Inspection positioning method and system of intelligent bionic robot at grain wharf

Abstract

The invention relates to the technical field of polling and positioning, in particular to a polling and positioning method and system of an intelligent bionic robot at a grain wharf, the foot end contact angle is adjusted according to ground features and foot pressure values, the robot can adjust and optimize the contact angle and posture according to real-time ground feedback, and the polling and positioning accuracy is improved. By measuring the grounding pressure and the slippage adjustment value of the feet in real time, the robot can be dynamically adjusted according to the actual situation of each step, slippage caused by ground friction force changes or obstacles is reduced, and the stability of the robot is improved by obtaining and analyzing multiple angular accelerations and linear accelerations in real time and combining with local movement tracks. According to the method, more accurate motion state information can be provided for the robot, a corresponding local optimization map can be formed by obtaining the three-dimensional environment image in each local motion track, the robot can flexibly adjust the own motion path and the foot end contact angle according to the optimization maps, the inspection path is further optimized, and the inspection efficiency is improved. And the overall inspection efficiency and safety are improved.

Description

Technical Field

[0001] The present invention relates to the field of patrol inspection and positioning technology, and in particular to a patrol inspection and positioning method and system for an intelligent bionic robot at a grain terminal. Background Art

[0002] Bionic robots are a type of robot that simulates the movement mechanism of organisms. They improve their adaptability and efficiency in complex environments by imitating the movement structure and behavior of animals or humans in nature. Bionic robots usually have flexible movement methods, such as imitating the gait of quadrupeds, or imitating the movement of human arms and legs, which makes them highly adaptable in complex terrain and dynamic environments. Grain terminals are complex and challenging working environments. Their ground is usually composed of piled grain, cargo, sand, slippery cement, steel or other industrial facilities. The terrain is uneven and may be affected by different weather conditions. To ensure production efficiency and safety, grain terminals usually require regular inspections and maintenance to check grain stacking, equipment operation, environmental sanitation, etc.

[0003] At present, during the inspection process, bionic robots usually optimize the trajectory by calculating the error between the current position and the target position. However, grain terminals usually have obstacles such as uneven ground, scattered objects, and stacked goods. These factors cause the ground to be ups and downs, and there may be uneven friction on the surface. The existing bionic robot inspection positioning technology lacks adjustment of the foot contact angle, which causes the robot to be unable to adapt to changes in the ground in time when facing irregular terrain, thereby causing positioning errors, which in turn causes the bionic robot to miss certain areas that need to be inspected, thus affecting the efficiency and quality of the inspection. Summary of the invention

[0004] The main purpose of the present invention is to provide a method for patrolling and positioning an intelligent bionic robot at a grain terminal, aiming to solve the technical problems in the prior art.

[0005] The present invention proposes a method for positioning an intelligent bionic robot at a grain terminal, comprising: Acquire ground features of multiple preset sections of the grain terminal and multiple initial foot end joint angles of the bionic robot, and adjust each initial foot end joint angle of the bionic robot according to each of the initial foot end joint angles and each ground feature; Obtaining the position characteristics and multiple foot bottoming pressure values ​​of the bionic robot after each initial foot end joint angle adjustment, and obtaining corresponding slip adjustment values ​​according to the position characteristics and multiple foot bottoming pressure values; Adjusting the foot contact point of the bionic robot according to each of the slip adjustment values, and acquiring in real time multiple angular accelerations and linear accelerations of the bionic robot in each preset road section during the adjustment process; Generate a local motion trajectory of the bionic robot in each preset section according to multiple angular accelerations and linear accelerations, and obtain a three-dimensional environmental image of the grain terminal at multiple consecutive moments in each local motion trajectory; Acquire a corresponding local environment map according to the plurality of three-dimensional environment images, and acquire a corresponding local optimization map according to each of the local environment maps; A corresponding total point cloud data set is obtained according to each of the local motion trajectories, and a three-dimensional positioning inspection model is generated according to multiple total point cloud data sets and local optimization maps, so that the bionic robot performs inspection and positioning according to the three-dimensional positioning inspection model.

[0006] Preferably, the step of adjusting each initial foot end joint angle of the bionic robot according to each initial foot end joint angle and each ground feature comprises: Acquire height data of a plurality of adjacent points according to each of the ground features, and acquire a corresponding ground normal vector according to the plurality of height data; Acquire a corresponding ground horizontal slope and a ground vertical slope according to each of the ground normal vectors, and acquire a corresponding ground slope angle according to each of the ground horizontal slope and the ground vertical slope; Acquire a foot contact plane normal vector corresponding to the bionic robot according to each of the initial foot joint angles, and acquire a corresponding initial foot contact angle according to each of the foot contact plane normal vectors and the ground normal vector; Determining whether each of the initial foot-end contact angles is different from a ground slope angle; If the initial foot contact angle is different from the ground slope angle, a corresponding error angle is obtained according to each initial foot contact angle and ground slope angle, and the initial foot joint angle of the bionic robot is adjusted according to each error angle.

[0007] Preferably, the step of obtaining the slip adjustment value according to the position feature and a plurality of foot bottom contact pressure values ​​comprises: Acquire the center of gravity position information and each foot end position information of the bionic robot according to the position feature, and acquire the corresponding foot weight vector according to the center of gravity position information and each foot end position information; Obtaining a torque vector sum according to the plurality of foot bottom contact pressure values ​​and foot weight vectors, and determining whether the torque vector sum is zero; If the sum of the torque vectors is zero, it is determined that the bionic robot has not slipped; If the moment vector sum is not zero, it is determined that the bionic robot has slipped, and the reference force, friction coefficient, real-time foot end contact angle and motion characteristics of the bionic robot at this time are obtained, wherein the motion characteristics include real-time speed, real-time acceleration, total mass and gravitational acceleration; Acquire the weight of the bionic robot according to the total mass and the gravitational acceleration, and acquire the vertical normal force according to the weight of the bionic robot and the real-time foot end contact angle; Acquire the inertial force according to the total mass and the real-time acceleration, and acquire the horizontal normal force according to the inertial force and the real-time foot end contact angle; Obtaining a total normal force according to the horizontal normal force and the vertical normal force, and obtaining a friction force according to the total normal force and a friction coefficient; A slip adjustment factor is obtained according to the reference force and the friction force, and a slip adjustment value is obtained according to the slip adjustment factor, the real-time speed and the real-time acceleration.

[0008] Preferably, the step of generating a local motion trajectory according to a plurality of angular accelerations and linear accelerations comprises: Acquiring a sampling frequency and initial features of the bionic robot, wherein the initial features include initial position information, initial linear velocity, initial angular velocity, and initial angle; Acquire a time step according to the sampling frequency, and acquire a corresponding real-time updated linear velocity according to the time step, the initial linear velocity and each linear acceleration; Obtaining a real-time updated position according to each of the real-time updated linear velocities, initial positions, linear accelerations and time steps; Obtaining a corresponding real-time updated angular velocity according to the time step, the initial angular velocity and each angular acceleration; Acquire a real-time updated angle according to each of the real-time updated angular velocity, angular acceleration, initial angle and time step; With time as the X-axis, angle as the Y-axis, and position as the Z-axis, a three-dimensional coordinate axis of time-angle-position is established, and the initial position information and initial angular velocity are plotted on the three-dimensional coordinate axis of time-angle-position as the starting point; The real-time update angle and real-time update position corresponding to each time step are plotted as connection points on the three-dimensional coordinate axis of time-angle-position; The starting point and multiple connection points are sequentially connected through curves to obtain a local motion trajectory.

