Robot navigation device based on mechanical visual positioning

Abstract

The invention relates to the technical field of robot navigation, in particular to a robot navigation device based on mechanical visual positioning, which is used for solving the problems that the positioning and navigation precision of an existing visual navigation method is greatly reduced under a complex environment, and even the robot navigation device cannot work normally. The robot navigation device comprises a mechanical vision acquisition module, an image analysis module, a vision adjustment module, a map positioning module, a path planning module and a robot navigation module, the robot navigation device can collect high-quality image information under different illumination conditions, has high environmental adaptability, and improves the navigation reliability of a robot under a complex illumination environment; the robot navigation device can intelligently select the robot navigation path, has a real-time path adjustment function, can re-plan the path in time according to the change of the environment, ensures the safety and feasibility of the path, and enables the robot to adapt to different working environments and task requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of robot navigation, and in particular to a robot navigation device based on mechanical visual positioning. Background Art

[0002] With the rapid development of technology, robots are increasingly being used in various fields. Mobile robots are particularly popular in areas such as industrial automation and logistics warehousing. Mobile robots need to accurately locate themselves and plan their paths to complete tasks such as material handling and cargo sorting.

[0003] Robot navigation technology is a key technology for robots to achieve autonomous movement and task execution. Common robot navigation methods mainly use visual navigation methods. Visual navigation usually uses camera-based image information and image processing algorithms for positioning and navigation. However, traditional visual navigation methods have high requirements for environmental conditions. In complex environments, positioning and navigation accuracy will drop significantly, or even fail to work properly, seriously affecting the robot's navigation reliability and safety. Therefore, the development of a robot navigation device based on mechanical visual positioning is of great practical significance.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the Invention

[0005] In order to overcome the above-mentioned technical problems, the purpose of the present invention is to provide a robot navigation device based on mechanical visual positioning, which solves the problem that the existing visual navigation method has high requirements on environmental conditions, the positioning and navigation accuracy will be greatly reduced in complex environments, and it may even fail to work normally, seriously affecting the navigation reliability and safety of the robot.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A robot navigation device based on mechanical visual positioning includes the following modules:

[0008] A map positioning module is used to perform path planning after receiving a map positioning instruction, obtain a path sub-area Hi-Zj, and send the path sub-area Hi-Zj to a path planning module;

[0009] A path planning module is used to obtain an analysis path o based on the path sub-area Hi-Zj, obtain path optimization information of the analysis path o, and send the path optimization information to the image analysis module; wherein the path optimization information includes road length information, obstacle information, and height information;

[0010] An image analysis module is used to obtain a path optimization coefficient according to the path optimization information, obtain a robot navigation path according to the path optimization coefficient, and send the robot navigation path to the robot navigation module;

[0011] The specific process of the image analysis module obtaining the robot navigation path is as follows:

[0012] Scaling the road length information according to the corresponding scaling ratio to obtain road length adjustment information;

[0013] Scaling the obstacle information according to the corresponding scaling ratio to obtain obstacle adjustment information;

[0014] Scaling the height information according to the corresponding scaling ratio to obtain height adjustment information;

[0015] Obtain the inverse of the sum of the road length adjustment information, the obstacle adjustment information, and the height adjustment information, and mark it as the path optimization coefficient;

[0016] Sort all analyzed paths o in descending order according to the path optimization coefficient, mark the first analyzed path o as the robot navigation path, and send the robot navigation path to the robot navigation module;

[0017] The robot navigation module is used to control the robot to travel according to the robot navigation path.

[0018] As a preferred embodiment of the present invention, the specific process of the map positioning module obtaining the path sub-area Hi-Zj is as follows:

[0019] After receiving the map positioning command, the robot's current position is obtained and marked as the positioning point, and the robot's target position is obtained and marked as the target point;

[0020] Connect the positioning point and the target point with a line segment, mark the connected line segment as the positioning-target line, and magnify the positioning-target line according to a preset multiple to obtain the midpoint of the area;

[0021] Use the midline of the area as the center line of the rectangular area, draw the rectangular area according to the preset aspect ratio, and mark it as the path planning area;

[0022] The path planning area is divided into several square areas according to the preset area, and they are labeled as path sub-areas Hi-Zj in sequence; where Hi represents the i-th path sub-area in the horizontal direction, and i = 1, ..., n, n is a positive integer, i is the number of the horizontal path sub-area, and n is the total number of horizontal path sub-areas; where Zj represents the j-th path sub-area in the vertical direction, and j = 1, ..., m, m is a positive integer, j is the number of the vertical path sub-area, and m is the total number of vertical path sub-areas;

[0023] Send all path sub-regions Hi-Zj to the path planning module.

