Control system of multi-foot heavy-load platform suitable for field complex terrains

Abstract

The invention discloses a control system of a multi-legged heavy-load platform suitable for field complex terrains, belongs to the field of intelligent control of multi-legged heavy-load platforms, and solves the problem of poor all-weather walking control effect of the multi-legged heavy-load platform under the field complex terrains. The system comprises a visual detection module, a real-time map generation module and a motion control module, the visual detection module is used for collecting a field environment image and environment information, performing color space correction on the field environment image by using the environment information, and performing obstacle detection on the corrected field environment image; the path planning module is used for planning a path capable of crossing an obstacle by taking the real-time position of the multi-legged heavy-load platform as a starting point and the detected position of the obstacle as an end point when the obstacle is detected, so as to obtain a path planning optimization result; and the motion control module is used for generating a control signal of the multi-foot heavy-load platform according to the path planning optimization result and controlling the operation of the multi-foot heavy-load platform by using the control signal.

Description

Technical Field

[0001] The invention belongs to the field of intelligent control of multi-legged heavy-load platforms, and in particular relates to a control system of a multi-legged heavy-load platform suitable for outdoor complex terrain. Background Art

[0002] When a multi-legged heavy-load platform works in a complex terrain environment in the wild, the real-time interaction between the visual inspection system and the platform control system is crucial to the stability of the platform and the efficiency of task completion. However, the existing multi-legged heavy-load platform has the following shortcomings, resulting in its poor performance in field applications.

[0003] On the one hand, when operating in the field, the multi-legged heavy-duty platform is often affected by complex lighting and bad weather. For the visual inspection system, uneven lighting or bad weather will lead to poor visual inspection results, making it difficult to guide the platform to move normally in the field. Therefore, the all-weather walking ability of the multi-legged heavy-duty platform is a technical problem that needs to be solved at this stage.

[0004] On the other hand, the existing path planning algorithm is based on random sampling, which may affect the path generation results and lead to unstable path quality. At the same time, the path planning algorithm focuses on how to cross obstacles. However, for multi-legged heavy-load platforms, in some scenarios, it is possible to cross obstacles. How to optimize the path planning results of multi-legged heavy-load platforms is an urgent problem to be solved. Summary of the invention

[0005] In view of the above analysis, an embodiment of the present invention aims to provide a control system for a multi-legged heavy-load platform suitable for complex outdoor terrain, so as to solve the problem of poor all-weather walking control effect of the multi-legged heavy-load platform in complex outdoor terrain.

[0006] The present invention provides a control system for a multi-legged heavy-load platform suitable for outdoor complex terrain, the system comprising a visual detection module, a path planning module and a motion control module; wherein: The visual detection module is used to collect field environment images and environmental information, use the environmental information to perform color space correction on the field environment images, and perform obstacle detection on the corrected field environment images; The path planning module is used to plan a path that can cross the obstacle when an obstacle is detected, taking the real-time position of the multi-legged heavy-load platform as the starting point and the position of the detected obstacle as the end point, and obtain the path planning optimization result; A motion control module, used to generate a control signal of the multi-legged heavy-load platform according to the path planning optimization result, and use the control signal to control the operation of the multi-legged heavy-load platform; The visual inspection module includes a color correction unit; The color correction unit is used to obtain the normalized radiation intensity according to the environmental information, and use the normalized radiation intensity to perform color space correction on the outdoor environment image to obtain a corrected outdoor environment image.

[0007] On the basis of the above scheme, the present invention also makes the following improvements: Further, the environmental information includes atmospheric turbidity and direct angle of the sun under the current atmospheric environment; the normalized radiation intensity is obtained according to the environmental information, and the following is performed: According to the atmospheric turbidity under the current atmospheric environment, the current radiation wavelength is obtained , Current radiation wavelength The original radiation energy of the incident atmosphere , the transmission factor at the current radiation wavelength , surface energy reflectivity , surface energy absorption rate and the spectral reflectance of the obstructing object ;at this time: Radiation wavelength The atmosphere absorbs energy It is expressed as: (1) Radiation wavelength The ground below reflects the energy of the sky light It is expressed as: (2) in, represents the direct angle of the sun, is the Boltzmann constant; Radiation wavelength The radiation intensity under It is expressed as: (3) is the response function of the camera; According to the maximum and minimum values ​​of the radiation intensity at different radiation wavelengths, the radiation wavelength The radiation intensity under Min-Max normalization is performed to obtain the normalized radiation intensity.

