An operating attitude control method, system and storage medium for heavy-load robots

Abstract

The present invention discloses an operating attitude control method, system and storage medium for heavy-load robots, which relates to the technical field of robot control. It constructs a collection of acquisition features E in real time, and combines the load distribution information and the center-of-gravity offset △G obtained by the internal monitoring system of the robot to effectively evaluate the operating attitude and load conditions of the robot in a complex environment. The robot can accurately adjust its forward attitude and dynamically generate control commands through the execution vector W to achieve real-time control and correction. By continuously tracking and statistically analyzing the change amount △Z of the center-of-gravity position of the robot, adjustments and warnings are triggered in a timely manner to further improve the stability and safety of the robot in complex tasks. Compared with traditional methods, this method can not only respond in real time to dynamic factors such as ground undulations and load changes, but also enhance the adaptability to external environment and changes in the robot's own state. Especially during operation, through a precise attitude adjustment and center-of-gravity offset evaluation mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of robot control, and specifically provides an operating attitude control method, system and storage medium for heavy-load robots. Background Art

[0002] With the progress of technology, automation technology has been gradually applied in multiple fields. Especially in heavy-load operations such as industry, logistics, and agriculture, heavy-load robots, as an important part of intelligent equipment, are playing an increasingly important role. These robots are mainly used for tasks such as handling, loading and unloading, and transportation, and can replace manual labor for efficient and precise operations. Specifically, heavy-load robots can not only handle common handling operations, such as handling large quantities of goods in warehouses, but are also widely used in special scenarios, such as transporting dangerous goods, handling building materials, and port loading and unloading.

[0003] In the invention with the Chinese patent application number CN202411359674.0, an intelligent control method, device and equipment for heavy-load robots are disclosed, belonging to the field of heavy-load robots. In this invention, the torque information received by each lever arm in each operable attitude is calculated based on the mass information of the goods and the dataset of operable postures of the heavy-load robot. Finally, the operating attitude of the heavy-load robot during the loading and unloading process is obtained according to the torque information received by each lever arm in the operable attitude and the movement path of the goods during the handling process, and the heavy-load robot is controlled according to the operating attitude of the heavy-load robot during the loading and unloading process. This invention fully considers the change of the maximum load torque of each lever arm of the heavy-load robot in different operating postures, so that the heavy-load robot can change its operating posture according to the characteristics of the maximum load torque, which can improve the service life of the heavy-load robot, reduce the maintenance cost of the heavy-load robot, and make the control of the heavy-load robot more reasonable.

[0004] It can be seen from this that the above invention can solve limited scenarios and situations. For example, most of the existing control methods can only preset the load distribution and attitude adjustment strategies, but cannot adjust according to real-time feedback during actual operation. This results in the inability to respond in a timely manner when the robot encounters sudden ground undulations or unevenness during transportation, thus affecting the overall transportation efficiency and the quality of task completion. Especially in complex environments, the impact of ground unevenness on the robot is very significant. At the same time, most of the existing attitude control methods for heavy-load robots are designed based on predetermined load distributions and ground conditions. These methods usually assume that the ground conditions encountered by the robot during operation are relatively flat or predictable. However, in practical applications, especially in industrial environments, the ground often has irregular undulations, potholes, and obstacles. These uneven grounds directly affect the load distribution and stability of the robot. Existing technologies often cannot monitor and compensate for the impact of ground unevenness in real time. Once the robot encounters uneven ground, there may be uneven load distribution or center-of-gravity shift, resulting in unstable robot attitude, and even abnormal situations such as tilting, overturning, or insufficient power. Existing control methods rely more on static load calculations and ground condition predictions, lacking dynamic and real-time adaptability. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention provides a running attitude control method, system, and storage medium for heavy-load robots, which solve the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A running attitude control method for heavy-load robots includes the following steps:

[0007] S1. By integrating a laser scanner and a depth camera sensor on the robot, real-time data of the ground along the current forward path of the robot is obtained, and after collection and analysis, it is recorded as the collection feature set E;

[0008] S2. Obtain the load distribution of the robot through the monitoring system integrated with the robot, and calculate the center-of-gravity offset △G of the current robot according to the load status, and output the state evaluation vector B of the robot;

[0009] S3. Analyze the ground along the current forward path of the robot based on the collection feature set E and the state evaluation vector B to obtain the attitude adjustment vector △D for the robot's self-adaptive adjustment;

[0010] S4. The robot adjusts the current forward attitude of the robot based on the obtained attitude adjustment vector △D to obtain the adjusted execution vector W, and generates a control command for the execution vector W to dynamically adjust the forward attitude of the robot and mark the center-of-gravity position G of the current robot;

[0011] S5. By statistically calculating the change amount △Z of the center of gravity position of the robot within a fixed period and comparing it with the preset threshold Zthe of the center of gravity offset during the robot's operation, the evaluation result of the center of gravity offset during the current robot's operation is obtained, and iterative optimization is performed based on the evaluation result of the center of gravity offset during the operation.

[0012] Preferably, the S1 includes S11 and S12;

[0013] S11. By integrating a laser scanner and a depth camera sensor on the robot, real-time data of the ground along the forward path of the current robot is obtained, and the height value Zg(x, y) at the position (x, y), the slope change value Gd(x, y) at the position (x, y), and the obstacle identifier ZAbs at the position (x, y) are obtained, and a ground feature data set G is formed.

[0014] Among them, the height value Zg(x, y) is obtained through matching and analysis of the laser scanning position height information Zc(x, y) and the position height Zl(x, y) identified by the depth camera sensor;

[0015] S12. By marking the timestamp information and the unique identifier information of the carrier on the ground feature data set G, an acquisition feature set E of the ground along the forward path of the robot transporting the carrier at time t is formed.