[0024] As a preferred embodiment of the present invention, the specific process of the path planning module obtaining the path optimization information is as follows:

[0025] The path sub-region Hi-Zj where the positioning point is located is taken as the starting point, and the path sub-region Hi-Zj where the target point is located is taken as the end point;

[0026] Starting from the starting point, the adjacent path sub-regions Hi-Zj are sequentially connected until they are connected to the end point, forming several movement paths, which are marked as analysis paths o in sequence; where o = 1, ..., k, k is a positive integer, o is the number of any analysis path, and k is the total number of analysis paths;

[0027] Get the total length of the analysis path o and mark it as the path length information;

[0028] Obtain the total number of path subregions Hi-Zj containing obstacles in the analysis path o and mark them as the obstacle value. Obtain the total volume of all obstacles and mark it as the obstacle body value. Obtain the heights of all obstacles and mark the maximum height as the obstacle height value. Draw an elliptical cylinder using the obstacle value as the height of the elliptical cylinder, and the obstacle body value and obstacle height values as the semi-major axis and semi-minor axis of the base ellipse of the elliptical cylinder, respectively. Mark the volume of the elliptical cylinder as the obstacle information.

[0029] Get the maximum altitude and minimum altitude in the analysis path o, obtain the difference between the two, and mark it as altitude information;

[0030] The road length information, obstacle information and height information are sent to the image analysis module.

[0031] As a preferred embodiment of the present invention, the robot navigation device based on mechanical visual positioning further includes:

[0032] The mechanical visual acquisition module is used to obtain visual acquisition information and send the visual acquisition information to the image analysis module; wherein the visual acquisition information includes light intensity information and color difference information.

[0033] As a preferred embodiment of the present invention, the specific process of the mechanical visual acquisition module acquiring visual acquisition information is as follows:

[0034] Obtain the light intensity of the camera shooting area on the robot and the preset standard light intensity, obtain the difference between the two, and mark it as light intensity information;

[0035] Get the average color RGB value of the preset area in the image captured by the camera and the preset standard color RGB value, obtain the color deviation value between the two, and mark it as color difference information; the color deviation value can be calculated in the following way: set the average color RGB value to R1G1B1 and the standard color RGB value to R0G0B0,

[0036] The light intensity information and color difference information are sent to the image analysis module.

[0037] As a preferred embodiment of the present invention, the image analysis module is further configured to generate a light intensity adjustment instruction and a color difference adjustment instruction based on the visual acquisition information, and send the light intensity adjustment instruction and the color difference adjustment instruction to the visual adjustment module.

[0038] As a preferred embodiment of the present invention, the specific process of the image analysis module generating the light intensity adjustment instruction and the color difference adjustment instruction is as follows:

[0039] The light intensity information is compared with the preset light intensity analysis threshold. The comparison results are as follows:

[0040] If the light intensity information is greater than or equal to the light intensity analysis threshold, a light intensity adjustment instruction is generated and sent to the visual adjustment module;

[0041] Compare the color difference information with the preset color difference analysis threshold. The comparison results are as follows:

[0042] If the color difference information is greater than or equal to the color difference analysis threshold, a color difference adjustment instruction is generated and sent to the visual adjustment module;

[0043] Scaling the light intensity information according to the corresponding scaling ratio to obtain light intensity adjustment information;

[0044] Scaling the color difference information according to the corresponding scaling ratio to obtain color difference adjustment information;

[0045] Obtain the sum of the light intensity adjustment information and the color difference adjustment information, and mark it as the lighting analysis value;

[0046] The daylighting analysis value is compared with the preset daylighting analysis threshold. The comparison results are as follows:

[0047] If the lighting analysis value is greater than or equal to the lighting analysis threshold, a light intensity adjustment instruction and a color difference adjustment instruction are generated and sent to the visual adjustment module.

[0048] As a preferred embodiment of the present invention, the robot navigation device based on mechanical visual positioning further includes:

[0049] The visual adjustment module is used to perform visual adjustment according to the light intensity adjustment instruction and the color difference adjustment instruction. After the adjustment is completed, a map positioning instruction is generated and sent to the map positioning module.