[0008] Furthermore, the color space correction of the outdoor environment image is performed using the normalized radiation intensity to obtain the corrected outdoor environment image. The following steps are performed: Each element in the outdoor environment image is subjected to difference calculation with the normalized radiation intensity, and the elements with negative difference calculation results are set to zero, so as to obtain the corrected outdoor environment image.

[0009] Further, the visual detection module also includes an obstacle detection unit; The obstacle detection unit is used to perform image enhancement and edge detection on the corrected field environment image in sequence, and to perform obstacle detection and terrain type recognition on the edge detection result of the field environment image by using a template matching method to recognize the terrain type in the field environment image; when an obstacle is detected, the obstacle type and the size characteristics of the obstacle are also recognized.

[0010] Further, in the path planning module, execute: Obtain the real-time position of the multi-legged heavy-load platform through the GPS positioning system , obtain the position of obstacles through LiDAR , constructed from arrive The initial path of From arrive The initial path is optimized and obstacle crossing detection is performed in turn to obtain a path regularization optimization result that can cross obstacles.

[0011] Further, construct from arrive The initial path is: Initialize a tree with A random tree with a root node Perform multiple rounds of random sampling, generating a new node in each round until the new node reaches or approaches the position of the obstacle; As the starting point, connect all newly generated nodes in sequence. As the end point, a set of path points is formed, thereby constructing a path from the starting point To the location of the obstacle The initial path.

[0012] Furthermore, in each round of random sampling, a new node is generated by selecting a random point from the real-time field environment map space. , and find the random point in the random tree The closest node , and then Towards Expand in the direction of ; The newly generated node and connected to form branches of random trees.

[0013] Further, from arrive To optimize the initial path, execute: Connect the starting points on the initial path and end point , forming a straight line segment L; respectively calculating the vertical distance from each node in the path point set to the straight line L, if the vertical distance is greater than or equal to the threshold, retain the node; otherwise, remove the node; thus obtaining an updated path point set and constructing an optimized path.

[0014] Furthermore, the optimized path is tested for obstacles by executing: Compare the maximum crossing height and width of the multi-legged heavy-load platform with the size characteristics of the obstacle to determine whether the multi-legged heavy-load platform can successfully cross the obstacle. If it is possible, an obstacle crossing strategy is generated based on the maximum crossing height and width of the multi-legged heavy-load platform and the size characteristics of the obstacle, and the optimized path together with the obstacle crossing strategy is used as the path planning optimization result; Otherwise, the path is abandoned and the initial path is regenerated until the path planning optimization result is generated.

[0015] Furthermore, the motion control module includes a control signal generating unit, a gait generating unit and an execution control unit; wherein, A control signal generating unit, used for generating a control signal of the multi-legged heavy-load platform according to the path planning optimization result; A gait generation unit, used to obtain a matching gait scheme by integrating the control signal, the terrain type and the load of the multi-legged heavy-load platform; The execution control unit is used to adjust the operation of the motion control mechanism of each leg in the multi-legged heavy-load platform according to the gait plan.

[0016] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects: The control system of the multi-legged heavy-load platform suitable for outdoor complex terrain provided by the present invention can use the collected environmental information to correct the outdoor environment image, and perform obstacle detection on this basis, so as to ensure that the obstacle detection result eliminates the influence of bad weather, and can achieve relatively accurate obstacle detection around the clock. At the same time, the present invention optimizes the preliminary path planning results by optimizing the path planning process, and on this basis, judges whether the multi-legged heavy-load platform can cross obstacles during the movement, and re-plans the path when it cannot cross obstacles, and finally controls the movement of the multi-legged heavy-load platform accordingly. The above-mentioned system solves the problem of poor all-weather walking control effect of the multi-legged heavy-load platform under outdoor complex terrain through the functional optimization of multiple modules and the cooperation between them.

