinate system conversion to obtain de-noised point cloud data under a unified world coordinate system; performing space clipping on the de-noised point cloud data to obtain effective point cloud data in the stacking area range; a mechanical arm directional bounding box is generated under the unified world coordinate system, and whether the mechanical arm directional bounding box intersects with the stacking area or not is detected through the separation axis theorem; if not, a 2.5 D elevation map is generated according to the effective point cloud data in the stacking area range, and a real-time stacking model of the mechanical arm is obtained according to the 2.5 D elevation map; therefore, quick response and flexible adjustment are realized in a dynamic scene.

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 method for automatically generating an ancient building elevation by using a multi-modal neural network, and the method comprises the steps: obtaining and preprocessing a three-dimensional inclined point cloud model and a three-dimensional laser point cloud model of a target ancient building, and obtaining the indoor and outdoor point cloud data of the ancient building; performing feature point matching and rigid body transformation fusion on the ancient building indoor and outdoor point cloud data to obtain a three-dimensional ancient building point cloud model; performing semantic segmentation on the three-dimensional ancient building point cloud model through projection and a multi-modal neural network to obtain each structure area of the ancient building; and according to each structural region of the ancient building, extracting each component contour from the three-dimensional point cloud model to generate an ancient building elevation.

The invention discloses a non-flat terrain autonomous navigation method based on path pruning and trajectory smoothing. The method comprises the following steps: constructing an elevation map with three-dimensional map information as a navigation map according to robot navigation space information; based on the navigation map, performing global path search by adopting a path pruning strategy to obtain an initial path from the starting point to the target point; based on the initial path, track optimization is carried out by using a B spline curve, and a smooth track meeting robot dynamics constraints is generated; in response to an obstacle which is detected on the smooth trajectory and cannot be avoided, performing three-dimensional obstacle crossing trajectory planning based on terrain elevation information of the elevation map, and generating an obstacle crossing trajectory capable of crossing the obstacle; wherein the robot performs autonomous navigation based on the smooth track or the obstacle crossing track. According to the invention, efficient global search, smooth trajectory optimization and intelligent obstacle crossing decision of the robot in unknown and non-flat terrains can be realized.

The invention provides a cross-layer path planning method and system based on a multilayer elevation map, and the method comprises the steps: obtaining three-dimensional point cloud data of a multilayer environment, carrying out the adaptive layering of the three-dimensional point cloud data, and obtaining the number of layers of a point cloud group correspondingly covering all elevation information; constructing an elevation map of each layer of point cloud group; calculating the height difference between the current grid and the adjacent grid to obtain the trafficability of the current grid, analyzing the terrain, and optimizing the elevation map; identifying an effective point at the junction of each layer as a passing gateway point; and performing cross-layer path search based on the constructed multi-layer elevation map and the passage gateway point. According to the method, seamless path planning of cross-layer scenes such as stairs and slopes is realized based on the multi-layer elevation map and the layering strategy, and the problem of path breakage during interlayer transition of a traditional method is solved.

The invention relates to the technical field of material transfer, and discloses an unmanned automatic stacking and transferring method and system for bulk materials, and the method comprises the steps: collecting the three-dimensional point cloud data of a bulk material storage yard, carrying out the splicing of the three-dimensional point cloud data, obtaining the overall point cloud of the storage yard, and building an elevation map according to the overall point cloud of the storage yard; collecting body attitude data of the bulk cargo stacking transfer truck, planning a material taking motion path on the basis of the elevation map according to the body attitude data, updating the three-dimensional point cloud data in real time, and correcting the material taking motion path; and communicating with rear-end transfer equipment, performing cooperative control on a material taking motion path, dynamically calculating the quantity of remaining materials in a storage yard according to the three-dimensional point cloud data updated in real time, and completing unmanned automatic stacking, taking and transferring of the bulk materials. Through unmanned and intelligent control, efficient, safe and stable operation of the bucket-wheel stacker-reclaimer is achieved, manual intervention is reduced, the equipment operation efficiency is improved, and the intelligent operation and maintenance requirements of a large bulk material storage yard are met.

An approach is provided for accelerated flood modeling. Using a down-sampling neural network, a low-resolution elevation map is generated from a high-resolution elevation map. Using a partial differential equation (PDE) model, a low-resolution water depth map is generated from the low-resolution elevation map and one or more boundary conditions. Using an up-sampling neural network, a high-resolution water depth map is generated from the low-resolution water depth map. The down-sampling and up-sampling neural networks are trained by minimizing a loss between the generated high-resolution water depth map and ground truth data generated by the PDE model using the high-resolution elevation map as direct input and without using the low-resolution elevation map.