[0016] Preferably, the laser scanning position height information Zc(x, y) is obtained through the laser scanning height information measured in real time by the laser scanner, and the position height Zl(x, y) identified by the depth camera sensor is obtained through real-time analysis by the depth camera sensor;

[0017] The height value Zg(x, y) is obtained through the following calculation formula:

[0018] ;

[0019] In the formula, max represents the maximum value function, and max(Zc(x, y), Zl(x, y)) represents taking the maximum height value of the laser scanning position height information Zc(x, y) and the position height Zl(x, y) identified by the depth camera sensor at the same position (x, y);

[0020] The slope change value Gd(x, y) is obtained through the following calculation formula:

[0021] ;

[0022] In the formula, arctan represents the arctangent function, and dx represents the horizontal distance between the height values at the position (x, y) and the position (x + 1, y);

[0023] The obstacle identifier ZAbs is obtained by comparing with a preset ground height threshold Zgthe of the robot's forward path;

[0024] Specifically, the comparison is carried out in the following way:

[0025] ;

[0026] When the obstacle identifier ZAbs ≥ the ground height threshold Zgthe of the robot's forward path, it is determined that the height value of the ground of the robot's forward path at the position (x, y) is abnormal, and the obstacle identifier ZAbs = 1 is marked;

[0027] When the obstacle identifier ZAbs < the ground height threshold Zgthe of the robot's forward path, it is determined that the height value of the ground of the robot's forward path at the position (x, y) is normal, and the obstacle identifier ZAbs = 0 is marked.

[0028] Preferably, the S2 includes S21 and S22;

[0029] S21. Obtain the load state of the robot by establishing a monitoring system integrated with the robot, including using an accelerometer and a force sensor to cooperate to obtain the load mass of each component of the robot, so as to obtain the center of gravity position G of the robot under the current operating state, and compare it with the preset target center of gravity position Gtarget of the robot to obtain the center of gravity offset △G of the current robot;

[0030] S22. Integrate the center of gravity offset △G with the operating attitude angle of the robot to output the state evaluation vector B of the robot, where the operating attitude angle includes the pitch angle Pitch, the roll angle Roll and the heading angle Yaw, and is specifically obtained by a gyroscope sensor, an acceleration sensor and a magnetometer sensor.

[0031] Preferably, the center of gravity position G is obtained by the following calculation formula:

[0032] ;

[0033] In the formula, Xg, Yg and Zg respectively represent the horizontal axis coordinate, the vertical axis coordinate and the longitudinal axis coordinate of the total center of gravity of the robot, n represents the total number of robot components, q(i) represents the mass of the i-th component, and x(i), y(i) and z(i) respectively represent the horizontal axis coordinate, the vertical axis coordinate and the longitudinal axis coordinate of the i-th component;

[0034] The center of gravity offset △G is obtained by a calculation formula, where the target center of gravity position Gtarget includes the target horizontal axis Xtarget, the target vertical axis Ytarget and the target longitudinal axis Ztarget of the target center of gravity position.

[0035] Preferably, the step S3 includes S31;

[0036] S31. Analyze the influence of the ground on the attitude and load of the robot along the forward path by analyzing the collected feature set E and the state evaluation vector B, and calculate the attitude adjustment vector △D for the robot's adaptive adjustment, including the offset △X in the horizontal axis position, the offset △Y in the vertical axis position, the offset △Z in the longitudinal axis position, the pitch angle adjustment △Pitch, the roll angle adjustment △Roll, and the yaw angle adjustment △Yaw;

[0037] The offset △X in the horizontal axis position is obtained by the following calculation formula:

[0038] ;

[0039] In the formula, k1 represents the first proportionality coefficient, Zg(x, y) represents the height value at the position (x, y), and Gd(x, y) represents the slope change value at the position (x, y);

[0040] The offset △Y in the vertical axis position is obtained by the following calculation formula:

[0041] ;

[0042] In the formula, k2 represents the second proportionality coefficient, which is specifically used to adjust the influence of the height difference of the path on the offset of the vertical axis position △Y;

[0043] The offset △Z in the longitudinal axis position is obtained by the following calculation formula:

[0044] ;

[0045] In the formula, k3 represents the third proportionality coefficient, which is specifically used to control the influence degree of the obstacle on the offset △Z in the longitudinal axis position, and ZAbs represents the obstacle identifier;

[0046] The pitch angle adjustment △Pitch is obtained by the following calculation formula:

[0047] ;

[0048] In the formula, k4 and k7 respectively represent the fourth proportionality coefficient and the seventh proportionality coefficient, which are specifically used to adjust the influence of the offset on the pitch angle adjustment △Pitch;

[0049] The roll angle adjustment △Roll is obtained by the following calculation formula:

[0050] ;

[0051] In the formula, k5 and k8 respectively represent the fifth proportionality coefficient and the eighth proportionality coefficient, which are specifically used to adjust the influence of the offset on the roll angle adjustment amount △Roll;

[0052] The heading angle adjustment amount △Yaw is obtained through the following calculation formula:

[0053] ;

[0054] In the formula, k6 and k9 respectively represent the sixth proportionality coefficient and the ninth proportionality coefficient, which are specifically used to adjust the influence of the offset on the heading angle adjustment amount △Yaw.

[0055] Preferably, the S4 includes S41;

[0056] S41. The robot adjusts the forward attitude of the current robot based on the obtained attitude adjustment vector △D. Specifically, by converting the horizontal axis position offset △X, vertical axis position offset △Y, longitudinal axis position offset △Z, pitch angle adjustment amount △Pitch, roll angle adjustment amount △Roll, and heading angle adjustment amount △Yaw in the attitude adjustment vector △D into control commands, and acting on the mechanical components through the internal motion control system of the robot to change the forward attitude of the robot, the adjusted execution vector W is obtained, and a control command is generated for the execution vector W to dynamically adjust the forward attitude of the robot and mark the center of gravity position G of the current robot;

[0057] The execution vector W is obtained through the following conversion formula:

[0058] ;

[0059] In the formula, f represents the conversion function preset by the control system.