[0017] In the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not to be considered as limiting the present invention. In the entire drawings, the same reference symbols represent the same components; Figure 1 A schematic diagram of the structure of a control system of a multi-legged heavy-load platform suitable for outdoor complex terrain provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0020] A specific embodiment of the present invention discloses a control system for a multi-legged heavy-load platform suitable for outdoor complex terrain, the structural schematic diagram of which is shown in FIG. Figure 1 The platform includes a visual inspection module, a path planning module and a motion control module. The functions of each module and their interaction are described in detail below.

[0021] (1) Visual inspection module The visual detection module is used to collect field environment images and environmental information, use the environmental information to perform color space correction on the field environment images, and perform obstacle detection on the corrected field environment images.

[0022] Preferably, in this embodiment, the visual detection module includes a color correction unit and an obstacle detection unit, and the functions of each unit are described in detail as follows.

[0023] 1) Color correction unit The color correction unit is used to obtain the normalized radiation intensity according to the environmental information, and use the normalized radiation intensity to perform color space correction on the outdoor environment image to obtain a corrected outdoor environment image.

[0024] In the specific implementation process, this embodiment uses an all-weather camera to collect field environment images and environmental information at the same time. The collected environmental information includes: atmospheric turbidity and direct sunlight angle under the current atmospheric environment.

[0025] According to the atmospheric turbidity under the current atmospheric environment, the current radiation wavelength (radiation wavelength affected by the atmosphere) and the original radiation energy of the incident atmosphere at the current radiation wavelength can be calculated. The transmission factor, surface energy reflectivity, surface energy absorption rate and spectral reflectivity of obstacle objects at the current radiation wavelength can also be obtained by looking up the table. Atmospheric turbidity describes the scattering and absorption effect of the amount and type of suspended particles (such as dust and aerosols) in the atmosphere on solar radiation. Specifically, the radiation wavelength affected by the atmosphere can be determined according to the following formulas (1)-(3).

[0026] (1) (2) in, Indicates the radiation intensity after being affected by the atmosphere; represents the wavelength of radiation not affected by the atmosphere, represents the intensity of solar radiation waves not affected by the atmosphere, and Can be queried; is the optical path length factor, which is related to the direct angle of the sun Related; is the wavelength of radiation Atmospheric optical thickness, turbidity coefficient and Angstrom Index It is determined based on the atmospheric turbidity under the current atmospheric environment, reflecting the concentration and particle size distribution of aerosols. Based on formulas (1) and (2), it can be calculated , then, Planck's law can be used to inversely solve the wavelength of radiation affected by the atmosphere . Planck's law formula is expressed as: (3) in, is Planck's constant, is the Boltzmann constant, Indicates temperature in Kelvin (absolute temperature).

[0027] Afterwards, based on the above information obtained through calculation and table lookup, the radiation intensity at the current radiation wavelength can be calculated. For the specific process, see formulas (4)-(6).

[0028] Radiation wavelength The atmosphere absorbs energy It is expressed as: (4) in, The radiation wavelength The original radiation energy of the incident atmosphere under The radiation wavelength The transmission factor under the condition of atmospheric pressure can characterize the degree of penetration of radiation energy by the atmosphere.

[0029] Radiation wavelength The ground below reflects the energy of the sky light It is expressed as: (5) in, represents the direct angle of the sun, The radiation wavelength The surface energy reflectivity under Indicates the wavelength of radiation The surface energy absorption rate under the ground.

[0030] Radiation wavelength The radiation intensity under It is expressed as: (6) in, The radiation wavelength Spectral reflectance of the obstacle object below; is the response function of the camera, which is used to convert the actual illumination or scene brightness (light source intensity) into pixel values ​​on the image.

[0031] It should be noted that, in this embodiment, It is the radiation intensity obtained after being affected by the atmosphere, and the radiation wavelength at this time is calculated by Planck's law , radiation wavelength The radiation intensity after being absorbed and reflected by the surface is expressed as .