The invention discloses a method and device for determining a road surface unevenness risk area and a storage medium, and belongs to the technical field of road surface evenness detection.The method comprises the steps that initial point cloud data of a detected road surface are acquired; based on a predetermined calibration relation between a point cloud coordinate system and a three-dimensional detection coordinate system, the initial point cloud data is converted into the three-dimensional detection coordinate system to obtain target point cloud data, the transverse axis and the longitudinal axis of the three-dimensional detection coordinate system are parallel to the detected road surface, and the vertical axis of the three-dimensional detection coordinate system is perpendicular to the detected road surface; determining an elevation map of the detected pavement according to the target point cloud data; and determining an uneven risk area of the detected pavement according to the elevation map. According to the detection method based on the point cloud, the point cloud is converted into the elevation map to determine the uneven risky road surface, and the detection method is not affected by environmental factors such as illumination and is high in accuracy.

The invention provides an unmanned aerial vehicle autonomous homing and obstacle avoidance method, system and device and a medium, and the method comprises the following steps: 1, an unmanned aerial vehicle reads elevation data according to the longitudes and latitudes of a target point and a starting point, and converts the elevation data into three-dimensional coordinates; 2, performing global path exploration by taking elevation data represented by three-dimensional coordinates and unmanned aerial vehicle dynamics constraints as restrictions, and generating a reachable route composed of a plurality of waypoints; step 3, smoothing the reachable route to enable the reachable route to become a better homing route; and 4, homing the unmanned aerial vehicle through the homing route, and avoiding obstacles in the homing process through a laser radar and a DWA algorithm. The method is realized based on an elevation map, and relates to an embedded system, laser radar environment perception and an obstacle avoidance algorithm, so that the unmanned aerial vehicle can safely and quickly realize autonomous homing in an unknown environment.

The invention provides a terrain elevation map generation method and system based on radial basis function fitting, and the method comprises the steps: receiving historical point cloud information and odometer information, generating a local point cloud map, and carrying out the downsampling; dynamically generating a center point of the RBF based on the down-sampled local point cloud map and the ground fitting area; calculating a kernel matrix according to the down-sampled local point cloud map and the central point, and iteratively calculating the weight of the central point through GPU parallel acceleration to generate a terrain manifold; and calculating and outputting an elevation map through GPU parallel acceleration according to the terrain manifold. According to the method, the fitting speed is remarkably increased, the real-time performance of elevation map generation is ensured, and the map generation speed is greatly increased; the elevation information of the dynamic obstacle is updated while the elevation information of the static obstacle is reserved, and perception information is provided for movement, navigation and obstacle avoidance of the intelligent wheelchair; and an elevation map, a gradient map and a normal vector map with any resolution can be output.

According to the hybrid elevation map generation method based on multi-modal data fusion and reinforcement learning, through vision-LiDAR deep fusion, direction perception feature coding, space-time consistency optimization and reinforcement learning dynamic parameter adjustment, elevation map generation with 15-meter long distance and 0.05-meter / pixel high resolution in a medium-speed unmanned driving scene is realized. According to the method, the elevation error is reduced to be lower than 15 cm, the path planning success rate is increased to 90%, the system cost is reduced by 70%, and robust navigation support is provided for a complex unstructured environment.

The invention discloses a point cloud ground segmentation method based on an elevation map, and belongs to the technical field of ground segmentation and modeling. The method comprises the following steps: performing outlier filtering on point cloud data; performing point cloud rasterization on the point cloud data, and calculating a grid point cloud variance; performing ground grid identification on each grid unit according to the grid point cloud data variance; calculating ground grid elevation information to update a local elevation map; non-ground grid elevation information is estimated, and a global elevation map is updated. By adopting the outlier filtering algorithm of the elevation map, the ground points can be dynamically identified according to the surrounding terrain, the false screening of the effective ground points is reduced, and the condition that the point clouds in part of element regions are too sparse is avoided, so that the distribution uniformity and the processing efficiency of the point cloud data are improved, and the requirements on computing resources are reduced; the method is suitable for practical application scenes with limited resources; and a grid point threshold and a variance threshold are dynamically set, so that ground points can be identified and extracted more accurately.

