Control method for self-adaptively adjusting intelligent manned mobile robot

Abstract

The invention discloses a control method for adaptively adjusting an intelligent manned mobile robot, and relates to the technical field of manned mobile robot control, and the method comprises the following steps: S1, building a map model, and carrying out the main path planning in the map model; s2, performing positioning analysis on the slope in the moving direction by combining the attitude data with the image data; s3, the mechanical parameters are combined with the road surface state and the weight data to carry out slope threshold value influence adaptation analysis; s4, performing branch path analysis according to the image data in combination with the main path and machine parameters, and obtaining passable branch paths; and S5, increasing the distance and combining the adjustment data to select and evaluate each passable branch path, and merging the selected branch paths into the main path. According to the invention, a distinctive path planning strategy is adopted, so that the robot can flexibly cope with a complex and changeable environment, the path is adjusted in time, and smooth travel is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of manned mobile robot control, and specifically, to a control method for an adaptive adjustment intelligent manned mobile robot. Background Art

[0002] In the field of intelligent manned mobile robots, the existing technologies are dedicated to providing travel convenience for people with mobility difficulties, aiming to help users achieve autonomous and efficient movement. Through positioning and navigation technologies, the robot can plan paths in a certain environment and guide the user to the target location.

[0003] Currently, most intelligent manned mobile robots work in relatively simple scenarios with less environmental changes, such as indoor flat and fixed-layout places. However, in actual complex scenarios, the existing robots usually adopt a unified path planning and operation mode, without fully considering individual differences of users, such as the influence of factors like weight and physical condition on driving. This makes the robot unable to provide accurate and comfortable services when facing different user needs, reducing the user experience and restricting its further development in personalized travel services.

[0004] In terms of environmental adaptability and path optimization, when encountering complex road conditions, such as temporary road construction, potholes or slopes, etc., the robot cannot adjust the path in time, resulting in the robot possibly getting into trouble or choosing a poor route, affecting travel efficiency and safety. Therefore, it is necessary to propose a control method for an adaptive adjustment intelligent manned mobile robot.

[0005] The above is the technical cognition and thinking of the inventor of the present invention for understanding the present invention, and it is not necessarily the prior art. Summary of the Invention

[0006] The purpose of the present invention is to provide a control method for an adaptive adjustment intelligent manned mobile robot, so that the manned mobile robot can provide path planning strategies with individual differences for different users.

[0007] To this end, the present invention provides a control method for an adaptive adjustment intelligent manned mobile robot, including the following steps: S1. Establish a map model, obtain the real-time geographical location and the target geographical location of the user, and at the same time perform main path planning in the map model by combining the real-time geographical location with the target geographical location; S2. Collect image data of the moving direction, and at the same time obtain the attitude data of the manned mobile robot, and perform positioning analysis on the slope of the moving direction by combining the attitude data with the image data; S3. Obtain the mechanical parameters of the manned mobile robot, analyze the road surface state through the image data, and at the same time obtain the weight data of the user, and perform influence slope threshold adaptation analysis by combining the mechanical parameters with the road surface state and the weight data; S4. Screen the slopes with driving influence by combining the located slopes with the influence slope threshold, perform branch path analysis according to the image data, the main path and the machine parameters, and eliminate the branch paths of the slopes with driving influence to obtain passable branch paths; S5. Perform increased distance analysis by combining each passable branch path with the main path, and at the same time perform adjustment data analysis by combining each branch path with the attitude data, perform selection evaluation on each branch path by combining the increased distance with the adjustment data, and incorporate the selected branch path into the main path.

[0008] Compared with the prior art, the present invention has the following beneficial effects:

[0009] 1) According to the control method of the present invention, analyze the image data and attitude data of the moving direction, perceive the road surface state and slope information in real time, upload them to the map model to update the data, screen the slopes with driving influence according to this information, and plan passable branch paths, so that the robot can flexibly respond to complex and changeable environments, timely adjust the path, and ensure a smooth journey.