[0060] Preferably, the S5 includes S51;

[0061] S51. By statistically analyzing the fluctuation change information of the center of gravity position G of the robot within a fixed period, the center of gravity position change amount △Z is obtained, and it is compared with the preset robot running center of gravity offset threshold Zthe to obtain the running center of gravity offset evaluation result of the current robot, and an iterative optimization mechanism is carried out according to the running center of gravity offset evaluation result;

[0062] The center of gravity position change amount △Z is obtained through the following calculation formula:

[0063] ;

[0064] In the formula, M represents the total number of samples within a fixed period, t(j + 1, G) represents the center of gravity position at the sampling time t of the (j + 1)-th time, and t(j, G) represents the center of gravity position at the sampling time t of the j-th time;

[0065] The running center-of-gravity offset evaluation result is obtained through the following comparison method:

[0066] When the change amount of the center-of-gravity position △Z ≥ the running center-of-gravity offset threshold Zthe of the robot, the running center-of-gravity offset evaluation result is obtained as an abnormal result, triggering an iterative optimization mechanism, including adjusting the acquisition of the attitude adjustment vector △D, and performing center-of-gravity position correction and attitude angle correction, specifically by adjusting the first proportionality coefficient k1 to the ninth proportionality coefficient k9;

[0067] When the change amount of the center-of-gravity position △Z < the running center-of-gravity offset threshold Zthe of the robot, the running center-of-gravity offset evaluation result is obtained as a non-abnormal result.

[0068] An operating attitude control system for a heavy-load robot includes a forward path acquisition module, a machine state acquisition module, an attitude analysis and adjustment module, an adjustment generation and marking module, and an iterative optimization module;

[0069] The forward path acquisition module integrates a laser scanner and a depth camera sensor on the robot to obtain real-time data of the ground of the current forward path of the robot in real time, and records it as the acquisition feature set E after acquisition and analysis;

[0070] The machine state acquisition module obtains the load distribution of the robot by establishing a connection with the monitoring system integrated inside the robot, and calculates and obtains the center-of-gravity offset amount △G of the current robot, and outputs the state evaluation vector B of the robot;

[0071] The attitude analysis and adjustment module analyzes the influence of the ground of the forward path of the robot on the attitude and load of the robot through the acquisition feature set E and the state evaluation vector B, and calculates and obtains the attitude adjustment vector △D for the robot to adaptively adjust;

[0072] The adjustment generation and marking module adjusts the forward attitude of the current robot based on the obtained attitude adjustment vector △D, obtains the adjusted execution vector W, and generates a control command for the execution vector W to dynamically adjust the forward attitude of the robot and mark the center-of-gravity position G of the current robot;

[0073] The iterative optimization module obtains the running center-of-gravity offset evaluation result of the current robot by counting the change amount of the center-of-gravity position △Z of the robot within a fixed period and comparing it with the preset running center-of-gravity offset threshold Zthe of the robot, and performs iterative optimization according to the running center-of-gravity offset evaluation result.

[0074] An operating attitude control storage medium for a heavy-load robot stores a computer program, and when the computer program is executed, an operating attitude control method for a heavy-load robot is implemented.

[0075] The present invention provides a running attitude control method, system and storage medium for heavy-load robots, which have the following beneficial effects:

[0076] (1) The acquisition feature set E is constructed in real time, and combined with the load distribution information and the center-of-gravity offset △G obtained by the internal monitoring system of the robot, the running attitude and load conditions of the robot in a complex environment are effectively evaluated. The robot can accurately adjust its forward attitude, and dynamically generate control commands through the execution vector W to achieve real-time control and correction. By continuously tracking and statistically analyzing the change amount △Z of the center-of-gravity position of the robot, adjustments and warnings are triggered in a timely manner, further improving the stability and safety of the robot in complex tasks. Compared with traditional methods, this method can not only respond in real time to dynamic factors such as ground undulations and load changes, but also enhance the adaptability to external environments and changes in the robot's own state. Especially during operation, through the accurate attitude adjustment and center-of-gravity offset evaluation mechanism, problems such as the inability of traditional methods to adjust the robot's attitude and load distribution in real time and the neglect of the impact of ground unevenness and environmental changes on the running stability of the robot are effectively solved.

[0077] (2) By calculating and optimizing the current center-of-gravity position G of the robot and comparing it with the target center-of-gravity position Gtarget, the center-of-gravity offset △G is obtained, which effectively guides the robot to maintain balance on unstable ground. For example, in response to factors such as height changes, slope differences, and obstacle positions on the path, the robot can reasonably adjust its attitude angle to maintain stability. By using different proportionality coefficients, flexible responses to factors such as path changes, load offsets, and attitude adjustments are achieved, effectively avoiding over-adjustment or under-adjustment situations and ensuring the smooth operation of the robot. This method not only improves the robot's adaptability to complex environments, but also greatly enhances its running stability on dynamic and changing ground, further optimizing the robot's attitude control system.

[0078] (3) By precisely controlling the attitude adjustment vector △D of the robot, it is ensured that the robot can always adapt to changes in the ground and load in real time during forward movement. By statistically analyzing the fluctuating changes in the center-of-gravity position of the robot within a fixed period, the change amount △Z of the center-of-gravity position is obtained and compared with the preset center-of-gravity offset threshold Zthe to evaluate the running center-of-gravity offset situation of the robot. When it is detected that the change amount △Z of the center-of-gravity position exceeds the preset threshold, the system triggers an iterative optimization mechanism to further adjust the attitude adjustment vector △D. By correcting the center-of-gravity position and attitude angle, the forward path of the robot is optimized. This mechanism can respond to abnormal states in a timely manner, ensuring the continuous and stable operation of the robot under heavy loads and in complex environments, and effectively avoiding attitude instability problems caused by load changes or path mutations. Through dynamic adjustment and optimization, the adaptability and operation efficiency of the robot in various complex terrains are improved. Description of the Drawings

[0079] Figure 1 Schematic diagram of the steps of a method for controlling the running posture of a heavy-load robot according to the present invention;