The invention discloses an unmanned aerial vehicle route global planning method based on key feature points, and relates to the field of unmanned aerial vehicle route planning, and the method comprises the steps: carrying out the hierarchical division of a three-dimensional elevation map according to the performance of an unmanned aerial vehicle; according to obstacle pixel proportions of different layers, an optimal layer is screened out; on the basis of the segmentation maps of the optimal layer and the adjacent layer and in combination with the three-dimensional geographic features, obtaining corrected key feature points; utilizing a heuristic weight calculation formula to calculate heuristic weight values of the corrected key feature points; according to a sorting result of the heuristic weight values, a plurality of corrected key feature points of the unmanned aerial vehicle route are obtained according to a connection barrier-free principle and in combination with a three-dimensional path algorithm until a target point is selected; and based on the initial point, the target point and the plurality of corrected key feature points of the unmanned aerial vehicle route, generating an optimized unmanned aerial vehicle flight path after corner optimization. According to the method, the three-dimensional feasible flight route can be efficiently generated, and the calculation time is short.

The invention discloses a wheat harvesting navigation path generation method and an automatic harvester, and relates to the technical field of automatic driving of agricultural machinery, and the wheat harvesting navigation path generation method comprises the following steps: S1, collecting three-dimensional point cloud data of a wheat field through a laser radar, and enabling a collection region to at least cover the region boundary of a harvested region and a non-harvested region; s2, performing noise reduction and voxel grid downsampling processing on the three-dimensional point cloud data to generate a two-dimensional elevation map; s3, setting grid characteristics and threshold values thereof according to the wheat variety height characteristics; and S4, carrying out binarization processing on the two-dimensional elevation map pixel by pixel based on the grid features and the threshold values thereof. According to the wheat harvesting navigation path generation method, boundary extraction and path conversion are carried out based on radar point cloud, the method has the characteristics of robustness and real-time performance, an automatic harvester based on the method can reduce the recutting rate and land rolling compaction during operation, and resources are effectively saved.

The invention discloses a self-adaptive gesture recognition method based on an FMCW (Frequency Modulated Continuous Wave) radar, aiming at solving the problem that the traditional gesture recognition technology limits the position and orientation of a user. According to the method, an FMCW radar is used for collecting gesture signals, distance, speed, azimuth angle and pitch angle information is extracted through four-dimensional fast Fourier transform, and a distance-Doppler diagram, a distance-azimuth diagram and a distance-pitch angle diagram are constructed. A 2D-CA-CFAR algorithm and a DBSCAN algorithm are adopted to carry out interference suppression and target separation, and parameters of gestures and human body targets are extracted. A parameter mapping algorithm based on spatial physical characteristics is designed, gesture parameters of any position and orientation are mapped to a predefined position and orientation, and position and orientation dependency is eliminated. And finally, calculating a matching distance between each trajectory diagram and the reference trajectory by adopting a fast dynamic time warping algorithm, performing multi-dimensional feature fusion, constructing a fusion matching distance, and constructing a classifier based on the fusion matching distance to realize robust recognition of various gestures in different positions and orientations.

The invention relates to the technical field of image data processing, in particular to a geological safety monitoring and early warning method and system. The method comprises the following steps: acquiring an elevation map of a mining area and a current sailing path of an unmanned aerial vehicle for executing safety exploration in the mining area, and positioning the path on the elevation map; marking pixel points on the path as navigation pixel points, and obtaining a plurality of prediction lines of the navigation pixel points; acquiring predicted elevation data of the navigation pixel points; acquiring corrected elevation data of the navigation pixel points; obtaining collapse coefficients of the navigation pixel points; and according to the collapse coefficient, determining a dangerous area of the mining area, and realizing early warning based on geological safety monitoring. According to the method, prediction is performed based on the navigation pixel points and the historical elevation data on the plurality of prediction lines, so that error transmission caused by single-direction prediction is avoided, the accuracy and reliability of elevation data prediction are improved, and the accuracy of safety monitoring and early warning of a mine is further improved.

The invention provides an underground pipe network detection positioning method and device, and the method comprises the steps: obtaining the multi-source data of an underground pipe network of a to-be-detected point and the surrounding environment, including ground image data, inspection well internal image data, adjacent inspection well spacing data, ground elevation data, pipe bottom elevation data, and position coordinates; inputting the ground image data into a semantic segmentation network, and outputting a ground semantic segmentation image; respectively extracting contours of the ground semantic segmentation image and the image data in the inspection well to obtain a first edge image and a second edge image; constructing a pavement information measurement model; pipeline diameter information, pipeline gradient and rain and sewage pipeline mixed connection conditions are determined; and integrating the pavement information measurement model, the first edge image, the second edge image, the pipeline diameter information, the pipeline gradient and the like, and drawing a planar graph and an elevation graph of the underground drainage pipe network. According to the method, high-precision detection and positioning of the underground pipe network are remarkably improved, and the method is suitable for city updating and city informatization construction.