[0009] Preferably, the step of acquiring a local environment map according to the plurality of three-dimensional environment images, and acquiring a local optimized map according to the local environment map comprises: Dividing each of the three-dimensional environment images into a plurality of local windows, and acquiring a plurality of pixel points according to each of the local windows; Obtaining the horizontal gradient and the vertical gradient of each pixel point, and obtaining the sum of squares of the horizontal gradients according to the multiple horizontal gradients; Obtaining a vertical gradient square sum according to the plurality of vertical gradients, and obtaining a gradient product sum according to the plurality of vertical gradients and horizontal gradients; Generate an autocorrelation matrix according to the gradient product sum, the horizontal gradient square sum, and the vertical gradient square sum, and obtain a determinant value and a trace value according to the autocorrelation matrix; Obtaining a response function value according to the determinant value and the trace value, and determining whether the response function value is greater than a preset threshold; If the response function value is greater than a preset threshold, the pixel point is determined to be a corner point; Acquire corresponding matching points according to the plurality of corner points, and construct a local environment map according to the plurality of matching points using a SLAM algorithm; Extracting feature points corresponding to each corner point according to the local environment map, and obtaining a reprojection error according to a plurality of the feature points and corner points; The feature points in the local environment map are optimized and adjusted according to the reprojection error to obtain a local optimized map.

[0010] Preferably, the step of acquiring a corresponding total point cloud data set according to each of the local motion trajectories, and generating a three-dimensional positioning inspection model according to a plurality of the total point cloud data sets and the local optimized map comprises: According to each of the local motion trajectories, the point cloud data sets to be registered at multiple preset positions of the grain terminal are collected in real time, and the point cloud data set to be registered at the central position is selected as the target point cloud data set; For each point to be registered in each of the point cloud data sets to be registered, the nearest neighbor matching point is obtained from the target point cloud data set by using the Euclidean distance measurement method; Obtaining a distance difference between each of the nearest neighbor matching points and the point to be registered, and adjusting the position of each of the point to be registered in each of the point cloud data sets to be registered according to the distance difference to obtain a corresponding registration point cloud data set; Each registered point cloud data set is converted into a global coordinate system and duplicate registration points are deleted to obtain a total point cloud data set; The PSR surface reconstruction algorithm is used to convert each total point cloud data set and the corresponding local optimization map into a local three-dimensional inspection model; The edge overlap points of each two adjacent local three-dimensional inspection models are obtained, and multiple local three-dimensional inspection models are combined and spliced ​​according to each of the edge overlap points to obtain a three-dimensional positioning inspection model.

[0011] The present application also provides an intelligent bionic robot inspection and positioning system at a grain terminal, comprising: The first adjustment module is used to obtain ground features of multiple preset sections of the grain terminal and multiple initial foot end joint angles of the bionic robot, and adjust each initial foot end joint angle of the bionic robot according to each of the initial foot end joint angles and each ground feature; A first acquisition module is used to acquire the position characteristics and multiple foot bottoming pressure values ​​of the bionic robot after each initial foot end joint angle adjustment, and acquire corresponding slip adjustment values ​​according to the position characteristics and multiple foot bottoming pressure values; A second acquisition module is used to adjust the foot contact point of the bionic robot according to each of the slip adjustment values, and to acquire in real time multiple angular accelerations and linear accelerations of the bionic robot in each preset road section during the adjustment process; A first generating module is used to generate a local motion trajectory of the bionic robot in each preset section according to a plurality of angular accelerations and linear accelerations, and obtain a three-dimensional environmental image of the grain terminal at a plurality of consecutive moments in each local motion trajectory; A third acquisition module, used to acquire a corresponding local environment map according to the plurality of the three-dimensional environment images, and to acquire a corresponding local optimization map according to each of the local environment maps; The second generation module is used to obtain the corresponding total point cloud data set according to each of the local motion trajectories, and generate a three-dimensional positioning inspection model according to multiple total point cloud data sets and local optimization maps, so that the bionic robot can perform inspection and positioning according to the three-dimensional positioning inspection model.

[0012] Preferably, the first adjustment module includes: A first acquisition unit, configured to acquire height data of a plurality of adjacent points according to each of the ground features, and acquire a corresponding ground normal vector according to the plurality of height data; A second acquisition unit, configured to acquire a corresponding ground horizontal slope and a ground vertical slope according to each of the ground normal vectors, and to acquire a corresponding ground slope angle according to each of the ground horizontal slope and the ground vertical slope; A third acquisition unit is used to acquire a foot end contact plane normal vector corresponding to the bionic robot according to each of the initial foot end joint angles, and to acquire a corresponding initial foot end ground contact angle according to each of the foot end contact plane normal vectors and the ground normal vector; A judging unit, used for judging whether each of the initial foot end contact angles is different from a ground slope angle; If the initial foot-end contact angle is different from the ground slope angle, obtaining a corresponding error angle according to each of the initial foot-end contact angle and the ground slope angle; The adjusting unit is used to adjust the initial foot end joint angle of the bionic robot according to each of the error angles.

[0013] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned intelligent bionic robot inspection and positioning method at a grain terminal are implemented.

[0014] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned intelligent bionic robot inspection and positioning method at a grain terminal are implemented.

[0015] The beneficial effects of the present invention are as follows: by dynamically adjusting each initial foot joint angle, the bionic robot can better adapt to these irregular terrains; by adjusting the foot contact angle according to ground features and foot pressure values, the robot can make adjustments based on real-time ground feedback to optimize the contact angle and posture; by real-time measurement of the bottom contact pressure and slip adjustment value of the foot, the robot can make dynamic adjustments based on the actual situation of each step to reduce slippage caused by changes in ground friction or obstacles, thereby effectively improving positioning accuracy and stability; by real-time acquisition and analysis of multiple angular accelerations and linear accelerations, combined with the local motion trajectory of the bionic robot in each preset section, the robot can be provided with more precise motion. By acquiring three-dimensional environmental images in each local motion trajectory, a corresponding local optimized map can be formed. The robot can flexibly adjust its own motion path and foot contact angle according to these optimized maps. By acquiring three-dimensional environmental images at multiple consecutive moments and generating corresponding environmental maps based on these images, the changes in the surrounding environment can be dynamically updated. This enables the robot to make adjustments based on the latest environmental data, optimize positioning and inspection paths, and obtain high-precision three-dimensional positioning and inspection models by integrating multiple total point cloud data sets. These data not only help the bionic robot to achieve efficient positioning, but also further provide high-quality map information, thereby optimizing the inspection path and improving the overall inspection efficiency and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The figure is a schematic diagram of a method flow according to an embodiment of the present invention.

[0017] Figure 2 FIG. 1 is a schematic diagram of a system structure according to an embodiment of the present invention.

[0018] Figure 3 A schematic diagram of the internal structure of a computer device according to an embodiment of the present application.

[0019] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0020] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0021] like Figure 1-Figure 3 As shown, the present application provides a method for an intelligent bionic robot to inspect and locate at a grain terminal, comprising: S1, obtaining ground features of multiple preset road sections of the grain terminal and multiple initial foot end joint angles of the bionic robot, and adjusting each initial foot end joint angle of the bionic robot according to each of the initial foot end joint angles and each ground feature; S2, obtaining the position characteristics and multiple foot bottoming pressure values ​​of the bionic robot after each initial foot end joint angle adjustment, and obtaining corresponding slip adjustment values ​​according to the position characteristics and multiple foot bottoming pressure values; S3, adjusting the foot contact point of the bionic robot according to each of the slip adjustment values, and acquiring in real time multiple angular accelerations and linear accelerations of the bionic robot in each preset road section during the adjustment process; S4, generating a local motion trajectory of the bionic robot in each preset section according to multiple angular accelerations and linear accelerations, and obtaining a three-dimensional environmental image of the grain terminal at multiple consecutive moments in each local motion trajectory; S5, acquiring corresponding local environment maps according to the plurality of the three-dimensional environment images, and acquiring corresponding local optimization maps according to each of the local environment maps; S6. Obtain a corresponding total point cloud data set according to each of the local motion trajectories, and generate a three-dimensional positioning inspection model according to multiple total point cloud data sets and local optimization maps, so that the bionic robot performs inspection and positioning according to the three-dimensional positioning inspection model.