[0080] Figure 2 Block diagram of a system for controlling the running posture of a heavy-load robot according to the present invention. Specific implementation manner

[0081] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention. Embodiment 1

[0082] The present invention provides a method for controlling the running posture of a heavy-load robot. Please refer to Figure 1 , including the following steps:

[0083] S1. By integrating a laser scanner and a depth camera sensor on the robot, real-time data of the ground of the current forward path of the robot is obtained in real time, and after collection and analysis, it is recorded as the collection feature set E;

[0084] S2. By establishing a monitoring system integrated with the robot, the load distribution of the robot is obtained, and according to the load state, the center of gravity offset △G of the current robot is calculated, and the state evaluation vector B of the robot is output;

[0085] S3. By analyzing the current forward path ground of the robot with the collection feature set E and the state evaluation vector B, the influence of the robot's posture and load is obtained, and the posture adjustment vector △D for the robot to adaptively adjust is obtained;

[0086] S4. The robot adjusts the current forward posture of the robot based on the obtained posture adjustment vector △D, obtains the adjusted execution vector W, and generates a control command for the execution vector W to dynamically adjust the forward posture of the robot and mark the center of gravity position G of the current robot;

[0087] S5. By statistically analyzing the change amount △Z of the center of gravity position of the robot within a fixed period and comparing it with the preset threshold Zthe of the center of gravity offset of the robot's operation, the evaluation result of the center of gravity offset of the current robot's operation is obtained, and iterative optimization is performed according to the evaluation result of the center of gravity offset of the operation.

[0088] In this embodiment, through steps S1 to S5, ground data is collected and analyzed in real time to construct a collection feature set E. Combining the load distribution information and the center of gravity offset ΔG obtained by the robot's internal monitoring system, the running posture and load condition of the robot in a complex environment are effectively evaluated. The robot can accurately adjust its forward posture and dynamically generate control commands through the execution vector W to achieve real-time control and correction. By continuously tracking and statistically analyzing the change amount ΔZ of the robot's center of gravity position and comparing it with the preset center of gravity offset threshold Zthe, this method can automatically evaluate and optimize the running state of the robot within a fixed period, trigger adjustments and warnings in a timely manner, and further improve the stability and safety of the robot in complex tasks. Compared with traditional methods, this method can not only respond to dynamic factors such as ground undulation and load changes in real time, but also enhance the adaptability to external environment and the change of the robot's own state through the close association between the collection feature set E and the state evaluation vector B. Especially during operation, through the accurate posture adjustment and center of gravity offset evaluation mechanism, it effectively solves the problems that traditional methods cannot adjust the robot's posture and load distribution in real time and ignore the impact of ground unevenness and environmental changes on the running stability of the robot. This innovative method ensures the continuous operation of the robot in an unstable environment, reduces the maintenance cost and potential failure risks, and further improves the intelligence level of heavy-load robots. Embodiment 2

[0089] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically: S1 includes S11 and S12;

[0090] S11. By integrating a laser scanner and a depth camera sensor on the robot, real-time data of the ground along the forward path of the current robot is obtained, including the height value Zg(x, y) at the position (x, y), the slope change value Gd(x, y) at the position (x, y), and the obstacle identifier ZAbs at the position (x, y), to form a ground feature data set G.

[0091] Among them, the height value Zg(x, y) is obtained through the matching and analysis of the laser scanning position height information Zc(x, y) and the depth camera sensor-identified position height Zl(x, y).

[0092] S12. By marking the timestamp information and the unique identifier information of the carried object on the ground feature data set G, a collection feature set E of the ground along the forward path of the robot transporting the carried object at time t is formed.

[0093] The laser scanning position height information Zc(x, y) is obtained from the laser scanning height information measured in real time by the laser scanner, and the depth camera sensor recognition position height Zl(x, y) is obtained by real-time analysis of the depth camera sensor;

[0094] The height value Zg(x, y) is obtained by the following calculation formula:

[0095] ;

[0096] In the formula, max represents the maximum value function, and max(Zc(x, y), Zl(x, y)) represents taking the maximum height value of the laser scanning position height information Zc(x, y) and the depth camera sensor recognition position height Zl(x, y) at the same position (x, y), so that the maximum value at the forward position (x, y) can be continuously obtained as the robot's forward path changes;

[0097] The slope change value Gd(x, y) is obtained by the following calculation formula:

[0098] ;

[0099] In the formula, arctan represents the arctangent function, and dx represents the horizontal distance between the height values at the positions (x, y) and (x + 1, y);

[0100] The obstacle identification ZAbs is obtained by comparing with the preset ground height threshold Zgthe of the robot's forward path;

[0101] Specifically, the comparison is made in the following way:

[0102] ;

[0103] When the obstacle identification ZAbs ≥ the ground height threshold Zgthe of the robot's forward path, it is judged that the height value of the ground of the robot's forward path at the position (x, y) is abnormal, and the obstacle identification ZAbs = 1 is marked;

[0104] When the obstacle identification ZAbs < the ground height threshold Zgthe of the robot's forward path, it is judged that the height value of the ground of the robot's forward path at the position (x, y) is normal, and the obstacle identification ZAbs = 0 is marked.