The invention discloses a single-frame unmanned aerial vehicle image pixel positioning method and system based on an elevation map, and relates to the technical field of unmanned aerial vehicle image processing and geographic positioning, and the method comprises the steps: obtaining a single-frame original image shot by an unmanned aerial vehicle, unmanned aerial vehicle attitude information during shooting, camera internal and external parameters, and a digital elevation map of a shooting region; according to the internal reference of the camera, converting the pixel coordinate of the target pixel in the original image into a normalized plane coordinate system of the camera, and carrying out coordinate distortion correction to obtain the back projection point coordinate of the target pixel; converting a camera-unmanned aerial vehicle body coordinate system and a body-geographic inertial reference coordinate system according to camera external parameters and unmanned aerial vehicle attitude information to obtain normalized ray direction vectors corresponding to target pixels; an earth surface height constraint is constructed based on a digital elevation map, and an intersection point of a ray and the ground is determined by performing iterative approximation on a space point along a ray direction. According to the invention, rapid and accurate conversion from the single-frame unmanned aerial vehicle image pixel to the geographic coordinate can be realized.

The invention discloses a digital surface model (DSM)-guided hyperspectral and LiDAR combined unmixing method, relates to the field of multi-modal image processing, and aims to solve the problems of end member confusion and insufficient space structure maintenance caused by spectrum similarity in hyperspectral unmixing. According to the method, a hyperspectral image and LiDAR data of the same area are obtained, a LiDAR elevation map is expanded into a multiband profile through an attribute configuration file method, and a digital surface model (DSM) is generated. Constructing a spectrum and space double-branch auto-encoder, respectively extracting spectrum and space features and carrying out fusion mapping, obtaining an abundance matrix by using a normalized exponential function, and reconstructing a hyperspectral image; a double-branch adaptive mixed channel attention mechanism is designed in a spectrum branch, and a space attention mechanism is introduced in a space branch. In the training process, a self-defined loss function is formed by combining DSM-guided structure entropy regularization, spectral angular distance and root-mean-square error, and the space continuity and boundary retention of the abundance graph are improved. The method is suitable for hyperspectral unmixing and multi-modal remote sensing fine identification under complex terrains.

The invention relates to a training method of a perception and control model, and a robot control method and device. The method comprises the following steps: acquiring a plurality of sample image sets; wherein each sample image set comprises a depth image and an elevation image which represent the same terrain; performing multi-stage training on the initial model according to the plurality of sample image sets to obtain a sensing and control model for controlling the action of the robot; wherein the multi-stage training comprises the following steps of: training an elevation image codec by utilizing an elevation image to obtain a trained elevation image codec; training a depth image codec by using the depth image and the trained elevation image codec to obtain a trained depth image codec; and performing reinforcement learning training on the action strategy neural network by using the trained depth image codec, thereby facilitating the improvement of the model training efficiency through the interaction of perception and control, helping the robot to accurately perceive the working environment, and realizing accurate action control.

The application discloses a kind of single frame unmanned plane image pixel positioning method and system based on elevation map, it is related to unmanned plane image processing and geographic positioning technical field, obtain the single frame original image of unmanned plane shooting and the digital elevation map of the shooting area when unmanned plane attitude information, camera internal and external parameters, shooting;According to camera internal parameter, the pixel coordinates of target pixel in original image are converted to camera normalization plane coordinate system, and target pixel back projection point coordinates are obtained after coordinate distortion correction;According to camera external parameter and unmanned plane attitude information, camera-unmanned plane body coordinate system, body-geographic inertial reference coordinate system conversion is carried out, and the normalized ray direction vector corresponding to target pixel is obtained;Based on digital elevation map, construct ground height constraint, by iterative approximation to space point along ray direction, determine the intersection of ray and ground.The application can realize the fast and accurate conversion of single frame unmanned plane image pixel to geographic coordinate.

This invention relates to a method for constructing an elevation map, belonging to the field of autonomous navigation technology, and solves the problem of driving safety of existing unmanned tracked vehicles in complex environments. The method includes: determining the vehicle's first pose information using point cloud data; determining the vehicle's second pose information using inertial navigation data; extracting a global descriptor from the point cloud data and performing loop closure detection to obtain the vehicle's third pose information; constructing and solving a factor graph with the first, second, and third pose information as observation factors to obtain the vehicle's pose information; stitching the point cloud data based on the pose information to obtain a point cloud map; clustering the point clouds in the point cloud map and constructing an elevation map based on the clustering results.