[0022] As described in the above steps S1-S6, currently, during the inspection process, the bionic robot usually optimizes the trajectory by calculating the error between the current position and the target position. However, grain terminals usually have obstacles such as uneven ground, scattered objects, and stacked goods. These factors cause the ground to be uneven and there may be uneven friction on the surface. The existing bionic robot inspection positioning technology lacks adjustment of the foot contact angle, which causes the robot to be unable to adapt to changes in the ground in time when facing irregular terrain, thereby causing positioning errors, which in turn causes the bionic robot to miss certain areas that need to be inspected, thereby affecting the efficiency and quality of the inspection. The present invention adjusts each initial foot-end joint angle of the bionic robot through the ground features of multiple preset sections of the grain terminal and multiple initial foot-end joint angles of the bionic robot, and obtains the corresponding slip adjustment value through the position features of the bionic robot after each initial foot-end joint angle adjustment and multiple foot bottoming pressure values, and adjusts the foot contact point of the bionic robot according to each slip adjustment value, wherein the foot contact point refers to the position where the foot of the bionic robot contacts the ground, and the initial foot-end joint angle refers to the initial joint angle configuration of the robot foot (similar to the foot of a biological robot), that is, in the machine Before the robot starts walking or working, the preset angles of the joints of the foot (such as toes, ankles, etc.) are set. The foot bottoming pressure value refers to the pressure when the sole of each foot of the robot contacts the ground. The slip adjustment value refers to the amount of slip that needs to be adjusted due to factors such as uneven ground and changes in friction during the process of the robot's foot contacting the ground. Since grain terminals usually have uneven ground, scattered objects, stacked goods and other obstacles, the ground is ups and downs. These complex ground features may cause existing inspection robots to be unable to accurately track the target position, resulting in large errors.By dynamically adjusting each initial foot joint angle, the bionic robot can better adapt to these irregular terrains, thereby reducing errors caused by terrain changes. By adjusting the foot contact angle according to ground features and foot pressure values, the robot can adjust according to real-time ground feedback, optimize the contact angle and posture, thereby improving stability and accuracy in complex environments. By real-time measurement of the foot's bottoming pressure and slip adjustment value, the robot can dynamically adjust according to the actual situation of each step. This mechanism enables the robot to adapt to different ground conditions and reduce slippage caused by changes in ground friction or obstacles, thereby effectively improving positioning accuracy and stability. By adjusting the foot contact angle and combining bottoming pressure, the robot can adjust the bottoming pressure and slip adjustment value of the foot. The robot can adjust the slip according to the pressure and position characteristics, and can accurately capture the impact of ground changes on the robot, thereby improving the positioning accuracy. It can adapt to environmental changes by adjusting the ground characteristics and the foot end angle, rather than relying on too many complex calculations, thereby simplifying the control logic of the robot system and improving the response speed and adaptability of the system. Through the adjustment of the foot end angle and pressure, the robot can better distribute the weight, optimize the way the foot contacts the ground, reduce the risk of slippage and tilt, and thus enhance the stability and passability of the robot. The present invention obtains multiple angular accelerations and linear accelerations of the bionic robot in each preset section during the adjustment process in real time, and generates a bionic robot according to the multiple angular accelerations and linear accelerations. In each preset section of the local motion trajectory, the three-dimensional environment images of the grain terminal at multiple continuous moments in each local motion trajectory are obtained, the corresponding local environment map is obtained through the multiple three-dimensional environment images, and the corresponding local optimization map is obtained according to each local environment map, and then the corresponding total point cloud data set is obtained through each of the local motion trajectories, and a three-dimensional positioning inspection model is generated according to the multiple total point cloud data sets and the local optimization map, so that the bionic robot performs inspection and positioning according to the three-dimensional positioning inspection model, wherein the point cloud data set is a set of spatial points collected by laser radar or other sensors, each point contains three-dimensional coordinates, represents the surface of an object in the environment, and obtains and analyzes multiple angular accelerations and linear The acceleration, combined with the local motion trajectory of the bionic robot in each preset section, can provide the robot with more accurate motion state information, which enables the robot to better adapt to different terrains and environmental changes, thereby reducing positioning errors caused by factors such as uneven ground or uneven friction. By acquiring three-dimensional environmental images in each local motion trajectory, a corresponding local optimization map can be formed. The robot can flexibly adjust its own motion path and foot contact angle according to these optimization maps, thereby effectively coping with irregular terrain, reducing errors and ensuring the completion of inspection tasks. By acquiring three-dimensional environmental images at multiple consecutive moments and generating corresponding environmental maps based on these images, changes in the surrounding environment can be dynamically updated.For example, factors such as cargo stacking and object movement will affect ground conditions. Real-time three-dimensional environmental images can reflect these changes and generate more accurate local optimized maps, which enables the robot to adjust based on the latest environmental data, optimize positioning and inspection paths, and obtain high-precision three-dimensional positioning and inspection models by integrating multiple total point cloud data sets. These data not only help the bionic robot to achieve efficient positioning, but also further provide high-quality map information, thereby optimizing the inspection path, so that the robot can more intelligently identify and avoid obstacles, thereby avoiding collisions or stagnation, and improving overall inspection efficiency and safety. A three-dimensional positioning inspection model is formed. The robot can automatically optimize its own motion path during driving. The bionic robot can adjust the foot contact angle and other motion parameters based on real-time feedback to adapt to irregular terrain and complex environment, thereby improving the inspection efficiency and the robot's adaptability in different terrains. Therefore, through the comprehensive use of angular acceleration, linear acceleration, three-dimensional environmental images, point cloud data and local optimization maps, the problem of positioning error of the bionic robot in irregular terrain is effectively overcome, and the robot's adaptability, accuracy and autonomy in complex environments are improved, providing a more reliable and efficient solution for inspection tasks in complex environments such as grain terminals.

[0023] In one embodiment, the step S1 of adjusting each initial foot end joint angle of the bionic robot according to each initial foot end joint angle and each ground feature comprises: S11, acquiring height data of a plurality of adjacent points according to each of the ground features, and acquiring a corresponding ground normal vector according to the plurality of height data; S12, acquiring a corresponding ground horizontal slope and a ground vertical slope according to each of the ground normal vectors, and acquiring a corresponding ground slope angle according to each of the ground horizontal slope and the ground vertical slope; S13, obtaining a foot contact plane normal vector corresponding to the bionic robot according to each of the initial foot joint angles, and obtaining a corresponding initial foot contact angle according to each of the foot contact plane normal vectors and the ground normal vector; S14, determining whether each of the initial foot end contact angles is different from the ground slope angle; If the initial foot-end contact angle is different from the ground slope angle, obtaining a corresponding error angle according to each of the initial foot-end contact angle and the ground slope angle; S15. Adjusting the initial foot joint angle of the bionic robot according to each of the error angles.