[0105] In this embodiment, by collecting the ground data of the forward path in real time, the height values Zg(x, y), slope change values Gd(x, y), and obstacle identifiers ZAbs at different positions (x, y) are accurately obtained. Through methods such as maximum value selection and difference calculation, the ground feature data set G on the forward path is continuously updated. These data provide comprehensive ground status information for the robot, enabling the robot to perceive the changes in the forward path in real time, including ground undulations, slope changes, and potential obstacles. During this process, by marking the time stamps and carrying unique identifier information for the ground feature data at each moment, a complete collection feature set E is finally formed, providing accurate environmental feedback for subsequent robot attitude adjustment and load control. By comprehensively using the three parameters of the height value Zg(x, y), slope change value Gd(x, y), and obstacle identifier ZAbs, the real-time adaptability of the robot to the environment is ensured. By dynamically updating these ground feature data, the robot can accurately judge the ground status of the forward path, timely identify ground obstacles, and take corresponding countermeasures. In addition, the maximum value selection mechanism of the height value Zg(x, y) improves the accuracy of the data, and the calculation of the slope change value Gd(x, y) can effectively help the robot identify the changes in the road surface slope and avoid losing balance on uneven ground. Finally, by judging the obstacle identifier ZAbs, the robot can avoid the influence of obstacles on the forward route, thus ensuring the stable operation of the robot in a complex and dynamic environment. This innovative method significantly improves the perception ability and adaptability of the robot under different ground conditions, laying a solid foundation for subsequent attitude adjustment and load control. Embodiment 3

[0106] This embodiment is an explanatory description based on Embodiment 2. Please refer to Figure 1 , specifically: S2 includes S21 and S22;

[0107] S21. Obtain the load status of the robot by establishing a monitoring system integrated with the robot's interior, including using an accelerometer and a force sensor to cooperate to obtain the load mass of each component of the robot, so as to obtain the center of gravity position G of the robot under the current operating state, and compare it with the preset target center of gravity position Gtarget of the robot to obtain the center of gravity offset △G of the current robot;

[0108] S22. Integrate the center of gravity offset △G with the operating attitude angles of the robot to output the state evaluation vector B of the robot, where the operating attitude angles include the pitch angle Pitch, roll angle Roll, and yaw angle Yaw, which are specifically obtained through a gyroscope sensor, an acceleration sensor, and a magnetometer sensor.

[0109] The center of gravity position G is obtained through the following calculation formula:

[0110] ;

[0111] In the formula, Xg, Yg, and Zg respectively represent the horizontal axis coordinate, vertical axis coordinate, and longitudinal axis coordinate of the total center of gravity of the robot, n represents the total number of robot components, q(i) represents the mass of the i-th component, and x(i), y(i), and z(i) respectively represent the horizontal axis coordinate, vertical axis coordinate, and longitudinal axis coordinate of the i-th component;

[0112] The center of gravity offset △G is obtained through the calculation formula, where the target center of gravity position Gtarget includes the target horizontal axis Xtarget, target vertical axis Ytarget, and target longitudinal axis Ztarget of the target center of gravity position.

[0113] The S3 includes S31;

[0114] S31. By analyzing the influence of the forward path of the robot on the posture and load of the robot through the collected feature set E and the state evaluation vector B, calculate and obtain the posture adjustment vector △D for the robot to adaptively adjust, including the horizontal axis position offset △X, vertical axis position offset △Y, longitudinal axis position offset △Z, pitch angle adjustment amount △Pitch, roll angle adjustment amount △Roll, and heading angle adjustment amount △Yaw;

[0115] The horizontal axis position offset △X is obtained through the following calculation formula:

[0116] ;

[0117] In the formula, k1 represents the first proportionality coefficient, which is specifically used to control the influence of the ground height change on the horizontal axis position offset △X, Zg(x, y) represents the height value at the position (x, y), (Zg(x, y) - Zg(x - 1, y) represents the height difference at the current path position, reflecting the change of the front and rear ground, and Gd(x, y) represents the slope change value at the position (x, y), which is used to represent the slope difference between the current position and the adjacent position on the path, and adjusts the adjustment required for the robot in the forward direction through this parameter;

[0118] The vertical axis position offset △Y is obtained through the following calculation formula:

[0119] ;

[0120] In the formula, k2 represents the second proportionality coefficient, which is specifically used to adjust the influence of the height difference of the path on the vertical axis position offset △Y axis offset;

[0121] The longitudinal axis position offset △Z is obtained through the following calculation formula:

[0122] ;

[0123] In the formula, k3 represents the third proportionality coefficient, specifically used to control the influence degree of the obstacle on the offset amount △Z of the vertical axis position, and ZAbs represents the obstacle identifier;

[0124] Among them, the first proportionality coefficient k1, the second proportionality coefficient k2, and the third proportionality coefficient k3 are related to the offset of the robot's center of gravity (for example, the adjustment amount of the position error), usually in the range of [0.1, 1], and the specific value is set by the user;

[0125] The pitch angle adjustment amount △Pitch is obtained through the following calculation formula:

[0126] ;

[0127] In the formula, k4 and k7 respectively represent the fourth proportionality coefficient and the seventh proportionality coefficient, specifically used to adjust the influence of the offset amount on the pitch angle adjustment amount △Pitch;

[0128] The roll angle adjustment amount △Roll is obtained through the following calculation formula:

[0129] ;

[0130] In the formula, k5 and k8 respectively represent the fifth proportionality coefficient and the eighth proportionality coefficient, specifically used to adjust the influence of the offset amount on the roll angle adjustment amount △Roll;

[0131] The yaw angle adjustment amount △Yaw is obtained through the following calculation formula:

[0132] ;

[0133] In the formula, k6 and k9 respectively represent the sixth proportionality coefficient and the ninth proportionality coefficient, specifically used to adjust the influence of the offset amount on the yaw angle adjustment amount △Yaw;

[0134] Among them, the fourth proportionality coefficient k4, the fifth proportionality coefficient k5, and the sixth proportionality coefficient k6 are related to the attitude angles (pitch, roll, yaw) of the robot and the path characteristics (such as slope, obstacles, undulations, etc.), usually in the range of [0.1, 1], and the specific value is set by the user;

[0135] The seventh proportionality coefficient k7, the eighth proportionality coefficient k8, and the ninth proportionality coefficient k9 are related to the response sensitivity of the load, path, and attitude changes, usually in the range of [0.05, 0.5], and the specific value is set by the user;

[0136] For example, when there are large vertical undulations in the path, the influence of the vertical axis position offset ΔZ on the pitch angle adjustment ΔPitch is significant. The fourth proportionality coefficient k4 can be set to 0.8, while for the adjustment of the horizontal axis position offset ΔX, a relatively low coefficient, the seventh proportionality coefficient k7 = 0.3, can be set to avoid over-adjustment.