[0032] According to the maximum and minimum values ​​of the radiation intensity at different radiation wavelengths (usually known or obtained through experiments or data statistics), the radiation wavelength The radiation intensity under Min-Max normalization is performed to obtain the normalized radiation intensity.

[0033] The color space correction of the outdoor environment image is performed using the normalized radiation intensity to obtain a corrected outdoor environment image. In the specific implementation process, each element in the outdoor environment image can be subtracted from the normalized radiation intensity, and the elements with negative subtraction results are set to zero, thereby obtaining a corrected outdoor environment image.

[0034] In this embodiment, the color correction unit can remove the influence of the outdoor lighting environment and the adverse weather environment in the outdoor environment image based on the atmospheric optics and camera imaging mechanism, thereby obtaining a clear and recognizable outdoor environment image, that is, a corrected outdoor environment image.

[0035] 2) Obstacle detection unit The obstacle detection unit is used to perform image enhancement and edge detection on the corrected field environment image in sequence, and use template matching to perform obstacle detection and terrain type recognition on the edge detection result of the field environment image to recognize the terrain type in the field environment image; when an obstacle is detected, the obstacle type and the size characteristics of the obstacle are also recognized.

[0036] Preferably, in the obstacle detection unit, a histogram equalization method is used to adjust the color distribution of the corrected field environment image, enhance the contrast between the obstacle and the environment, and obtain an enhanced field environment image. Afterwards, edge detection is performed on the enhanced field environment image. In the specific implementation process, the canny algorithm can be used to detect the boundary information of the image from the enhanced field environment image to provide clear contour information, and the edge tracking algorithm can be used to connect the obstacle edge and output the obstacle edge contour line to represent the obstacle contour; the detected obstacle is template matched to obtain the specific obstacle type to guide the walking method of the multi-legged heavy-load platform.

[0037] Preferably, in this embodiment, the terrain category covers a variety of field environment terrains, such as ditches and farmlands, etc. For ditch terrain, it specifically includes ditch slope, water depth and aquatic vegetation, etc. For farmland surrounding terrain, it specifically includes farmland soil types (sand, clay, loam) and crop types, etc. The size characteristics of obstacles mainly include the planar spatial characteristics of obstacles, such as the width, height, sharpness and smoothness of obstacles.

[0038] (2) Path planning module The path planning module is used to plan a path that can cross the obstacle when an obstacle is detected, taking the real-time position of the multi-legged heavy-load platform as the starting point and the position of the detected obstacle as the end point, to obtain the path planning optimization result.

[0039] In this embodiment, the path planning module can integrate the terrain type, the positioning results of the GPS positioning system and the position detection results of the laser radar to generate a real-time field environment map space. The visual detection module obtains clear terrain features and obstacle features. At the same time, the laser radar provides accurate three-dimensional spatial data to identify the spatial distribution, form and dynamic information of surrounding obstacles. Through the point cloud data of the laser radar, the system can obtain the precise location and movement of obstacles; combining image processing with the environmental information provided by the laser radar, it can construct an accurate field environment map to form a real-time field environment map space, which not only shows the location of obstacles, but also accurately reflects the terrain characteristics of different areas.

[0040] At the same time, in this embodiment, the multi-legged heavy-duty platform has a built-in GPS positioning system and a laser radar, and the real-time position of the multi-legged heavy-duty platform is obtained through the GPS positioning system. , obtain the position of obstacles through LiDAR .

[0041] Preferably, this embodiment is constructed by the following method: arrive The initial path: Initialize a tree with A random tree with a root node Perform multiple rounds of random sampling, generating a new node in each round until the new node reaches or approaches the position of the obstacle; As the starting point, connect all newly generated nodes in sequence. As the end point, a set of path points is formed, thereby constructing a path from the starting point To the location of the obstacle The initial path of In each round of random sampling, a new node is generated as follows: Select a random point from the live wild environment map space , and find the random point in the random tree The closest node , and then Towards Expand in the direction of ; The expanded formula is expressed as: (7) in, is the expansion step length; The newly generated node and connected to form branches of random trees.

[0042] From the starting point To the location of the obstacle The initial path is optimized and obstacle crossing detection is performed in turn to obtain a path regularization optimization result that can cross obstacles.