[0010] 2) According to the control method of the present invention, by obtaining the user's weight data, perform influence slope threshold adaptation analysis in combination with mechanical parameters and road surface state, adjust the path planning strategy according to the individual differences of users. For users with a larger weight, the system will plan the route more cautiously and avoid steeper slopes. Set weights according to the user's emphasis on increased distance and seat adjustment, and select the branch path that best meets the user's needs to meet the personalized travel needs of different users.

[0011] In addition to the purposes, features and advantages described above, the present invention has other purposes, features and advantages. The present invention will be further described in detail below with reference to the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The specification drawings constituting a part of this application are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0013] Figure 1 It is a flowchart of the control method for the adaptive adjustment intelligent manned mobile robot of the present invention;

[0014] Figure 2 It is a flowchart of the present invention for obtaining a main path representing the shortest distance between the target geographical location and the real-time geographical location;

[0015] Figure 3 It is a flowchart of the present invention for obtaining all slopes within the visible range of the moving direction;

[0016] Figure 4 It is a flowchart of the present invention for obtaining the influence slope threshold that will cause discomfort to the user according to the analysis result;

[0017] Figure 5 It is a flowchart of the present invention for recording the slopes corresponding to each branch path and the route changes to the main path;

[0018] Figure 6 It is a flowchart of the present invention for updating the main path. Detailed implementation mode

[0019] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0020] Please refer to Figures 1 to 6 , the control method of the adaptive adjustment intelligent manned mobile robot in this embodiment includes the following steps S1 - S6.

[0021] S1. Establish a map model, obtain the real-time geographical location and the target geographical location of the user, and at the same time perform main path planning in the map model by combining the real-time geographical location with the target geographical location.

[0022] In step S1, connect to a commercial map software for map data collection, and establish a map model configured for the manned mobile robot according to the collected map data;

[0023] Connect to the map software for map data collection, collect various map-related data such as road information, building locations, terrain and landforms, etc. According to the collected map data, establish a map model suitable for the configuration of the manned mobile robot. This map model will serve as the basis for subsequent operations such as path planning of the robot.

[0024] In one embodiment, the intelligent manned mobile robot is an intelligent wheelchair, and is equipped with a seat angle tilting device (the function of this device is to be able to change the horizontal angle between the seat and the ground. For example, when encountering a slope or when the sitting posture needs to be adjusted, the seat angle can be adjusted through this device to improve the safety and comfort of sitting), which is used to change the horizontal angle between the seat and the ground, and is also equipped with a shock absorption device (this shock absorption device is used to reduce the vibration impact on the rider caused by uneven road surfaces or other vibration sources during the driving of the robot, and improve the smoothness and comfort of riding) and GPS (through GPS, the real-time geographical location information of the user can be collected, which is crucial for functions such as path planning, real-time positioning, and navigation in combination with the map model of the robot).

[0025] In one embodiment, step S1 includes the following steps S11 and S12:

[0026] S11. Collect the real-time geographical location of the user through GPS. At the same time, before departure, display the map model to the user for target geographical location collection, and determine the target geographical location according to the user's selection on the map model. The specific steps are as follows:

[0027] Initialize the GPS device: Turn on the GPS module of the intelligent manned mobile robot to make it enter the working state and ensure that it can receive satellite signals. This step is the prerequisite for obtaining the real-time geographical location. Only when the GPS device works properly can subsequent data collection be carried out. When the intelligent manned mobile robot starts working, the GPS will enter the working state.

[0028] Real-time geographical location collection: The GPS module continuously receives signals from multiple satellites. By measuring the time difference of the satellite signals propagating to the receiver, the real-time geographical location coordinates of the device (i.e., the intelligent manned mobile robot, which is the location of the user) are calculated using the principle of triangulation, usually represented in the form of longitude and latitude.

[0029] Display the map model: On the control terminal of the intelligent manned mobile robot (such as a display screen or a mobile device connected thereto), load the previously established map model. The map model should clearly display the surrounding environment information, including roads, buildings, landmarks, etc., to provide an intuitive spatial reference for the user.