[0024] As described in the above steps S11-S15, the slope angle refers to the inclination angle of the ground relative to the horizontal plane or the reference plane, the ground normal vector refers to the direction perpendicular to the ground, indicating the direction in which the ground is inclined, the foot end contact plane normal vector refers to the normal direction vector of the foot contact plane when the robot foot end contacts the ground, and the initial foot end ground contact angle refers to the initial angle of the robot foot end when it contacts the ground. The present invention obtains the ground normal vector through the height data of multiple adjacent points of the ground feature, obtains the horizontal slope of the ground and the vertical slope of the ground according to the ground normal vector, and obtains the ground slope angle according to the horizontal slope of the ground and the vertical slope of the ground. By obtaining the height data of multiple adjacent points and calculating the ground normal vector, the inclination and direction of the ground can be accurately described, which can help the machine The robot accurately understands the slope characteristics of the ground. By adjusting the contact angle of the foot according to the slope angle of the ground, the robot can better adapt to complex terrain and avoid slippage and unstable walking problems. Bionic robots often face slippage problems caused by inaccurate contact angles of the foot in complex ground environments. By accurately calculating the slope and normal vector of the ground, accurate feedback of ground features can be provided to help adjust the contact angle of the robot's foot, thereby reducing slippage. By obtaining the slope angle of the ground and adjusting the contact angle of the foot accordingly, the robot can maintain good contact in complex terrain and reduce walking instability caused by uneven ground. Traditional positioning methods may fail to effectively combine ground features with visual data, resulting in positioning errors and unstable trajectories.By combining the calculation of the ground slope angle with the visual positioning data, the robot can model the environment more accurately, so that it can accurately navigate and locate in complex environments such as grain terminals. The combined motion trajectory can ensure that the robot maintains a stable route in a dynamically changing environment. The calculation of the ground slope angle and normal vector provides the robot with more accurate environmental data, which can help optimize the positioning process during the dynamic update of the environmental map. By combining these ground features with the visual positioning information, the trajectory calculation error caused by complex environment or sensor error in traditional methods can be better reduced. By introducing ground slope information in the global positioning process, the robot can more accurately identify and adapt to environmental changes under different ground conditions, which can reduce the robot Trajectory drift or error caused by uneven ground is improved to improve the stability of the motion trajectory. The foot contact plane normal vector of the bionic robot is obtained according to the initial foot joint angle, and the initial foot grounding angle is obtained according to the foot contact plane normal vector and the ground normal vector, and it is judged whether the initial foot grounding angle is different from the ground slope angle. If the initial foot grounding angle is different from the ground slope angle, the error angle is obtained according to the initial foot grounding angle and the ground slope angle, and the PID control algorithm is used to adjust the initial joint angle of the bionic robot according to the error angle until the initial foot grounding angle is the same as the ground slope angle. The foot contact angle of the bionic robot may not be accurately adjusted in different ground environments, especially in complex terrains, resulting in poor stability and insufficient adaptability of the robot. By obtaining the normal vector of the foot contact plane based on the initial foot joint angle, and then adjusting the joint angle in combination with the PID control algorithm, it is possible to ensure that the angle at which the foot contacts the ground accurately matches the ground slope angle, thereby improving the robot's stability and adaptability in complex terrain. By adjusting the foot contact angle in real time to match the ground slope angle, the bionic robot can better adapt to various uneven surfaces, avoid problems such as slippage and instability, and increase its mobility in complex environments. Using the PID control algorithm to provide real-time feedback and adjustment of the ground angle error can make the robot adaptive in a dynamic environment.For example, when the robot is walking, if the ground changes, the PID algorithm can automatically adjust the ground contact angle of the foot, so that the robot's motion trajectory is more stable and avoid error accumulation caused by contact angle mismatch. The present invention can combine visual positioning data with the robot's motion trajectory for global positioning. The visual system can help the robot detect ground slope and obstacles, while the motion trajectory data helps analyze the robot's actual moving state. Dynamic adjustment based on these data can improve positioning accuracy, reduce positioning errors, and enhance the robot's global motion control capabilities. By optimizing the foot ground contact angle, the robot's motion trajectory in complex environments will be more precise and reduce the occurrence of errors. The stable contact angle reduces the possibility of the robot tilting or being unstable, thereby ensuring that the robot's posture is more stable during walking and effectively reducing the risk of falling or sliding.

[0025] In one embodiment, the step S2 of acquiring the slip adjustment value according to the position feature and a plurality of foot bottom contact pressure values ​​comprises: S21, acquiring the center of gravity position information and each foot end position information of the bionic robot according to the position feature, and acquiring the corresponding foot weight vector according to the center of gravity position information and each foot end position information; S22, obtaining a torque vector sum according to the plurality of foot bottom contact pressure values ​​and foot weight vectors, and determining whether the torque vector sum is zero; If the sum of the torque vectors is zero, it is determined that the bionic robot has not slipped; If the moment vector sum is not zero, it is determined that the bionic robot has slipped, and the reference force, friction coefficient, real-time foot end contact angle and motion characteristics of the bionic robot at this time are obtained, wherein the motion characteristics include real-time speed, real-time acceleration, total mass and gravitational acceleration; S23, obtaining the weight of the bionic robot according to the total mass and the gravitational acceleration, and obtaining the vertical normal force according to the weight of the bionic robot and the real-time foot end contact angle; S24, obtaining an inertial force according to the total mass and the real-time acceleration, and obtaining a horizontal normal force according to the inertial force and the real-time foot end contact angle; S25. Obtaining a total normal force according to the horizontal normal force and the vertical normal force, and obtaining a friction force according to the total normal force and a friction coefficient; S26: Obtain a slip adjustment factor according to the reference force and the friction force, and obtain a slip adjustment value according to the slip adjustment factor, the real-time speed, and the real-time acceleration.

[0026] As described in the above steps S21-S26, slip generally refers to the relative movement between the robot's feet and the ground, which is usually manifested as one or more supporting feet failing to fully contact the ground, or uneven distribution of contact force resulting in insufficient ground friction, thereby causing slip. The foot weight vector refers to the vector between the robot's foot end and the center of gravity. The present invention obtains the corresponding foot weight vector through the bionic robot's center of gravity position information and each foot end position information, obtains the torque vector sum according to multiple foot bottoming pressure values ​​and the foot weight vector, and determines whether the torque vector sum is zero. If the torque vector sum is not zero, it is determined that the bionic robot has slipped, and the bionic robot's reference force, friction coefficient, real-time foot end contact angle and motion characteristics are obtained at this time, wherein the motion characteristics include real-time speed, real-time acceleration, total mass and gravitational acceleration. The bionic robot may slip during the inspection process due to uneven ground, changes in friction or the presence of obstacles. By acquiring the center of gravity and the position information of each foot end and the bottom contact pressure in real time, combined with the method of calculating the torque vector sum, it is possible to accurately detect whether the robot has slipped. If the torque vector sum is not zero, the system can determine that slippage has occurred, so that adjustment measures can be taken in time to prevent the robot from further losing stability or positioning errors. By dynamically adjusting the slip value, the robot can better adapt to these irregular ground and objects, allowing it to operate more stably on uneven ground. By making real-time slip adjustments based on the pressure value and torque vector of each foot end, the robot can adjust its foot contact angle and position at each step, allowing the robot to quickly adapt to terrain changes and improve positioning accuracy. By real-time monitoring of the bionic robot's center of gravity, foot end position information and pressure value, the robot can accurately judge and respond to slippage, thereby improving the robot's adaptability, stability and positioning accuracy. In complex and irregular ground environments, the bionic robot can automatically adjust the foot end contact angle to avoid slippage, thereby achieving high efficiency in complex environments such as grain terminals. , stable inspection tasks, the weight of the bionic robot is obtained through the total mass and gravitational acceleration, and the vertical normal force is obtained according to the weight of the bionic robot and the real-time foot contact angle, the inertial force is obtained according to the total mass and real-time acceleration, and the horizontal normal force is obtained according to the inertial force and the real-time foot contact angle, the total normal force is obtained according to the horizontal normal force and the vertical normal force, and the friction force is obtained according to the total normal force and the friction coefficient, the slip adjustment factor is obtained through the reference force and the friction force, and the slip adjustment value is obtained according to the slip adjustment factor, the real-time speed and the real-time acceleration. By obtaining the foot contact angle in real time, the robot can adjust its gait to adapt to uneven and undulating ground. By adjusting the contact angle in real time, the robot can respond to changes in irregular terrain in a timely manner and avoid imbalance or deviation from the target path due to the inability to adapt to ground changes. By calculating the inertial force and the normal force, the robot can more accurately sense its contact state with the ground, and further accurately control the movement direction and trajectory, which can not only enhance the robot's adaptability to terrain changes,It can also reduce the positioning error caused by the inability to adjust the foot contact angle in time. By calculating the friction and slip adjustment factors, the robot can make adaptive adjustments according to the unevenness of ground friction, reduce the impact of slip on positioning, and can adjust the path in real time according to the robot's current position, foot contact angle, acceleration and speed information, so as to ensure that the robot can continuously optimize its trajectory according to real-time environmental changes. The robot can quickly respond to changes in ground conditions and adjust its walking method to ensure accurate arrival at the target position while avoiding stagnation or deviations caused by not adapting to ground changes. By real-time monitoring and adjusting the foot contact angle and friction, the robot can adapt to different ground conditions, thereby reducing the interference of the external environment on its movement. The above method dynamically adjusts the robot's motion state by integrating multiple factors (gravitational acceleration, contact angle, inertia, friction, etc.), significantly improving the adaptability of the bionic robot on complex and uneven ground. By optimizing the foot contact angle and reducing the impact of friction unevenness on positioning accuracy, the robot can perform inspection tasks more accurately and stably, avoiding positioning errors caused by uneven ground or obstacles, thereby improving inspection efficiency and work reliability.