[0043] Preferably, in this embodiment, the path optimization is performed in the following manner: connecting the starting points in the initial path and end point , forming a straight line segment L; calculate the vertical distance from each node in the path point set to the straight line L respectively, if the vertical distance is greater than or equal to the threshold , then the point is considered to be more important to the path and needs to be retained; if the vertical distance is less than the threshold , the node is considered redundant and removed; thus, an updated set of path points is obtained and an optimized path is constructed. In the specific implementation process, the threshold It can be set according to the actual situation. For example, the step size is set to This path optimization method can remove useless nodes in the initial path, make the path smoother, and reduce the computational complexity of path planning.

[0044] Preferably, in this embodiment, obstacle crossing detection is performed in the following manner: the maximum crossing height and width of the multi-legged heavy-load platform are compared with the size characteristics of the obstacle to determine whether the multi-legged heavy-load platform can successfully cross the obstacle. If so, an obstacle crossing strategy is generated based on the maximum crossing height and width of the multi-legged heavy-load platform and the size characteristics of the obstacle, and the optimized path together with the obstacle crossing strategy is used as the path planning optimization result; otherwise, the path is abandoned and the initial path is regenerated until the path planning optimization result is generated.

[0045] Exemplarily, in this embodiment, the obstacle crossing strategy is generated based on the maximum crossing height and width of the multi-legged heavy-load platform and the size characteristics of the obstacle, which can be implemented in a one-step manner. The core of this gait method is to adjust the movement path and support sequence of the legs of the multi-legged heavy-load platform one by one, as well as the selected crossing point and the performed crossing action to ensure the balance of the platform and the crossing ability of the obstacle. The control method of the one-step is described as follows: according to the size characteristics of the obstacle, determine the safe landing point position of the target leg, ensure that the landing point is away from the edge of the obstacle and is located in the stable area; according to the height of the obstacle, plan the lifting height of the single leg, determine the crossing path, including the starting point, the lifting path, and the terminal position; use the PID control method to divide the trajectory into the starting stage (leg lifting), the crossing stage (horizontal movement), and the terminal stage (landing), and use the PID controller to adjust the movement of each stage respectively to control the leg angle and position error; at the target position, use a force sensor to detect whether the landing point is safely in contact with the ground. If the contact is unstable, readjust the landing point.

[0046] Based on the above operations, the path planning module can generate a path planning result that can cross obstacles and optimize travel time and energy consumption according to the location, shape and motion information of obstacles.

[0047] (3) Motion control module The motion control module is used to generate a control signal for the multi-legged heavy-load platform according to the path planning optimization result, and use the control signal to control the operation of the multi-legged heavy-load platform.

[0048] Preferably, in this embodiment, the motion control module includes a control signal generating unit, a gait generating unit and an execution control unit. The specific functions of each unit are described as follows.

[0049] The control signal generating unit is used to generate the control signal of the multi-legged heavy-load platform according to the path planning optimization result. Preferably, the control signal includes the current motion state of the multi-legged heavy-load platform (such as current position, speed, posture, etc.) and the terminal task to be performed (such as travel direction, speed adjustment, gait conversion, etc.).

[0050] The gait generation unit is used to match the gait scheme based on the control signal, the terrain type and the load of the multi-legged heavy-load platform. Preferably, the appropriate gait scheme can also be calculated and generated based on the real-time environmental information, the load of the multi-legged heavy-load platform and the motion control instructions to ensure the stable walking of the multi-legged heavy-load platform under different terrains and load conditions. Preferably, the gait generation not only considers the direction of travel of the platform, but also adjusts the pace according to the ground type, obstacle distribution and foot contact. If the platform is heavily loaded or the ground is uneven, the system will automatically select a more stable gait to avoid platform instability due to too hasty or unstable steps.