[0030] Target geographical location collection: Guide the user to operate on the displayed map model. The user can mark the target location they want to reach on the map by touching the screen, using a control handle, etc.

[0031] Target geographical location determination: The system analyzes the location information selected by the user on the map model and converts it into target geographical location coordinates in the same coordinate system as the real-time geographical location, thus completing the process of determining the target geographical location.

[0032] S12. Combine the real-time geographical location with the target geographical location and perform main path planning on the summary result in the map model, so as to obtain a main path representing the shortest distance between the target geographical location and the real-time geographical location. The specific steps are as follows:

[0033] Construct a graph model: Convert the map model into a graph data structure, where the intersections, inflection points, etc. of the roads are used as the nodes of the graph, and the road segments connecting the nodes are used as the edges. Assign corresponding weights to each edge. The weights can be determined according to factors such as actual distance, travel time, road conditions, etc. At the same time, use the Dijkstra algorithm for planning:

[0034] Initialize the distance and predecessor nodes: Set the distance of the starting node (corresponding to the real-time geographical location) to 0, and the distances of other nodes to infinity. At the same time, set the predecessor nodes of all nodes to none.

[0035] Traverse the nodes: Start from the starting node and traverse all its neighbor nodes. For each neighbor node, calculate the distance from the starting node to this neighbor node (by adding the distance of the starting node to the weight of the edge from the starting node to the neighbor node). If the calculated distance is less than the current distance of the neighbor node, then update the distance of the neighbor node to the newly calculated distance, and set the predecessor node of the neighbor node to the starting node.

[0036] Select the next node: Among all the unvisited nodes, select the node with the minimum distance as the next current node, and repeat step (traverse the nodes) until the target node (corresponding to the target geographical location) is visited.

[0037] Generate the main path: Starting from the target node, trace back to the starting node according to the predecessor node information, and the sequence of nodes passed in turn is the main path from the real-time geographical location to the target geographical location.

[0038] S2. Collect image data of the moving direction, and at the same time obtain the attitude data of the manned mobile robot, and combine the attitude data with the image data to perform positioning analysis on the slope of the moving direction.

[0039] In one embodiment, S2 includes the following steps S21 and S22.

[0040] S21. Collect image data of the moving direction of the manned mobile robot, and at the same time online monitor the attitude data of the manned mobile robot.

[0041] Start the image acquisition device (such as a camera) installed on the manned mobile robot, make it aim at the moving direction, and continuously capture images at a certain frame rate (for example, 30 frames per second) to obtain an image sequence of the moving direction of the robot. These images will be used as the basic data for subsequent analysis.

[0042] Use an attitude sensor (such as an inertial measurement unit IMU combined with an accelerometer, gyroscope, magnetometer, etc.) installed on the robot to monitor the attitude data of the robot in real time.

[0043] S22. Combine the image data with the attitude data to perform a positioning analysis on the slopes in the moving direction, and obtain all the slopes within the visible range of the moving direction according to the positioning analysis results. The specific steps are as follows:

[0044] Slope feature extraction: Preprocess the collected images, including but not limited to operations such as grayscale conversion, filtering (such as Gaussian filtering to remove noise), and edge detection (such as using the Canny operator). In the preprocessed images, use image processing algorithms (such as gradient-based methods, Hough transform, etc.) to extract possible slope features. For example, the gradient-based method can calculate the gradient direction and amplitude of each pixel in the image, and identify the edges of the slope by analyzing the gradient information; through the Hough transform, the straight lines in the image can be detected. For slopes, their edges can usually be approximated as straight lines, so the position of the slope can be determined by detecting the straight lines.

[0045] Combined with attitude data analysis: Combine and analyze the extracted slope features with the real-time attitude data. For example, if there is a significant change in the pitch angle of the robot and a straight line or area that conforms to the slope features is detected in the image, it can be inferred that there is a slope ahead. Through the attitude data, information such as the inclination angle and direction of the slope can be further determined.