[0050] As a preferred embodiment of the present invention, the specific process of the visual adjustment module performing visual adjustment is as follows:

[0051] After receiving the light intensity adjustment command, the light intensity is compared with the preset standard light intensity. The comparison results are as follows: if the light intensity is less than the standard light intensity, the fill light is controlled to start and the brightness of the fill light is adjusted until the light intensity is equal to the standard light intensity; if the light intensity is greater than the standard light intensity, the aperture is controlled to be narrowed to reduce the amount of light entering the lens until the light intensity is equal to the standard light intensity;

[0052] After receiving the color difference adjustment command, adjust the color of the fill light until the average color RGB value of the preset area in the image is equal to the preset standard color RGB value;

[0053] After the adjustment is completed, a map positioning instruction is generated and sent to the map positioning module.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] The robot navigation device can collect and analyze lighting data in real time, and make intelligent adjustments based on the analysis results. It can collect high-quality image information under different lighting conditions, has strong environmental adaptability, and improves the navigation reliability of the robot in complex lighting environments; the robot navigation device can collect and analyze the analysis path in real time, and intelligently select the robot navigation path based on the analysis results. It has a real-time path adjustment function, can re-plan the path in time according to environmental changes, accurately estimate and optimize the robot's position, ensure the safety and feasibility of the path, enable the robot to adapt to different working environments and task requirements, have strong environmental adaptability, and ensure that the robot can reach the target location safely and efficiently. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] To facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0057] Figure 1 This is a principle block diagram of a robot navigation device based on mechanical vision positioning in the present invention.

[0058] Figure 2 This is a flow chart of a working method of a robot navigation device based on mechanical vision positioning according to the present invention. DETAILED DESCRIPTION

[0059] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0060] Example 1:

[0061] See also Figure 1 As shown, this embodiment is a robot navigation device based on mechanical visual positioning, which includes the following modules: a mechanical vision acquisition module, an image analysis module, a vision adjustment module, a map positioning module, a path planning module and a robot navigation module;

[0062] The mechanical visual acquisition module is used to obtain visual acquisition information and send the visual acquisition information to the image analysis module; wherein the visual acquisition information includes light intensity information and color difference information;

[0063] The image analysis module is used to generate a light intensity adjustment instruction and a color difference adjustment instruction according to the visual acquisition information, and send the light intensity adjustment instruction and the color difference adjustment instruction to the visual adjustment module; it is also used to obtain a path optimization coefficient according to the path optimization information, and obtain a robot navigation path according to the path optimization coefficient, and send the robot navigation path to the robot navigation module;

[0064] The visual adjustment module is used to perform visual adjustment according to the light intensity adjustment instruction and the color difference adjustment instruction, generate a map positioning instruction after the adjustment is completed, and send the map positioning instruction to the map positioning module;

[0065] The map positioning module is used to perform path planning after receiving the map positioning instruction, obtain the path sub-area Hi-Zj, and send the path sub-area Hi-Zj to the path planning module;

[0066] The path planning module is used to obtain the analysis path o according to the path sub-area Hi-Zj, obtain the path optimization information of the analysis path o, and send the path optimization information to the image analysis module; wherein the path optimization information includes road length information, obstacle information and height information;

[0067] The robot navigation module is used to control the robot to travel according to the robot navigation path.

[0068] Example 2:

[0069] See also Figure 2 As shown, this embodiment is a working method of a robot navigation device based on mechanical visual positioning, comprising the following steps:

[0070] Step 1: The mechanical visual acquisition module obtains visual acquisition information, wherein the visual acquisition information includes light intensity information and color difference information, and sends the visual acquisition information to the image analysis module;

[0071] Step 2: The image analysis module generates a light intensity adjustment instruction and a color difference adjustment instruction based on the visual acquisition information, and sends the light intensity adjustment instruction and the color difference adjustment instruction to the visual adjustment module;

[0072] Step 3: The visual adjustment module performs visual adjustment according to the light intensity adjustment instruction and the color difference adjustment instruction. After the adjustment is completed, a map positioning instruction is generated and sent to the map positioning module;

[0073] Step 4: After receiving the map positioning instruction, the map positioning module performs path planning, obtains the path sub-area Hi-Zj, and sends the path sub-area Hi-Zj to the path planning module;

[0074] Step 5: The path planning module obtains the analysis path o based on the path sub-area Hi-Zj and obtains the path optimization information of the analysis path o, where the path optimization information includes road length information, obstacle information, and height information, and sends the path optimization information to the image analysis module;

[0075] Step 6: The image analysis module obtains a path optimization coefficient based on the path optimization information, obtains a robot navigation path based on the path optimization coefficient, and sends the robot navigation path to the robot navigation module;

[0076] Step 7: The robot navigation module controls the robot to move along the robot navigation path.