[0051] The execution control unit is used to adjust the operation of the motion control mechanism of each leg in the multi-legged heavy-load platform according to the gait scheme. Preferably, the execution control unit adjusts the motion control system of each leg in the multi-legged heavy-load platform according to the gait scheme, and accurately executes the walking instructions of the platform. The execution control unit controls the motor, servo system and drive device of each leg according to the generated gait and motion instructions, accurately controls the motion timing and step amplitude of each leg, and ensures that the platform moves along the predetermined path. The execution control unit also needs to adjust the motion parameters such as speed, stride, etc. in real time to cope with changes in terrain or load.

[0052] In addition, the motion control module may also include a force and position feedback receiving unit, a feedback signal processing unit, and an abnormality detection and adaptive adjustment unit to optimize the moving process of the multi-legged heavy-load platform.

[0053] The force feedback receiving unit is used to receive feedback signals from the foot force sensors in real time and evaluate the load information and ground contact status of each foot. Specifically, during the platform walking process, the force feedback receiving unit collects the mechanical information of the foot contacting the ground in real time through the foot force sensors. This information includes the force conditions of each foot, the inclination angle of the foot, and the posture changes of the platform. The force feedback unit transmits this data to the feedback signal processing unit for analysis and processing.

[0054] The feedback signal processing unit is used to process the feedback signal and adjust the control strategy according to the feedback to ensure that the platform remains stable. For example, if the force on a certain foot is too large or unbalanced, the system will redistribute the load by adjusting the motion control signal to optimize the gait and ensure that the platform remains stable. If there are signs of platform tilt or imbalance, the system will automatically adjust the gait or slow down to prevent further instability.

[0055] The real-time feedback update unit transmits the processed feedback information to the path planning module to dynamically update the path planning of the platform and update the walking status of the platform in real time. For example, if a new obstacle or environmental change (such as uneven ground or new obstacles) is found, the system will correct the path in real time through the dynamic obstacle avoidance mechanism to ensure that the platform always moves along the optimal and safe path.

[0056] The anomaly detection and adaptive adjustment unit can monitor in real time the abnormal situations that may occur during the walking process of the multi-legged heavy-load platform, and automatically make adjustments to ensure that the multi-legged heavy-load platform can travel safely and efficiently in complex environments.

[0057] Through the collaborative work between the above functional units, the control system of the multi-legged heavy-load platform suitable for complex outdoor terrain can provide high-precision and high-stability support in the walking control of the heavy-load platform, and adapt to the dynamic changes of different terrains and loads. At the same time, through continuous signal feedback and updates, the system can achieve closed-loop control to ensure that the platform can travel efficiently and stably in a dynamic environment.

[0058] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0059] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A control system for a multi-legged heavy-load platform suitable for outdoor complex terrain, characterized in that: The system includes a visual detection module, a path planning module and a motion control module; wherein, The visual detection module is used to collect field environment images and environmental information, use the environmental information to perform color space correction on the field environment images, and perform obstacle detection on the corrected field environment images; The path planning module is used to plan a path that can cross the obstacle when an obstacle is detected, taking the real-time position of the multi-legged heavy-load platform as the starting point and the position of the detected obstacle as the end point, and obtain the path planning optimization result; A motion control module, used to generate a control signal of the multi-legged heavy-load platform according to the path planning optimization result, and use the control signal to control the operation of the multi-legged heavy-load platform; The visual inspection module includes a color correction unit; The color correction unit is used to obtain the normalized radiation intensity according to the environmental information, and use the normalized radiation intensity to perform color space correction on the outdoor environment image to obtain a corrected outdoor environment image.

2. The control system of the multi-legged heavy-load platform suitable for outdoor complex terrain according to claim 1 is characterized in that: The environmental information includes atmospheric turbidity and direct angle of the sun under the current atmospheric environment; the normalized radiation intensity is obtained according to the environmental information, and the following is performed: According to the atmospheric turbidity under the current atmospheric environment, the current radiation wavelength is obtained , Current radiation wavelength The original radiation energy of the incident atmosphere , the transmission factor at the current radiation wavelength , surface energy reflectivity , surface energy absorption rate and the spectral reflectance of the obstructing object ; at this time: Radiation wavelength The atmosphere absorbs energy It is expressed as: (1) Radiation wavelength The ground below reflects the energy of the sky light It is expressed as: (2) in, represents the direct angle of the sun, is the Boltzmann constant; Radiation wavelength The radiation intensity under It is expressed as: (3) is the response function of the camera; According to the maximum and minimum values ​​of the radiation intensity at different radiation wavelengths, the radiation wavelength The radiation intensity under Min-Max normalization is performed to obtain the normalized radiation intensity.