[0046] Slope positioning: Determine the specific position of the slope within the visible range of the robot's moving direction based on the position of the slope features in the image and the attitude information of the robot. Through the conversion between the image coordinates and the robot coordinate system, map the position of the slope in the image to the actual space position, so as to obtain the position information of all slopes within the visible range of the moving direction.

[0047] Result output: Organize and output the slope information (such as position, inclination angle, range, etc.) obtained from the positioning analysis, providing a basis for subsequent path planning and decision-making. The formula is as follows:

[0048]

[0049] Among them, a x 、a y 、a z are the components of the acceleration measured by the accelerometer on the x, y, and z axes respectively. In the static case, the angle between the gravitational acceleration g and the z-axis direction is the pitch angle θ.

[0050] In step S22, the positioning analysis of the slopes in the moving direction further includes the following steps S221 and S222.

[0051] S221. Upload the collected image data to the map model in real time, and update the map data of the map model.

[0052] Establish a communication connection between the manned mobile robot and the system where the map model is located (such as the server side) to ensure stable data transmission. After the system receives the uploaded image data, it parses and processes the image data. For example, it extracts the geographic feature information in the image (such as the location and shape information of roads, buildings, slopes, etc.), and then integrates the parsed geographic feature information with the existing map model data to update the relevant attribute information of the map model.

[0053] S222: Mark the position of the acquired slope on the map model, annotate the slope's inclination, and provide data for subsequent slope positioning analysis by the manned mobile robot.

[0054] According to the positioning information of the slope in the image data, the corresponding position is found in the map model, and the position of the slope is clearly marked on the map model using a specific marking symbol or color. The slope inclination information previously obtained through posture data and image analysis is associated with the slope position marked in the map model. In the data structure of the map model, an attribute field can be added to each slope mark to store the inclination information. For example, a "slope" field can be added to the database table and the corresponding inclination value can be filled in.

[0055] S3. Obtain the mechanical parameters of the manned mobile robot, analyze the road surface conditions through image data, and simultaneously obtain the user's weight data. Combine the mechanical parameters with the road surface conditions and weight data to analyze the impact on slope threshold adaptation.

[0056] In one embodiment, step S3 includes the following steps S31 and S32.

[0057] S31. Obtain mechanical parameters of the manned mobile robot, analyze the road surface condition through image data, score the road surface condition according to the analysis results, and simultaneously obtain the user's weight data using the sensors of the manned mobile robot.

[0058] S32. Perform an analysis on the slope threshold adaptation effect by combining the mechanical parameters with the road condition score and the weight data, and obtain the slope threshold effect that will cause discomfort to the user based on the analysis result.

[0059] The worse the road condition, the lower the slope threshold; the higher the weight, the lower the slope threshold. The specific steps are as follows:

[0060] Obtaining mechanical parameters: Read the mechanical parameters of the manned mobile robot from its design documents, configuration files, or built-in storage module. These parameters may include wheel radius, motor power, maximum torque, body weight, etc. These parameters are inherent properties of the robot and affect its ability to travel on different road surfaces and slopes.

[0061] Road surface condition analysis: The collected image data is processed to identify road surface characteristics, such as flatness, pothole degree, presence of obstacles, friction coefficient, etc. Based on the identified road surface characteristics, the road surface condition is divided into different levels, such as good, general, poor, severe, etc.

[0062] Pavement condition score: Assign a corresponding score to each pavement condition level. Good pavement condition score is 8 to 10 points, general pavement condition score is 5 to 7 points, poor pavement condition score is 2 to 4 points, and severe pavement condition score is 0 to 1 point. More detailed scoring standards can be formulated based on specific application scenarios and needs.

[0063] Obtaining user weight data: Using the pressure sensor installed on the seat of the manned mobile robot, the pressure applied by the user on the seat is measured in real time, and the user's weight data is calculated based on the relationship between pressure and gravity.