[0027] In one embodiment, the step S3 of generating a local motion trajectory according to a plurality of angular accelerations and linear accelerations comprises: S31, obtaining a sampling frequency and initial features of the bionic robot, wherein the initial features include initial position information, initial linear velocity, initial angular velocity and initial angle; S32, obtaining a time step according to the sampling frequency, and calculating a corresponding real-time updated linear velocity according to the time step, the initial linear velocity and each linear acceleration, wherein the calculation formula is: ; Among them, S(XS) represents the real-time updated linear velocity, C(XS) represents the initial linear velocity, X(JS) represents the linear acceleration, and S(BC) represents the time step; S33, obtaining a real-time updated position according to each of the real-time updated linear velocities, initial positions, linear accelerations and time steps, wherein the calculation formula is: ; Among them, S(XW) represents the real-time updated position, S(XS) represents the real-time updated linear velocity, C(XW) represents the initial position, and S(BC) represents the time step; S34, acquiring a corresponding real-time updated angular velocity according to the time step, the initial angular velocity and each angular acceleration; S35, acquiring a real-time update angle according to each of the real-time update angular velocity, angular acceleration, initial angle and time step, wherein the calculation formula of the real-time update angle is the same as the calculation formula of the real-time update position; S36, with time as the X-axis, angle as the Y-axis, and position as the Z-axis, a three-dimensional coordinate axis of time-angle-position is established, and the initial position information and the initial angular velocity are plotted on the three-dimensional coordinate axis of time-angle-position as the starting point; S37, drawing the real-time update angle and the real-time update position corresponding to each time step as connection points on the time-angle-position three-dimensional coordinate axis; S38, connecting the starting point and multiple connection points in sequence through curves to obtain a local motion trajectory.

[0028] As described in the above steps S31-S38, the present invention obtains the sampling frequency, initial position information, initial linear velocity, initial angular velocity and initial angle of the bionic robot, and obtains the time step according to the sampling frequency, obtains the corresponding real-time updated linear velocity according to the time step, the initial linear velocity and each linear acceleration, obtains the real-time updated position according to each real-time updated linear velocity, the initial position, the linear acceleration and the time step, obtains the corresponding real-time updated angular velocity according to the time step, the initial angular velocity and each angular acceleration, obtains the real-time updated angle according to each real-time updated angular velocity, the angular acceleration, the initial angle and the time step, takes time as the X-axis, the angle as the Y-axis, and the position as the Z-axis, establishes a time-angle-position three-dimensional coordinate axis, and converts the initial position information The initial angular velocity is used as the starting point and drawn on the time-angle-position three-dimensional coordinate axis. The real-time updated angle and real-time updated position corresponding to each time step are drawn as connection points on the time-angle-position three-dimensional coordinate axis. The starting point and multiple connection points are connected in turn through curves to obtain the local motion trajectory. Bionic robots are usually in complex ground environments, where the ground may be uneven, there may be slippage or inconsistent friction. By updating the robot's linear velocity, angular velocity, position and angle in real time, the robot's motion state on uneven ground can be more accurately reflected, thereby effectively predicting and adjusting the robot's motion trajectory. By updating the robot's position and angle in real time according to the sampling frequency, time step, initial velocity and acceleration, not only It can effectively simulate the robot's motion state and improve the accuracy of trajectory calculation. Traditional positioning methods usually rely on single sensor data, which may have errors or lack sufficient flexibility. By combining visual positioning data and real-time updated motion trajectories, the accuracy and robustness of positioning can be improved. The robot can calculate a more accurate global position based on visual information and motion trajectories, thereby avoiding error accumulation caused by a single data source. The motion and foot contact angle of the bionic robot will have a greater impact on the stability of the robot. By updating the angle and position in real time, this method can adjust the robot's motion state in time to avoid slippage caused by inaccurate contact angles, thereby improving the robot's adaptability and stability. By establishing a three-dimensional coordinate axis of time-angle-position and dynamically drawing the motion trajectory, the robot can adjust its motion strategy in real time to cope with sudden ground changes or obstacles. This enables the robot to cope with complex environments more flexibly and reduce path deviations caused by environmental changes. Through precise real-time updates and time step calculations, the path drift caused by initial position errors or sensor noise can be effectively reduced. During long-term operation, the robot can maintain high positioning accuracy, reduce error accumulation, and ensure a more stable and accurate motion trajectory by continuously correcting the real-time data of angle, speed, and position. By realizing dynamic trajectory optimization and real-time feedback adjustment, the robot can autonomously cope with complex environments without external intervention.This ability to autonomously adjust the trajectory of motion makes the robot more intelligent and can complete tasks without human intervention, reducing the burden of manual operation.

[0029] In one embodiment, the step S5 of acquiring a local environment map according to the plurality of three-dimensional environment images, and acquiring a local optimized map according to the local environment map, comprises: S51, dividing each of the three-dimensional environment images into a plurality of local windows, and acquiring a plurality of pixel points according to each of the local windows; S52, obtaining a horizontal gradient and a vertical gradient of each pixel point, and obtaining a horizontal gradient square sum according to a plurality of the horizontal gradients; S53, obtaining a vertical gradient square sum according to the plurality of vertical gradients, and obtaining a gradient product sum according to the plurality of vertical gradients and horizontal gradients; S54, generating an autocorrelation matrix according to the gradient product sum, the horizontal gradient square sum, and the vertical gradient square sum, and obtaining a determinant value and a trace value according to the autocorrelation matrix; S55, obtaining a response function value according to the determinant value and the trace value, and determining whether the response function value is greater than a preset threshold; If the response function value is greater than a preset threshold, the pixel point is determined to be a corner point; S56, acquiring corresponding matching points according to the multiple corner points, and using a SLAM algorithm to construct a local environment map according to the multiple matching points; S57, extracting feature points corresponding to each corner point according to the local environment map, and obtaining a reprojection error according to the plurality of feature points and corner points; S58. Optimize and adjust the feature points in the local environment map according to the reprojection error to obtain a local optimized map.