[0137] In this embodiment, by integrating an accelerometer, a force sensor, a gyroscope, an acceleration sensor, and a magnetometer sensor, the load state, the center of gravity position, and the attitude angle of the robot can be accurately obtained. Furthermore, the forward attitude of the robot can be dynamically calculated and adjusted. In particular, by obtaining the load mass of each component, calculating and optimizing the current center of gravity position G of the robot, comparing it with the target center of gravity position Gtarget, and obtaining the center of gravity offset ΔG, it effectively guides the robot to maintain balance on uneven ground. For example, in response to factors such as the height change, slope difference, and obstacle position of the path, the robot can reasonably adjust its attitude angle to maintain stability. By using different proportionality coefficients, flexible responses to factors such as path changes, load offsets, and attitude adjustments are achieved, effectively avoiding over-adjustment or under-adjustment and ensuring the smooth operation of the robot. This method not only improves the adaptability of the robot to complex environments but also greatly enhances its running stability on dynamic and changing ground, further optimizing the attitude control system of the robot. Embodiment 4

[0138] This embodiment is an explanatory description based on Embodiment 3. Please refer to Figure 1 , specifically: The S4 includes S41;

[0139] S41. The robot adjusts the forward attitude of the current robot based on the obtained attitude adjustment vector ΔD. Specifically, by converting the horizontal axis position offset ΔX, vertical axis position offset ΔY, vertical axis position offset ΔZ, pitch angle adjustment ΔPitch, roll angle adjustment ΔRoll, and yaw angle adjustment ΔYaw in the attitude adjustment vector ΔD into control commands, and through the internal motion control system of the robot acting on the mechanical components to change the forward attitude of the robot, the adjusted execution vector W is obtained, and a control command is generated for the execution vector W to dynamically adjust the forward attitude of the robot and mark the current center of gravity position G of the robot;

[0140] The execution vector W is obtained through the following conversion formula:

[0141] ;

[0142] In the formula, f represents a conversion function preset by the control system, which is specifically used to convert the vectors in the attitude adjustment vector ΔD into specific control signals.

[0143] The S5 includes S51;

[0144] S51. By statistically analyzing the fluctuation information of the center of gravity position G of the robot within a fixed period, obtaining the change amount of the center of gravity position ΔZ, comparing it with the preset center of gravity offset threshold Zthe for the robot's operation, obtaining the evaluation result of the center of gravity offset for the current robot, and performing an iterative optimization mechanism based on the evaluation result of the center of gravity offset;

[0145] The change amount of the center of gravity position ΔZ is obtained through the following calculation formula:

[0146] ;

[0147] In the formula, M represents the total number of samples within a fixed period, t(j + 1, G) represents the center of gravity position at the (j + 1)-th sampling time t, and t(j, G) represents the center of gravity position at the j-th sampling time t;

[0148] The evaluation result of the center of gravity offset for the operation is obtained through the following comparison method:

[0149] When the change amount of the center of gravity position ΔZ ≥ the center of gravity offset threshold Zthe for the robot's operation, the evaluation result of the center of gravity offset for the operation is obtained as an abnormal result, triggering an iterative optimization mechanism, including obtaining an attitude adjustment vector ΔD for adjustment, performing center of gravity position correction and attitude angle correction, specifically by adjusting the first proportional coefficient k1 to the ninth proportional coefficient k9;

[0150] When the change amount of the center of gravity position ΔZ < the center of gravity offset threshold Zthe for the robot's operation, the evaluation result of the center of gravity offset for the operation is obtained as a non-abnormal result.

[0151] In this embodiment, by precisely controlling the posture adjustment vector △D of the robot, it is ensured that the robot can always adapt to the changes in the ground and load in real time during forward movement. By converting each posture adjustment amount in the posture adjustment vector △D into specific control signals, the mechanical components are adjusted through the motion control system inside the robot, thereby realizing the dynamic adjustment of the forward posture of the robot. This flexible adjustment method enables the robot to maintain stable operation in complex and dynamic environments, improving the adaptability and stability of the system. In addition, by statistically analyzing the fluctuating changes in the center of gravity position of the robot within a fixed period, the change amount △Z of the center of gravity position is obtained and compared with the preset center of gravity offset threshold Zthe, so as to evaluate the offset of the operating center of gravity of the robot. When it is detected that the change amount △Z of the center of gravity position exceeds the preset threshold, the system will trigger an iterative optimization mechanism to further adjust the posture adjustment vector △D, and optimize the forward path of the robot by correcting the center of gravity position and posture angle. This mechanism can timely respond to abnormal states, ensure the continuous and stable operation of the robot under heavy loads and complex environments, and effectively avoid the problem of posture instability caused by load changes or path mutations. Through dynamic adjustment and optimization, the adaptability and operation efficiency of the robot in various complex terrains are improved. Embodiment 5

[0152] An operating posture control system for a heavy-load robot, please refer to Figure 2 , specifically: including a forward path acquisition module, a machine state acquisition module, a posture analysis and adjustment module, an adjustment generation and marking module, and an iterative optimization module;

[0153] The forward path acquisition module integrates a laser scanner and a depth camera sensor on the robot to obtain real-time data of the ground of the current forward path of the robot in real time, and records it as the acquisition feature set E after acquisition and analysis;

[0154] The machine state acquisition module obtains the load distribution of the robot by establishing a connection with the monitoring system integrated inside the robot, and calculates and obtains the center of gravity offset △G of the current robot, and outputs the state evaluation vector B of the robot;

[0155] The posture analysis and adjustment module analyzes the influence of the ground of the forward path of the robot on the posture and load of the robot through the acquisition feature set E and the state evaluation vector B, and calculates and obtains the posture adjustment vector △D for the robot to adaptively adjust;

[0156] The adjustment generation and marking module adjusts the forward posture of the current robot based on the obtained posture adjustment vector △D, obtains the adjusted execution vector W, generates a control command for the execution vector W to dynamically adjust the forward posture of the robot and mark the center of gravity position G of the current robot;

[0157] The iterative optimization module obtains the evaluation result of the running center-of-gravity offset of the current robot by statistically calculating the change amount △Z of the center-of-gravity position of the robot within a fixed period and comparing it with the preset running center-of-gravity offset threshold Zthe of the robot, and performs iterative optimization according to the evaluation result of the running center-of-gravity offset. Embodiment 6

[0158] A storage medium for controlling the running posture of a heavy-load robot, specifically: the storage medium stores a computer program, and when the computer program is executed, a method for controlling the running posture of a heavy-load robot is implemented.