[0064] Analysis of slope threshold adaptation: A model that influences slope threshold adaptation is established. This model takes into account mechanical parameters, road surface condition scores, and weight data. Mechanical parameters, road surface condition scores, and weight data are input into the model and analyzed according to the model's calculation rules. Based on the principle that "the worse the road surface condition, the lower the slope threshold; the higher the weight data, the lower the slope threshold," the model outputs the slope threshold that will cause user discomfort. The formula is as follows:

[0065]

[0066] Among them, M is the comprehensive coefficient of mechanical parameters, r is the wheel radius, P is the motor power, Wr is the vehicle body weight, and α1, α2, and α3 are the corresponding weight coefficients.

[0067]

[0068] Among them, Y θ is the impact slope threshold, S is the basic slope threshold, is the slope threshold under ideal road conditions (S=10), standard weight, and standard mechanical parameters, W u is the user's weight.

[0069] S4. Combine the slopes obtained by positioning with the influence slope threshold for influence screening to obtain slopes with driving influence. Based on the image data, the main path, and the machine parameters, perform branch path analysis to obtain passable branch paths.

[0070] In one embodiment, step S4 includes the following steps S41 and S42.

[0071] S41. Combine the slopes obtained by positioning with the influence slope threshold for influence screening. When the slope of the slope is greater than the influence slope threshold, it is determined that the slope has driving influence and is deleted. Conversely, when the slope of the slope is less than the influence slope threshold, it is determined that the slope has no driving influence and is retained.

[0072] S42. Combine the image data with the main path and machine parameters to perform branch path analysis to obtain branch paths that the manned mobile robot can pass through, and record the slopes corresponding to each branch path and the route changes to the main path.

[0073] The specific steps are as follows:

[0074] Branch path analysis: Prepare the image data (including surrounding environment information), the main path (the initial planned path from the starting point to the target point), and the mechanical parameters of the robot (such as wheel diameter, turning radius, maximum climbing ability, etc.). Based on the image data, identify the passable paths that may exist around the main path. These paths may be small roads, side roads, etc. Image processing and computer vision techniques can be used to identify information such as road boundaries and obstacles in the image, so as to determine potential branch paths.

[0075] Passability assessment: Combine the mechanical parameters of the robot to perform passability assessment on each potential path. For example, consider whether the width of the path can accommodate the robot to pass through, and whether the slope on the path is within the climbing ability range of the robot, etc., and screen out the branch paths that the manned mobile robot can pass through.

[0076] Record information: Record the slope information corresponding to each branch path, including the position and slope of the slope, and analyze the route changes of each branch path relative to the main path. For example, where the branch path separates from the main path, where it rejoins, and information such as the length and direction of the branch path.

[0077] S5. Combine each branch path with the main path for additional distance analysis, and at the same time combine each branch path with the attitude data for adjustment data analysis. Combine the additional distance with the adjustment data to perform selection evaluation on each branch path, and incorporate the selected branch path into the main path.

[0078] In one embodiment, step S5 includes the following steps S51 and S52.

[0079] S51. Combine each branch path with the main path for increased distance analysis to obtain the increased distance of the impact of each branch path on the main path. At the same time, combine each branch path with the attitude data for adjustment data analysis to obtain the adjustment data for the manned mobile robot to adjust the seat during driving. The specific steps are as follows:

[0080] Increased distance analysis: Calculate the total distance of the main path and the total distance after combining each branch path with the main path respectively. The coordinate information in the map model can be used to calculate the length of each segment on the path through the distance formula between two points, and then sum to obtain the total distance. For each branch path, calculate the increased distance relative to the main path. The formula is as follows:

[0081]

[0082]

[0083]

[0084] Among them, the main path consists of n points (x main，j , y main，j ) (j = 1, 2,..., n), and the path after combining the i-th branch path with the main path consists of m points (x combined，i，k , y combined，i，k ) (k = 1, 2,..., m). d is the distance between two points, D main is the main path distance, and D combined，i is the combined path distance;

[0085]

[0086] Among them, ΔD i is the increased distance.