[0030] As described in the above steps S51-S58, the present invention divides each three-dimensional environment image into multiple local windows, and obtains multiple pixel points according to each local window, obtains the sum of squares of horizontal gradients through the horizontal gradients of the multiple pixel points, obtains the sum of squares of vertical gradients through the vertical gradients of the multiple pixel points, and obtains the sum of gradient products according to the vertical gradients and horizontal gradients of the multiple pixel points, generates an autocorrelation matrix through the sum of gradient products, the sum of squares of horizontal gradients, and the sum of squares of vertical gradients, obtains determinant values ​​and trace values ​​according to the autocorrelation matrix, obtains response function values ​​according to the determinant values ​​and trace values, and determines whether the response function value is greater than a preset threshold value. If the response function value is greater than the preset threshold value, the pixel point is determined to be a corner point, wherein a corner point refers to a point in the point cloud data where the change in the local area is most obvious, usually representing the edge or feature point of an object, and having a higher curvature. The bionic robot needs to deal with complex environments such as uneven ground, scattered objects and obstacles during the inspection process, and traditional positioning methods may not be able to cope with these irregular terrains well. By dividing local windows in each three-dimensional environment image and calculating different gradients, the robot can more accurately perceive and adapt to changes in terrain. For example, the sum of the squares of horizontal and vertical gradients and the sum of gradient products can be used to better capture the undulations of the terrain, thereby improving adaptability to irregular changes in terrain. By calculating the determinant and trace values ​​of the autocorrelation matrix and judging the corner points based on the response function value, the robot can quickly identify key terrain features in a dynamic environment. Corner point detection can help the robot identify feature points on the ground (such as the edges and protrusions of obstacles), thereby optimizing path planning and obstacle avoidance strategies, and avoiding positioning deviations due to uneven ground or obstacles. By detecting and calculating the gradients of local images, the robot can identify areas with uneven friction, thereby adjusting the foot contact angle and gait, improving the robot's motion adaptability in complex environments, and reducing sliding errors caused by uneven friction. Through gradient calculation and response based on local windows Function determination, the bionic robot can continuously make real-time adjustments based on sensor data. This method can make the robot more adaptive when facing dynamic and complex terrain, respond to ground changes in a timely manner, and improve the stability and reliability of the robot during the inspection process. Using gradient information and response functions, the robot can accurately identify and optimize the error between the current and target positions, reduce the positioning error caused by irregular terrain, and thus achieve accurate trajectory optimization. By adjusting the path and positioning in real time, the error accumulation caused by ground undulations or obstacles can be effectively avoided. Among them, the trace of the autocorrelation matrix reflects the sum of the gradient changes in the local area of ​​the image in the horizontal and vertical directions. The determinant of the autocorrelation matrix represents the deformation ability of the matrix and is a measure of the intensity of the gradient change. The larger the determinant, the more complex the local changes in the image in this area, which may correspond to corners or edges. The response function is the key value to measure whether each pixel is a corner point. The response function formula is:, where R represents the response function, H(LS) represents the determinant value, and β represents the empirical constant, which is usually between 0.04 and 0.06, Z(JZ) represents the trace value. If the value of R is large, it usually means that the local area around the point has a strong change and has gradient information in multiple directions, which usually corresponds to the corner points of the image. The sum of gradient products reflects the relationship between the gradients of the image in two directions, which is often called gradient covariance. The sum of squared horizontal gradients and the sum of squared vertical gradients represent the intensity of change of the image in the horizontal and vertical directions, respectively, and are called gradient energy or the square of gradient amplitude. The present invention obtains corresponding matching points through multiple corner points, and uses the SLAM algorithm to construct a local environment map according to the multiple matching points, extracts feature points corresponding to each corner point according to the local environment map, and obtains reprojection errors according to the multiple feature points and the corner points. The reprojection error is used to optimize and adjust the feature points in the local environment map to obtain a local optimized map. The reprojection error refers to the error between the original point and the projection point when the three-dimensional point cloud data is projected onto a two-dimensional plane through the projection model of a camera or other sensor. Through corner point extraction and matching point calculation, the bionic robot can identify the key feature points in the environment. These feature points are used to construct a local environment map through the SLAM algorithm to help the robot identify and locate significant features in its surrounding environment (such as scattered objects, stacked goods and other obstacles). It can not only cope with complex ground conditions, but also improve the accuracy and reliability of the robot in complex environments. By calculating the reprojection error based on the feature points and corner points, and optimizing the adjustment It can accurately adjust the position of feature points in the local environment map, thereby reducing errors caused by terrain changes. The optimized local map can better adapt to irregular ground and improve positioning stability. Through the SLAM algorithm and corner point matching, the robot can obtain ground features in real time and dynamically adjust its path based on these features, which can help the robot better adapt to ground undulations and obstacles and avoid positioning errors caused by ground irregularities. The SLAM algorithm combined with corner point optimization and reprojection error correction can help the robot perceive the ground situation more accurately, and adjust the gait or foot contact angle in time according to the optimization information of the local map, thereby reducing the impact of uneven friction on the robot's movement. The SLAM algorithm The robot can update its local environment map in real time during the inspection process and adjust its path planning according to the new map. This dynamic update function enables the robot to respond to environmental changes in a timely manner, especially changes in ground friction and obstacles, and avoids the error accumulation caused by static path planning in traditional methods. SLAM and optimization algorithms obtain environmental feature points and error feedback in real time, which helps the robot adjust the foot contact angle and gait according to the real-time ground conditions. Through corner point matching, local map optimization and correction of reprojection errors, the robot can perform error correction in real time and reduce the error accumulation caused by irregular terrain or obstacles. This not only improves the accuracy of robot positioning, but also greatly improves the inspection efficiency. .

[0031] In one embodiment, the step S6 of acquiring a corresponding total point cloud data set according to each of the local motion trajectories, and generating a three-dimensional positioning inspection model according to a plurality of the total point cloud data sets and the local optimized map includes: S61, collecting point cloud data sets to be registered at multiple preset positions of the grain terminal in real time according to each of the local motion trajectories, and selecting the point cloud data set to be registered at the central position as the target point cloud data set; S62, using the Euclidean distance measurement method to obtain the nearest neighbor matching point from the target point cloud data set for each point to be registered in each of the point cloud data sets to be registered; S63, obtaining a distance difference according to each of the nearest neighbor matching points and the point to be registered, and adjusting the position of each of the point to be registered in each of the point cloud data sets to be registered according to the distance difference, to obtain a corresponding registration point cloud data set; S64, converting each registration point cloud data set into a global coordinate system and deleting duplicate registration points to obtain a total point cloud data set; S65, using the PSR surface reconstruction algorithm to convert each total point cloud data set and the corresponding local optimization map into a local three-dimensional inspection model; S66, obtaining edge overlap points of every two adjacent local three-dimensional inspection models, and combining and splicing multiple local three-dimensional inspection models according to each edge overlap point to obtain a three-dimensional positioning inspection model.

[0032] As described in the above steps S61-S66, the present invention collects the point cloud data sets to be registered at multiple preset positions of the grain terminal in real time through each local motion trajectory, and selects the point cloud data set to be registered at the central position as the target point cloud data set, and uses the Euclidean distance measurement method to obtain the nearest neighbor matching point from the target point cloud data set for each point to be registered in each point cloud data set to be registered, and obtains the distance difference according to each nearest neighbor matching point and the point to be registered, and adjusts the position of each point to be registered in each point cloud data set to be registered according to the distance difference to obtain the corresponding registration point cloud data set, wherein the nearest neighbor matching point refers to the point with the smallest distance to each point in the point cloud data set to be registered, which is found from the target point cloud data set through the Euclidean distance measurement. By matching each local point cloud data set with the target point cloud data set, the position of the robot in a complex environment can be updated and adjusted in real time. This point cloud registration method helps to accurately identify the difference between the current position of the robot and the target position, thereby improving the positioning accuracy. On an irregular ground such as a grain terminal, the robot can Through point cloud matching, the robot can quickly adapt to terrain changes and avoid the error accumulation that may occur in traditional methods. The nearest neighbor matching point is calculated using Euclidean distance, which can accurately capture the difference between the point cloud data set to be registered and the target point cloud. This accuracy helps the robot reduce the error caused by mismatching when dealing with obstacles such as scattered objects and stacked goods, thereby maintaining accurate positioning. Through point cloud registration technology, the position of these irregular terrains or obstacles can be identified in real time, and the path and posture of the robot can be adjusted. It can identify obstacles more accurately and avoid positioning errors caused by uneven ground or obstacles during inspection. Through positioning optimization based on point cloud registration, the robot can adjust its foot contact angle in real time to adapt to changes in different ground. For example, when the robot passes through uneven ground, it can adjust the contact angle and position of the foot according to the point cloud data to avoid sliding or unstable movement. This is especially important for bionic robots. Since there may be uneven friction on the ground of the grain terminal, traditional methods may find it difficult to cope with positioning errors caused by changes in friction. Through real-time point cloud registration and distance difference adjustment, the robot can dynamically adjust the foot contact angle, reduce the impact caused by uneven friction, and improve the robot's stability on irregular ground. Each time the point cloud data set is registered, the robot will adjust the point cloud according to the distance difference. This local optimization process helps the robot to continuously improve its understanding of the environment during the inspection process, thereby continuously improving its adaptive ability, especially when facing complex terrain or environmental changes. It can make timely adjustments to reduce positioning errors. Through local point cloud optimization and error adjustment, the robot can generate or update path planning in real time. Through real-time collection and registration of point cloud data for each local motion trajectory, errors can be adjusted and corrected in time to maintain high stability and reliability. Through accurate point cloud registration and dynamic adaptation, the robot can complete the inspection task more smoothly.Especially in a highly uncertain environment such as a grain terminal, it can ensure the efficient completion of the task. By converting each registered point cloud data set into a global coordinate system and deleting duplicate registration points, a total point cloud data set is obtained. The PSR surface reconstruction algorithm is used to convert each total point cloud data set and the corresponding local optimization map into a local three-dimensional inspection model. By obtaining the edge overlap points of each adjacent two local three-dimensional inspection models, and combining and splicing multiple local three-dimensional inspection models according to each edge overlap point, a three-dimensional positioning inspection model is obtained. By combining and splicing multiple local three-dimensional inspection models into a three-dimensional positioning inspection model, the terrain characteristics of the inspection area can be more comprehensively presented, which helps the robot to accurately locate and navigate in complex and rugged environments, especially in places such as grain terminals where there are uneven ground and obstacles. The detailed terrain information provided by the three-dimensional inspection model enables the robot to better adapt to changes in the ground, thereby reducing the positioning error caused by terrain changes. Through the PSR surface reconstruction algorithm and the splicing of local three-dimensional models, the robot can perceive subtle changes in the ground in real time and optimize its movement path based on this information. In this way, when the robot faces irregular terrain, it can estimate its position more accurately, thereby avoiding positioning errors caused by uneven friction and obstacles. By converting and reconstructing the total point cloud data, not only can the ground model be reconstructed more accurately, but also the dynamic adaptation between the robot foot end and the ground can be achieved by analyzing the relationship between the ground and the foot end contact angle. The present invention combines high-precision point cloud data and three-dimensional modeling technology to generate accurate terrain models in real time in uneven and complex environments, so that the robot can autonomously adjust and optimize the path. This not only reduces the need for manual intervention, but also improves the inspection efficiency and accuracy of the bionic robot, and adapts to more complex working environments, such as grain docks where there are debris and stacked objects. By obtaining the edge overlap points of two adjacent local three-dimensional inspection models and using these overlap points to splice and combine local models, a more complete and coherent three-dimensional positioning model can be achieved. This method can effectively avoid the limitations of a single model, especially when conducting large-scale inspections, it can reduce errors caused by changes in perspective or local environment, thereby improving the success rate of the entire inspection task. ,