[0159] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and deformations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A running attitude control method for a heavy-load robot, characterized in that: It includes the following steps: S1. By integrating a laser scanner and a depth camera sensor on the robot, real-time data of the ground along the current forward path of the robot is obtained in real time. After collection and analysis, it is recorded as the collection feature set E; S2. By establishing a monitoring system integrated with the robot, the load distribution of the robot is obtained, and the center-of-gravity offset ΔG of the current robot is calculated based on the load status, and the state evaluation vector B of the robot is output; S3. By analyzing the ground along the current forward path of the robot based on the collection feature set E and the state evaluation vector B, the influence on the posture and load of the robot is analyzed, and the posture adjustment vector ΔD for adaptive adjustment of the robot is obtained; S4. The robot adjusts its forward posture based on the obtained posture adjustment vector ΔD, obtains the adjusted execution vector W, and generates a control command for the execution vector W to dynamically adjust the forward posture of the robot and mark the center-of-gravity position G of the current robot; S5. By statistically analyzing the change amount ΔZ of the center-of-gravity position of the robot within a fixed period and comparing it with the preset center-of-gravity offset threshold Zthe for robot operation, the evaluation result of the center-of-gravity offset during the operation of the current robot is obtained, and iterative optimization is performed based on the evaluation result of the center-of-gravity offset during operation.

2. The operating attitude control method for a heavy-load robot according to claim 1, wherein: The above S1 includes S11 and S12; S11. By integrating a laser scanner and a depth camera sensor on the robot, real-time data of the ground along the current forward path of the robot is obtained in real time. The height value Zg(x, y) at the position (x, y), the slope change value Gd(x, y) at the position (x, y), and the obstacle identifier ZAbs at the position (x, y) are obtained to form the ground feature data set G; Among them, the height value Zg(x, y) is obtained by performing matching analysis on the laser scanning position height information Zc(x, y) and the depth camera sensor recognition position height Zl(x, y); S12. By marking the timestamp information and the unique identifier information of the carrier for the ground feature data set G, the collection feature set E of the ground along the forward path of the robot transporting the carrier at time t is formed.

3. The operating attitude control method for a heavy-load robot according to claim 2, characterized in that: The laser scanning position height information Zc(x, y) is obtained through the laser scanning height information measured by the laser scanner in real time, and the depth camera sensor recognition position height Zl(x, y) is obtained through real-time analysis by the depth camera sensor; The height value Zg(x, y) is obtained through the following calculation formula: ； In the formula, max represents the maximum value function, and max(Zc(x, y), Zl(x, y)) represents taking the maximum height value of the laser scanning position height information Zc(x, y) and the depth camera sensor recognition position height Zl(x, y) at the same position (x, y); The slope change value Gd(x, y) is obtained through the following calculation formula: ； In the formula, arctan represents the arctangent function, and dx represents the horizontal distance between the height values at the positions (x, y) and (x + 1, y); The obstacle identifier ZAbs is obtained by comparing it with the preset ground height threshold Zgthe for the forward path of the robot; Specifically, the comparison is made through the following method: ； When the obstacle identification ZAbs ≥ the ground height threshold Zgthe of the robot's forward path, it is determined that the height value of the ground of the robot's forward path at the position (x, y) is abnormal, and the obstacle identification ZAbs = 1 is marked; When the obstacle identification ZAbs < the ground height threshold Zgthe of the robot's forward path, it is determined that the height value of the ground of the robot's forward path at the position (x, y) is normal, and the obstacle identification ZAbs = 0 is marked.

4. The running attitude control method for a heavy-load robot according to claim 3, wherein: The S2 includes S21 and S22; S21. Obtain the load state of the robot by establishing a monitoring system integrated with the robot, including using an accelerometer and a force sensor to cooperate to obtain the load mass of each component of the robot, so as to obtain the center of gravity position G of the robot under the current operating state, and compare it with the preset target center of gravity position Gtarget of the robot to obtain the center of gravity offset △G of the current robot; S22. Integrate the center of gravity offset △G with the operating attitude angle of the robot to output the state evaluation vector B of the robot, where the operating attitude angle includes the pitch angle Pitch, the roll angle Roll and the heading angle Yaw, and is specifically obtained through a gyroscope sensor, an accelerometer sensor and a magnetometer sensor.

5. A running attitude control method for a heavy-load robot according to claim 4, characterized in that: The center of gravity position G is obtained by the following calculation formula: ； In the formula, Xg, Yg and Zg respectively represent the horizontal axis coordinate, the vertical axis coordinate and the longitudinal axis coordinate of the total center of gravity of the robot, n represents the total number of robot components, q(i) represents the mass of the i-th component, and x(i), y(i) and z(i) respectively represent the horizontal axis coordinate, the vertical axis coordinate and the longitudinal axis coordinate of the i-th component; The center-of-gravity offset △G is obtained through a calculation formula, where the target center-of-gravity position Gtarget includes a target horizontal axis Xtarget, a target vertical axis Ytarget, and a target longitudinal axis Ztarget of the target center-of-gravity position.