[0077] Example 3:

[0078] Based on any of the above embodiments, embodiment 3 of the present invention is a mechanical visual acquisition module. The function of the mechanical visual acquisition module is to obtain visual acquisition information. The specific process is as follows:

[0079] The mechanical vision acquisition module obtains the light intensity of the camera shooting area on the robot and the preset standard light intensity, obtains the difference between the two, and marks it as light intensity information;

[0080] The mechanical vision acquisition module obtains the average color RGB value of the preset area in the image captured by the camera and the preset standard color RGB value, obtains the color deviation value between the two, and marks it as color difference information; the color deviation value can be calculated in the following way: set the average color RGB value to R1G1B1 and the standard color RGB value to R0G0B0,

[0081] The mechanical vision acquisition module sends the light intensity information and color difference information to the image analysis module.

[0082] Example 4:

[0083] Based on any of the above embodiments, embodiment 4 of the present invention is an image analysis module, which has two functions:

[0084] One of its functions is to generate light intensity adjustment instructions and color difference adjustment instructions. The specific process is as follows:

[0085] The image analysis module compares the light intensity information with the preset light intensity analysis threshold. The comparison results are as follows:

[0086] If the light intensity information is greater than or equal to the light intensity analysis threshold, a light intensity adjustment instruction is generated and sent to the visual adjustment module;

[0087] The image analysis module compares the color difference information with the preset color difference analysis threshold. The comparison results are as follows:

[0088] If the color difference information is greater than or equal to the color difference analysis threshold, a color difference adjustment instruction is generated and sent to the visual adjustment module;

[0089] The image analysis module scales the light intensity information according to the corresponding scaling ratio to obtain light intensity adjustment information; wherein the scaling ratio corresponding to the light intensity information is set by the user based on experience and is not zero;

[0090] The image analysis module scales the color difference information according to a corresponding scaling ratio to obtain color difference adjustment information; wherein the scaling ratio corresponding to the color difference information is set by the user based on experience and is not zero;

[0091] The image analysis module obtains the sum of the light intensity adjustment information and the color difference adjustment information, and marks it as the lighting analysis value;

[0092] The image analysis module compares the lighting analysis value with the preset lighting analysis threshold. The comparison results are as follows:

[0093] If the lighting analysis value is greater than or equal to the lighting analysis threshold, a light intensity adjustment instruction and a color difference adjustment instruction are generated and sent to the visual adjustment module;

[0094] The second function is to obtain the robot navigation path. The specific process is as follows:

[0095] The image analysis module scales the road length information according to a corresponding scaling ratio to obtain road length adjustment information; wherein the scaling ratio corresponding to the road length information is set by the user based on experience and is not zero;

[0096] The image analysis module scales the obstacle information according to a corresponding scaling ratio to obtain obstacle adjustment information; wherein the scaling ratio corresponding to the obstacle information is set by the user based on experience and is not zero;

[0097] The image analysis module scales the height information according to a corresponding scaling ratio to obtain height adjustment information; wherein the scaling ratio corresponding to the height information is set by the user based on experience and is not zero;

[0098] The image analysis module obtains the inverse of the sum of the road length adjustment information, the obstacle adjustment information, and the height adjustment information, and marks it as the path optimization coefficient;

[0099] The image analysis module sorts all the analysis paths o in descending order according to the path optimization coefficient, marks the analysis path o at the first place as the robot navigation path, and sends the robot navigation path to the robot navigation module.

[0100] Example 5:

[0101] Based on any of the above embodiments, embodiment 5 of the present invention is a visual adjustment module. The function of the visual adjustment module is to generate a map positioning instruction. The specific process is as follows:

[0102] After receiving the light intensity adjustment command, the visual adjustment module compares the light intensity with the preset standard light intensity. The comparison results are as follows:

[0103] If the light intensity is less than the standard light intensity, the fill light is controlled to start and the brightness of the fill light is adjusted until the light intensity is equal to the standard light intensity;

[0104] If the light intensity is greater than the standard light intensity, the aperture is controlled to decrease, reducing the amount of light entering the lens until the light intensity is equal to the standard light intensity;

[0105] After receiving the color difference adjustment instruction, the visual adjustment module adjusts the color of the fill light until the average color RGB value of the preset area in the image is equal to the preset standard color RGB value;

[0106] After the visual adjustment module completes the adjustment, it generates a map positioning instruction and sends the map positioning instruction to the map positioning module.