[0033] The present application also provides an intelligent bionic robot inspection and positioning system at a grain terminal, comprising: The first adjustment module is used to obtain ground features of multiple preset sections of the grain terminal and multiple initial foot end joint angles of the bionic robot, and adjust each initial foot end joint angle of the bionic robot according to each of the initial foot end joint angles and each ground feature; A first acquisition module is used to acquire the position characteristics and multiple foot bottoming pressure values ​​of the bionic robot after each initial foot end joint angle adjustment, and acquire corresponding slip adjustment values ​​according to the position characteristics and multiple foot bottoming pressure values; A second acquisition module is used to adjust the foot contact point of the bionic robot according to each of the slip adjustment values, and to acquire in real time multiple angular accelerations and linear accelerations of the bionic robot in each preset road section during the adjustment process; A first generating module is used to generate a local motion trajectory of the bionic robot in each preset section according to a plurality of angular accelerations and linear accelerations, and obtain a three-dimensional environmental image of the grain terminal at a plurality of consecutive moments in each local motion trajectory; A third acquisition module, used to acquire a corresponding local environment map according to the plurality of the three-dimensional environment images, and to acquire a corresponding local optimization map according to each of the local environment maps; The second generation module is used to obtain the corresponding total point cloud data set according to each of the local motion trajectories, and generate a three-dimensional positioning inspection model according to multiple total point cloud data sets and local optimization maps, so that the bionic robot can perform inspection and positioning according to the three-dimensional positioning inspection model.

[0034] In one embodiment, the first adjustment module includes: A first acquisition unit, configured to acquire height data of a plurality of adjacent points according to each of the ground features, and acquire a corresponding ground normal vector according to the plurality of height data; A second acquisition unit, configured to acquire a corresponding ground horizontal slope and a ground vertical slope according to each of the ground normal vectors, and to acquire a corresponding ground slope angle according to each of the ground horizontal slope and the ground vertical slope; A third acquisition unit is used to acquire a foot end contact plane normal vector corresponding to the bionic robot according to each of the initial foot end joint angles, and to acquire a corresponding initial foot end ground contact angle according to each of the foot end contact plane normal vectors and the ground normal vector; A judging unit, used for judging whether each of the initial foot end contact angles is different from a ground slope angle; If the initial foot-end contact angle is different from the ground slope angle, obtaining a corresponding error angle according to each of the initial foot-end contact angle and the ground slope angle; The adjusting unit is used to adjust the initial foot end joint angle of the bionic robot according to each of the error angles.

[0035] It should be noted that each module and unit in the inspection and positioning system of the intelligent bionic robot at the grain terminal corresponds one by one to the steps in the inspection and positioning method of the intelligent bionic robot at the grain terminal.

[0036] like Figure 3As shown, the present application also provides a computer device, which may be a server, and its internal structure may be as shown in Figure 3 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store all data required for the process of the intelligent bionic robot patrol positioning method at the grain terminal. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the intelligent bionic robot patrol positioning method at the grain terminal is implemented.

[0037] Those skilled in the art will understand that Figure 3 The structure shown in is merely a block diagram of a portion of the structure related to the present application solution and does not constitute a limitation on the computer device to which the present application solution is applied.

[0038] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, any one of the above-mentioned intelligent bionic robot inspection and positioning methods at a grain terminal is implemented.

[0039] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0040] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0041] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for inspection and positioning of an intelligent bionic robot at a grain terminal, characterized in that: include: Acquire ground features of multiple preset sections of the grain terminal and multiple initial foot end joint angles of the bionic robot, and adjust each initial foot end joint angle of the bionic robot according to each of the initial foot end joint angles and each ground feature; Obtaining the position characteristics and multiple foot bottoming pressure values ​​of the bionic robot after each initial foot end joint angle adjustment, and obtaining corresponding slip adjustment values ​​according to the position characteristics and multiple foot bottoming pressure values; Adjusting the foot contact point of the bionic robot according to each of the slip adjustment values, and acquiring in real time multiple angular accelerations and linear accelerations of the bionic robot in each preset road section during the adjustment process; Generate a local motion trajectory of the bionic robot in each preset section according to multiple angular accelerations and linear accelerations, and obtain a three-dimensional environmental image of the grain terminal at multiple consecutive moments in each local motion trajectory; Acquire a corresponding local environment map according to the plurality of three-dimensional environment images, and acquire a corresponding local optimization map according to each of the local environment maps; A corresponding total point cloud data set is obtained according to each of the local motion trajectories, and a three-dimensional positioning inspection model is generated according to multiple total point cloud data sets and local optimization maps, so that the bionic robot performs inspection and positioning according to the three-dimensional positioning inspection model.

2. The method for inspection and positioning of an intelligent bionic robot at a grain terminal according to claim 1 is characterized in that: The step of adjusting each initial foot end joint angle of the bionic robot according to each initial foot end joint angle and each ground feature comprises: Acquire height data of a plurality of adjacent points according to each of the ground features, and acquire a corresponding ground normal vector according to the plurality of height data; Acquire a corresponding ground horizontal slope and a ground vertical slope according to each of the ground normal vectors, and acquire a corresponding ground slope angle according to each of the ground horizontal slope and the ground vertical slope; Acquire a foot contact plane normal vector corresponding to the bionic robot according to each of the initial foot joint angles, and acquire a corresponding initial foot contact angle according to each of the foot contact plane normal vectors and the ground normal vector; Determining whether each of the initial foot-end contact angles is different from a ground slope angle; If the initial foot-end contact angle is different from the ground slope angle, obtaining a corresponding error angle according to each of the initial foot-end contact angle and the ground slope angle; The initial foot end joint angle of the bionic robot is adjusted according to each of the error angles.