[0087] Adjustment data analysis: Associate each branch path with the previously collected attitude data. The attitude data includes information such as the tilt angle and acceleration of the robot during driving, which can reflect the situation where the seat needs to be adjusted when driving on this branch path. According to the attitude data, analyze the adjustment data for the manned mobile robot to adjust the seat during driving on each branch path. The adjustment data can include the change amount of the tilt angle of the seat and the number of adjustments. The specific formula is as follows:

[0088]

[0089] Among them, A i is the comprehensive index (adjustment data), Δθ i is the change amount of the tilt angle, n i is the number of adjustments, and a and b are the weight coefficients inside the corresponding adjustment data respectively.

[0090] S52. Set weights according to user requirements, and then combine the increased distance with the adjustment data and the weights set by the user to evaluate each branch path. Select the branch path with the highest evaluation value and incorporate it into the main path to update the main path. The specific steps are as follows:

[0091] Weight setting: Interact with the user to understand the user's emphasis on the increased distance and seat adjustment. For example, some users may be more concerned about the driving distance and hope to minimize the extra journey, while some users may be more concerned about the riding comfort and are more sensitive to seat adjustment. According to the user's needs, set weights for the increased distance and adjustment data respectively.

[0092] Selection and evaluation: For each branch path, calculate the evaluation value according to the increased distance, the adjustment data, and the corresponding weights. The evaluation value can comprehensively consider the impact of the increased distance and the adjustment data on the user experience. Compare the evaluation values of all branch paths and select the branch path with the highest evaluation value.

[0093] Main path update: Incorporate the selected branch path into the main path and replace the corresponding part in the main path to obtain the updated main path. The formula is as follows:

[0094]

[0095] Among them, E i is the evaluation value, and w1 and w2 are the weights of the increased distance and the adjustment data respectively.

[0096] The above are only embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A control method for an adaptive adjustment intelligent manned mobile robot, characterized in that, It includes the following steps: S1. Establish a map model, obtain the real-time geographical location of the user and the target geographical location, and at the same time conduct a main path planning in the map model by combining the real-time geographical location with the target geographical location; S2. Collect image data of the moving direction, obtain the attitude data of the manned mobile robot at the same time, and conduct a positioning analysis on the slope in the moving direction by combining the attitude data with the image data; S3. Obtain the mechanical parameters of the manned mobile robot, analyze the road surface condition through the image data, obtain the weight data of the user at the same time, and conduct an influence slope threshold adaptation analysis by combining the mechanical parameters with the road surface condition and the weight data; S4. Screen the slopes with driving influence by combining the slopes obtained by positioning with the influence slope threshold, conduct a branch path analysis according to the image data in combination with the main path and the machine parameters, and obtain the passable branch paths; S5. Conduct an increased distance analysis by combining each passable branch path with the main path, conduct an adjustment data analysis by combining each branch path with the attitude data at the same time, conduct a selection evaluation on each branch path by combining the increased distance with the adjustment data, and incorporate the selected branch path into the main path.

2. The control method of the adaptive adjustment intelligent manned mobile robot according to claim 1, characterized in that In step S1, connect to the map software to collect map data, and establish a map model configured for the manned mobile robot according to the collected map data; The intelligent manned mobile robot is a wheelchair, and is equipped with a seat angle inclination device for changing the horizontal angle between the seat and the ground, and is also equipped with a shock absorption device and a GPS.

3. The control method of the adaptive adjustment intelligent manned mobile robot according to claim 1, characterized in that The steps included in step S1 are as follows: S11. Collect the real-time geographical location of the user through the GPS. At the same time, before departure, display the map model to the user for collecting the target geographical location, and determine the target geographical location according to the user's selection in the map model; S12. Conduct a main path planning on the summary result of the real-time geographical location combined with the target geographical location in the map model, so as to obtain a main path representing the shortest distance between the target geographical location and the real-time geographical location.