6. The operating attitude control method for a heavy-load robot according to claim 5, characterized in that: The S3 includes S31; S31. Analyze the influence of the ground of the robot's forward path on the attitude and load of the robot by analyzing the acquisition feature set E and the state evaluation vector B, and calculate and obtain the attitude adjustment vector △D for the robot to adaptively adjust, including the horizontal axis position offset △X, the vertical axis position offset △Y, the longitudinal axis position offset △Z, the pitch angle adjustment amount △Pitch, the roll angle adjustment amount △Roll and the heading angle adjustment amount △Yaw; The horizontal axis position offset △X is obtained by the following calculation formula: ； In the formula, k1 represents the first proportional coefficient, Zg(x, y) represents the height value at the position (x, y), and Gd(x, y) represents the slope change value at the position (x, y); The vertical axis position offset △Y is obtained by the following calculation formula: ； In the formula, k2 represents the second proportional coefficient, which is specifically used to adjust the influence of the height difference of the path on the offset of the vertical axis position △Y axis; The longitudinal axis position offset △Z is obtained by the following calculation formula: ； In the formula, k3 represents the third proportional coefficient, which is specifically used to control the influence degree of the obstacle on the longitudinal axis position offset △Z, and ZAbs represents the obstacle identification; The pitch angle adjustment amount △Pitch is obtained by the following calculation formula: ； In the formula, k4 and k7 respectively represent the fourth proportional coefficient and the seventh proportional coefficient, which are specifically used to adjust the influence of the offset on the pitch angle adjustment amount △Pitch; The roll angle adjustment amount △Roll is obtained by the following calculation formula: ； In the formula, k5 and k8 respectively represent the fifth proportionality coefficient and the eighth proportionality coefficient, which are specifically used to adjust the influence of the offset on the roll angle adjustment amount △Roll; The heading angle adjustment amount △Yaw is obtained through the following calculation formula: ； In the formula, k6 and k9 respectively represent the sixth proportionality coefficient and the ninth proportionality coefficient, which are specifically used to adjust the influence of the offset on the heading angle adjustment amount △Yaw.

7. A running attitude control method for a heavy-load robot according to claim 6, characterized in that: The S4 includes S41; S41. The robot adjusts the forward posture of the current robot based on the obtained posture adjustment vector △D. Specifically, the horizontal axis position offset △X, the vertical axis position offset △Y, the longitudinal axis position offset △Z, the pitch angle adjustment amount △Pitch, the roll angle adjustment amount △Roll, and the heading angle adjustment amount △Yaw in the posture adjustment vector △D are converted into control commands, and the mechanical components are affected through the motion control system inside the robot to change the forward posture of the robot, obtain the adjusted execution vector W, and generate control commands for the execution vector W to dynamically adjust the forward posture of the robot and mark the center of gravity position G of the current robot; The execution vector W is obtained through the following conversion formula: ； In the formula, f represents the conversion function preset by the control system.

8. A running attitude control method for a heavy-load robot according to claim 7, characterized in that: The S5 includes S51; S51. By statistically analyzing the fluctuation change information of the center of gravity position G of the robot within a fixed period, obtain the center of gravity position change amount △Z, compare it with the preset robot operation center of gravity offset threshold Zthe, obtain the current robot operation center of gravity offset evaluation result, and perform an iterative optimization mechanism according to the operation center of gravity offset evaluation result; The center of gravity position change amount △Z is obtained through the following calculation formula: ； In the formula, M represents the total number of samples within a fixed period, t(j + 1, G) represents the center of gravity position at the sampling time t of the (j + 1)-th time, and t(j, G) represents the center of gravity position at the sampling time t of the j-th time; The operation center of gravity offset evaluation result is obtained through the following comparison method: When the center of gravity position change amount △Z ≥ the robot operation center of gravity offset threshold Zthe, obtain the operation center of gravity offset evaluation result as an abnormal result, trigger the iterative optimization mechanism, including adjusting the acquisition of the posture adjustment vector △D, performing center of gravity position correction and posture angle correction, specifically by adjusting the first proportionality coefficient k1 to the ninth proportionality coefficient k9; When the center of gravity position change amount △Z < the robot operation center of gravity offset threshold Zthe, obtain the operation center of gravity offset evaluation result as no abnormal result.

9. An operating attitude control system for a heavy-load robot, which is applied to an operating attitude control method for a heavy-load robot according to any one of claims 1-8, characterized in that: It includes a forward path acquisition module, a machine state acquisition module, a posture analysis and adjustment module, an adjustment generation and marking module, and an iterative optimization module; The forward path acquisition module integrates a laser scanner and a depth camera sensor on the robot to real-time obtain the real-time data of the ground of the current robot's forward path, and records it as the acquisition feature set E after acquisition and analysis; The machine state acquisition module obtains the load distribution of the robot by establishing a connection with the monitoring system integrated inside the robot, calculates and obtains the center of gravity offset amount △G of the current robot, and outputs the state evaluation vector B of the robot; The attitude analysis and adjustment module analyzes the influence of the ground on the attitude and load of the robot in the forward path of the robot by analyzing the acquired feature set E and the state evaluation vector B, and calculates and obtains the attitude adjustment vector △D for the robot's adaptive adjustment; The adjustment generation and marking module adjusts the forward attitude of the current robot based on the obtained attitude adjustment vector △D, obtains the adjusted execution vector W, generates a control command for the execution vector W to dynamically adjust the forward attitude of the robot and mark the center of gravity position G of the current robot; The iterative optimization module obtains the evaluation result of the center of gravity offset of the current robot's operation by statistically analyzing the change amount △Z of the center of gravity position of the robot within a fixed period and comparing it with the preset center of gravity offset threshold Zthe of the robot's operation, and performs iterative optimization according to the evaluation result of the center of gravity offset of the operation.

10. A storage medium for controlling the running attitude of a heavy-load robot: The storage medium stores a computer program, and when the computer program is executed, it implements the method for controlling the running attitude of a heavy-load robot according to any one of claims 1 to 8 above.

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