3. The method for inspection and positioning of an intelligent bionic robot at a grain terminal according to claim 1 is characterized in that: The step of obtaining the slip adjustment value according to the position feature and a plurality of foot bottom contact pressure values ​​comprises: Acquire the center of gravity position information and each foot end position information of the bionic robot according to the position feature, and acquire the corresponding foot weight vector according to the center of gravity position information and each foot end position information; Obtaining a torque vector sum according to the plurality of foot bottom contact pressure values ​​and foot weight vectors, and determining whether the torque vector sum is zero; If the sum of the torque vectors is zero, it is determined that the bionic robot has not slipped; If the moment vector sum is not zero, it is determined that the bionic robot has slipped, and the reference force, friction coefficient, real-time foot end contact angle and motion characteristics of the bionic robot at this time are obtained, wherein the motion characteristics include real-time speed, real-time acceleration, total mass and gravitational acceleration; Acquire the weight of the bionic robot according to the total mass and the gravitational acceleration, and acquire the vertical normal force according to the weight of the bionic robot and the real-time foot end contact angle; Acquire the inertial force according to the total mass and the real-time acceleration, and acquire the horizontal normal force according to the inertial force and the real-time foot end contact angle; Obtaining a total normal force according to the horizontal normal force and the vertical normal force, and obtaining a friction force according to the total normal force and a friction coefficient; A slip adjustment factor is obtained according to the reference force and the friction force, and a slip adjustment value is obtained according to the slip adjustment factor, the real-time speed and the real-time acceleration.

4. The method for inspection and positioning of an intelligent bionic robot at a grain terminal according to claim 1 is characterized in that: The step of generating a local motion trajectory according to a plurality of angular accelerations and linear accelerations comprises: Acquiring a sampling frequency and initial features of the bionic robot, wherein the initial features include initial position information, initial linear velocity, initial angular velocity, and initial angle; Acquire a time step according to the sampling frequency, and acquire a corresponding real-time updated linear velocity according to the time step, the initial linear velocity and each linear acceleration; Obtaining a real-time updated position according to each of the real-time updated linear velocities, initial positions, linear accelerations and time steps; Obtaining a corresponding real-time updated angular velocity according to the time step, the initial angular velocity and each angular acceleration; Acquire a real-time updated angle according to each of the real-time updated angular velocity, angular acceleration, initial angle and time step; With time as the X-axis, angle as the Y-axis, and position as the Z-axis, a three-dimensional coordinate axis of time-angle-position is established, and the initial position information and initial angular velocity are plotted on the three-dimensional coordinate axis of time-angle-position as the starting point; The real-time update angle and real-time update position corresponding to each time step are plotted as connection points on the three-dimensional coordinate axis of time-angle-position; The starting point and multiple connection points are sequentially connected through curves to obtain a local motion trajectory.

5. The method for inspection and positioning of an intelligent bionic robot at a grain terminal according to claim 1 is characterized in that: The step of obtaining a local environment map according to the plurality of three-dimensional environment images, and obtaining a local optimization map according to the local environment map comprises: Dividing each of the three-dimensional environment images into a plurality of local windows, and acquiring a plurality of pixel points according to each of the local windows; Obtaining the horizontal gradient and the vertical gradient of each pixel point, and obtaining the sum of squares of the horizontal gradients according to the multiple horizontal gradients; Obtaining a vertical gradient square sum according to the plurality of vertical gradients, and obtaining a gradient product sum according to the plurality of vertical gradients and horizontal gradients; Generate an autocorrelation matrix according to the gradient product sum, the horizontal gradient square sum, and the vertical gradient square sum, and obtain a determinant value and a trace value according to the autocorrelation matrix; Obtaining a response function value according to the determinant value and the trace value, and determining whether the response function value is greater than a preset threshold; If the response function value is greater than a preset threshold, the pixel point is determined to be a corner point; Acquire corresponding matching points according to the plurality of corner points, and construct a local environment map according to the plurality of matching points using a SLAM algorithm; Extracting feature points corresponding to each corner point according to the local environment map, and obtaining a reprojection error according to a plurality of the feature points and corner points; The feature points in the local environment map are optimized and adjusted according to the reprojection error to obtain a local optimized map.

6. The method for inspection and positioning of an intelligent bionic robot at a grain terminal according to claim 1 is characterized in that: The step of acquiring a corresponding total point cloud data set according to each of the local motion trajectories, and generating a three-dimensional positioning inspection model according to a plurality of the total point cloud data sets and the local optimized map includes: According to each of the local motion trajectories, the point cloud data sets to be registered at multiple preset positions of the grain terminal are collected in real time, and the point cloud data set to be registered at the central position is selected as the target point cloud data set; For each point to be registered in each of the point cloud data sets to be registered, the nearest neighbor matching point is obtained from the target point cloud data set by using the Euclidean distance measurement method; Obtaining a distance difference between each of the nearest neighbor matching points and the point to be registered, and adjusting the position of each of the point to be registered in each of the point cloud data sets to be registered according to the distance difference to obtain a corresponding registration point cloud data set; Each registered point cloud data set is converted into a global coordinate system and duplicate registration points are deleted to obtain a total point cloud data set; The PSR surface reconstruction algorithm is used to convert each total point cloud data set and the corresponding local optimization map into a local three-dimensional inspection model; The edge overlap points of each two adjacent local three-dimensional inspection models are obtained, and multiple local three-dimensional inspection models are combined and spliced ​​according to each of the edge overlap points to obtain a three-dimensional positioning inspection model.

7. An intelligent bionic robot inspection and positioning system at a grain terminal, characterized in that: include: The first adjustment module is used to obtain ground features of multiple preset sections of the grain terminal and multiple initial foot end joint angles of the bionic robot, and adjust each initial foot end joint angle of the bionic robot according to each of the initial foot end joint angles and each ground feature; A first acquisition module is used to acquire the position characteristics and multiple foot bottoming pressure values ​​of the bionic robot after each initial foot end joint angle adjustment, and acquire corresponding slip adjustment values ​​according to the position characteristics and multiple foot bottoming pressure values; A second acquisition module is used to adjust the foot contact point of the bionic robot according to each of the slip adjustment values, and to obtain in real time multiple angular accelerations and linear accelerations of the bionic robot in each preset section during the adjustment process; The first generation module is used to generate a local motion trajectory of the bionic robot in each preset section according to multiple angular accelerations and linear accelerations, and obtain a three-dimensional environmental image of the grain terminal at multiple consecutive moments in each local motion trajectory; A third acquisition module, used to acquire a corresponding local environment map according to the plurality of the three-dimensional environment images, and to acquire a corresponding local optimization map according to each of the local environment maps; The second generation module is used to obtain the corresponding total point cloud data set according to each of the local motion trajectories, and generate a three-dimensional positioning inspection model according to multiple total point cloud data sets and local optimization maps, so that the bionic robot can perform inspection and positioning according to the three-dimensional positioning inspection model.

8. The intelligent bionic robot inspection and positioning system at a grain terminal according to claim 7 is characterized in that: The first adjustment module includes: A first acquisition unit, configured to acquire height data of a plurality of adjacent points according to each of the ground features, and acquire a corresponding ground normal vector according to the plurality of height data; A second acquisition unit, configured to acquire a corresponding ground horizontal slope and a ground vertical slope according to each of the ground normal vectors, and to acquire a corresponding ground slope angle according to each of the ground horizontal slope and the ground vertical slope; A third acquisition unit is used to acquire a foot end contact plane normal vector corresponding to the bionic robot according to each of the initial foot end joint angles, and to acquire a corresponding initial foot end ground contact angle according to each of the foot end contact plane normal vectors and the ground normal vector; A judging unit, used for judging whether each of the initial foot end contact angles is different from a ground slope angle; If the initial foot-end contact angle is different from the ground slope angle, obtaining a corresponding error angle according to each of the initial foot-end contact angle and the ground slope angle; The adjusting unit is used to adjust the initial foot end joint angle of the bionic robot according to each of the error angles.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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