4. The control method of the adaptive adjustment intelligent manned mobile robot according to claim 1, wherein, The steps included in step S2 are as follows: S21. Collect image data of the moving direction of the manned mobile robot, and monitor the attitude data of the manned mobile robot online at the same time; S22. Conduct a positioning analysis on the slope in the moving direction by combining the image data with the attitude data, and obtain all the slopes within the visible range of the moving direction according to the positioning analysis result.

5. The control method of the adaptive adjustment intelligent manned mobile robot according to claim 4, characterized in that The positioning analysis of the slope in the moving direction in step S2 further includes the following steps: S221. Upload the collected image data to the map model in real time to update the map data of the map model; S222. Mark the position of the obtained slope in the map model, and attach the inclination degree of the slope at the same time, and provide data for the subsequent slope positioning analysis of the manned mobile robot.

6. The control method of the adaptive adjustment intelligent manned mobile robot according to claim 1, characterized in that, The steps included in step S3 are as follows: S31. Obtain the mechanical parameters of the manned mobile robot, analyze the road surface condition through the image data, score the road surface condition according to the analysis result, and obtain the weight data of the user by using the sensor of the manned mobile robot; S32. Combine the mechanical parameters with the road surface condition score and the weight data to conduct an impact slope threshold adaptation analysis, and obtain the impact slope threshold that may cause discomfort to the user according to the analysis results. Among them, the worse the road surface condition, the lower the impact slope threshold; the higher the weight data, the lower the impact slope threshold.

7. The control method of the adaptive adjustment intelligent manned mobile robot according to claim 6, characterized in that, The calculation formula for the impact slope threshold is as follows: ； where M is the comprehensive coefficient of mechanical parameters, r is the wheel radius, P is the motor power, Wr is the vehicle body weight, and α1, α2, and α3 are the corresponding weight coefficients. ； Among them, Y θ is the influencing slope threshold, S is the basic slope threshold, is the slope threshold under the ideal road surface condition (S = 10), standard body weight and standard mechanical parameters, W u is the user's body weight.

8. The control method of the adaptive adjustment intelligent manned mobile robot according to claim 1, characterized in that, Step S4 includes the following steps: S41. Combine the slope obtained by positioning with the slope threshold for impact screening. When the slope of the slope is greater than the slope threshold, it is determined that the slope has a driving impact. On the contrary, when the slope of the slope is less than the slope threshold, it is determined that the slope has no driving impact. S42. Combine the image data with the main path, machine parameters, and slope threshold for branch path analysis, obtain the branch paths that the manned mobile robot can pass through, and record the slopes corresponding to each branch path and the route changes to the main path.

9. The control method of the adaptive adjustment intelligent manned mobile robot according to claim 1, wherein Step S5 includes the following steps: S51. Combine each branch path with the main path for increased distance analysis to obtain the increased distance of the impact of each branch path on the main path. At the same time, combine each branch path with the attitude data for adjustment data analysis to obtain the adjustment data for the manned mobile robot to adjust the seat during driving for each branch path. S52. Set the weights according to the user's needs, and then combine the increased distance with the adjustment data and the weights set by the user to conduct a selection evaluation of each branch path, select the branch path with the highest evaluation value and incorporate it into the main path to update the main path.

10. The control method of the adaptive adjustment intelligent manned mobile robot according to claim 9, characterized in that, The selection evaluation of each branch path in step S5 includes: ； ； ； Among them, the main path consists of n points (x main，j , y main，j ), (j = 1, 2,..., n). The path formed by combining the i-th branch path with the main path consists of m points (x combined，i，k , y combined，i，k ), (k = 1, 2,..., m). d is the distance between two points, D main is the distance of the main path, and D combined，i is the distance of the combined path; ； where ΔD i is the increased distance; ； Among them, A i is a comprehensive index, Δθ i is the change in the tilt angle, n i is the number of adjustment times, and a and b are the weight coefficients within the corresponding adjustment data respectively; ； Among them, E i is the evaluation value, and w1 and w2 are the weights of the increased distance and the adjusted data respectively.

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