3. The control system of the multi-legged heavy-load platform suitable for outdoor complex terrain according to claim 2 is characterized in that: Use the normalized radiation intensity to perform color space correction on the outdoor environment image to obtain the corrected outdoor environment image. Execute: Each element in the outdoor environment image is subjected to difference calculation with the normalized radiation intensity, and the elements with negative difference calculation results are set to zero, so as to obtain the corrected outdoor environment image.

4. The control system of the multi-legged heavy-load platform suitable for outdoor complex terrain according to claim 3 is characterized in that: The visual detection module also includes an obstacle detection unit; The obstacle detection unit is used to perform image enhancement and edge detection on the corrected field environment image in sequence, and to perform obstacle detection and terrain type recognition on the edge detection result of the field environment image by template matching to recognize the terrain type in the field environment image; when an obstacle is detected, the obstacle type and the size characteristics of the obstacle are also recognized.

5. The control system of the multi-legged heavy-load platform suitable for outdoor complex terrain according to claim 4 is characterized in that: In the path planning module, execute: Obtain the real-time position of the multi-legged heavy-load platform through the GPS positioning system , obtain the position of obstacles through LiDAR , constructed from arrive The initial path of From arrive The initial path is optimized and obstacle crossing detection is performed in turn to obtain a path regularization optimization result that can cross obstacles.

6. The control system of the multi-legged heavy-load platform suitable for outdoor complex terrain according to claim 5 is characterized in that: Build from arrive The initial path is: Initialize a tree with is a random tree with root node; Perform multiple rounds of random sampling, generating a new node in each round until the new node reaches or approaches the position of the obstacle; As the starting point, connect all newly generated nodes in sequence. As the end point, a set of path points is formed, thereby constructing a path from the starting point To the location of the obstacle The initial path.

7. The control system of the multi-legged heavy-load platform suitable for outdoor complex terrain according to claim 6 is characterized in that: In each round of random sampling, a new node is generated by selecting a random point from the real-time field environment map space. , and find the random point in the random tree The closest node , and then Towards Expand in the direction of ; The newly generated node and connected to form branches of random trees.

8. The control system of the multi-legged heavy-load platform suitable for outdoor complex terrain according to claim 7 is characterized in that: From arrive To optimize the initial path, execute: Connect the starting points on the initial path and end point , forming a straight line segment L; respectively calculating the vertical distance from each node in the path point set to the straight line L, if the vertical distance is greater than or equal to the threshold, retain the node; otherwise, remove the node; thus obtaining an updated path point set and constructing an optimized path.

9. The control system of the multi-legged heavy-load platform suitable for outdoor complex terrain according to claim 8 is characterized in that: Perform obstacle crossing detection on the optimized path and execute: Compare the maximum crossing height and width of the multi-legged heavy-load platform with the size characteristics of the obstacle to determine whether the multi-legged heavy-load platform can successfully cross the obstacle. If it is possible, an obstacle crossing strategy is generated based on the maximum crossing height and width of the multi-legged heavy-load platform and the size characteristics of the obstacle, and the optimized path together with the obstacle crossing strategy is used as the path planning optimization result; Otherwise, the path is abandoned and the initial path is regenerated until the path planning optimization result is generated.

10. The control system of the multi-legged heavy-load platform suitable for outdoor complex terrain according to claim 9 is characterized in that: The motion control module includes a control signal generating unit, a gait generating unit and an execution control unit; wherein, A control signal generating unit, used for generating a control signal of the multi-legged heavy-load platform according to the path planning optimization result; A gait generation unit, used to obtain a matching gait scheme by integrating the control signal, the terrain type and the load of the multi-legged heavy-load platform; The execution control unit is used to adjust the operation of the motion control mechanism of each leg in the multi-legged heavy-load platform according to the gait plan.

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