Embodiments of the present disclosure disclose an airport abnormal area identification method and device, electronic equipment and readable medium. A specific embodiment of the method comprises: selecting reference area data and contrast area data; performing registration correction on the reference area remote sensing image and the contrast area remote sensing image to generate a corrected reference area image and a corrected contrast area image; performing area shadow detection on the corrected reference area image and the corrected contrast area image; performing constraint feature fusion on the corrected reference area image and the corrected contrast area image respectively to generate reference area main features and contrast area main features; performing abnormal area identification on the reference area main features and the contrast area main features; and performing verification grading on the candidate abnormal area identification information to obtain airport target area abnormal information. The embodiment can perform shadow detection on the airport area to identify abnormal shadow areas, and perform verification on the abnormal areas through a grid elevation map, thereby improving the safety of the airport area clearance.

The application discloses a building height detection method and device, a storage medium and an electronic device, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring a target remote sensing image corresponding to a target building, wherein the target building is a building to be detected in height; inputting the target remote sensing image into a target detection model for detection processing, and outputting height information of the target building, wherein the target detection model is a model generated by learning and training a target neural network model by using M remote sensing images, M shadow maps and M target elevation maps, the target neural network model is a neural network model with residual connection, the remote sensing image is a remote sensing image corresponding to a building, the shadow map is a shadow map of the building, the target elevation map is used for representing height information of the building, and M is a positive integer. Through the application, the problem that the effect of detecting the height of a building is poor in the related art is solved.

The invention discloses an elevation map construction method of a quadruped robot, and relates to the technical field of elevation map construction. Complete data coverage is ensured during data collection, real-time data quality monitoring is carried out, the accuracy of data collection can be ensured, the accuracy of data collection can be further improved by carrying out processing, classification, calibration and target segmentation on data, and therefore the accuracy of a map can be ensured; map construction is achieved based on the quadruped robot, the working efficiency can be improved, the intelligent degree is high, a three-dimensional map and a two-dimensional map can be generated, and therefore the applicability can be improved, in addition, through the arranged historical database and updating unit, the accuracy of the map can be guaranteed, the timeliness of the map can be prolonged, and the quality of the map can be guaranteed.

The invention relates to the technical field of material transfer, and discloses an unmanned automatic stacking and transferring method and system for bulk materials, and the method comprises the steps: collecting the three-dimensional point cloud data of a bulk material storage yard, carrying out the splicing of the three-dimensional point cloud data, obtaining the overall point cloud of the storage yard, and building an elevation map according to the overall point cloud of the storage yard; collecting body attitude data of the bulk cargo stacking transfer truck, planning a material taking motion path on the basis of the elevation map according to the body attitude data, updating the three-dimensional point cloud data in real time, and correcting the material taking motion path; and communicating with rear-end transfer equipment, performing cooperative control on a material taking motion path, dynamically calculating the quantity of remaining materials in a storage yard according to the three-dimensional point cloud data updated in real time, and completing unmanned automatic stacking, taking and transferring of the bulk materials. Through unmanned and intelligent control, efficient, safe and stable operation of the bucket-wheel stacker-reclaimer is achieved, manual intervention is reduced, the equipment operation efficiency is improved, and the intelligent operation and maintenance requirements of a large bulk material storage yard are met.

The embodiment of the invention provides a movement control method for a foot type robot. The movement control method is used for controlling the foot type robot to stably and accurately move in diversified terrains. The foot-type robot is loaded with a depth camera and an inertial measurement unit IMU, and the movement control method comprises the steps that in the walking process of the foot-type robot, point cloud data, IMU attitude data, remote control instruction data and motor state data collected by the depth camera are obtained; generating elevation map data based on the point cloud data and the IMU attitude data; inputting the elevation map data, the remote control instruction data, the IMU attitude data and the motor state data into a pre-trained reinforcement learning model to generate a motor control instruction; the reinforcement learning model is obtained by configuring various terrain scenes in a simulation training environment and training by adopting a priority sampling strategy; according to the priority sampling strategy, the sampling frequencies of different learning action sequences are determined according to the difficulty priorities of the different learning action sequences; and the motor control instruction is issued to a motor control system of the foot type robot, and the foot type robot is driven to execute corresponding actions.