[0107] Example 6:

[0108] Based on any of the above embodiments, embodiment 6 of the present invention is a map positioning module. The function of the map positioning module is to obtain the path sub-area Hi-Zj. The specific process is as follows:

[0109] After receiving the map positioning command, the map positioning module obtains the robot's current position and marks it as the positioning point, obtains the robot's target position and marks it as the target point;

[0110] The map positioning module connects the positioning point and the target point with a line segment, marks the connected line segment as the positioning-target line, and magnifies the positioning-target line according to a preset multiple to obtain the midpoint of the area;

[0111] The map positioning module uses the midline of the area as the center line of the rectangular area, draws the rectangular area according to the preset aspect ratio, and marks it as the path planning area;

[0112] The map positioning module divides the path planning area into several square areas according to the preset area, and marks them as path sub-areas Hi-Zj in sequence; wherein Hi represents the i-th path sub-area in the horizontal direction, and i=1, ..., n, n is a positive integer, i is the number of the horizontal path sub-area, and n is the total number of horizontal path sub-areas; wherein Zj represents the j-th path sub-area in the vertical direction, and j=1, ..., m, m is a positive integer, j is the number of the vertical path sub-area, and m is the total number of vertical path sub-areas;

[0113] The map positioning module sends all path sub-areas Hi-Zj to the path planning module.

[0114] Example 7:

[0115] Based on any of the above embodiments, embodiment 7 of the present invention is a path planning module. The function of the path planning module is to obtain path optimization information. The specific process is as follows:

[0116] The path planning module takes the path sub-region Hi-Zj where the positioning point is located as the starting point and the path sub-region Hi-Zj where the target point is located as the end point;

[0117] The path planning module connects adjacent path sub-regions Hi-Zj in sequence starting from the starting point until they are connected to the end point, forming several moving paths, and marks them in sequence as analysis paths o; wherein o=1, ..., k, k is a positive integer, o is the number of any analysis path, and k is the total number of analysis paths; wherein each path sub-region Hi-Zj contains at most 8 adjacent path sub-regions Hi-Zj, for example, the adjacent path sub-regions Hi-Zj of the path sub-region H6-Z5 are path sub-region H5-Z4, path sub-region H6-Z4, path sub-region H7-Z4, path sub-region H5-Z5, path sub-region H7-Z5, path sub-region H5-Z6, path sub-region H6-Z6, and path sub-region H7-Z6 respectively;

[0118] The path planning module obtains the total length of the analysis path o and marks it as the path length information;

[0119] The path planning module obtains the total number of path sub-areas Hi-Zj containing obstacles in the analyzed path o and marks them as the obstacle value. It also obtains the total volume of all obstacles and marks it as the obstacle value. It obtains the height of all obstacles and marks the maximum height as the obstacle height. It draws an elliptical cylinder using the obstacle value as the height of the elliptical cylinder and the obstacle value and obstacle height as the semi-major axis and semi-minor axis of the base ellipse of the elliptical cylinder, respectively. It then marks the volume of the elliptical cylinder as the obstacle information.

[0120] The path planning module obtains the maximum altitude and minimum altitude in the analysis path o, obtains the difference between the two, and marks it as altitude information;

[0121] The path planning module sends the road length information, obstacle information and height information to the image analysis module.

[0122] Example 8:

[0123] Based on any of the above embodiments, embodiment 8 of the present invention is a robot navigation module, and the function of the robot navigation module is to receive the robot navigation path and control the robot to travel according to the robot navigation path.

[0124] Based on the above embodiments 1-8, the working principle of the present invention is as follows:

[0125] A robot navigation device based on mechanical visual positioning, which obtains visual acquisition information through a mechanical visual acquisition module, generates light intensity adjustment instructions and color difference adjustment instructions according to the visual acquisition information through an image analysis module, performs visual adjustment according to the light intensity adjustment instructions and the color difference adjustment instructions through the visual adjustment module, generates a map positioning instruction after the adjustment is completed, performs path planning after receiving the map positioning instruction through the map positioning module, obtains a path sub-area, obtains an analysis path according to the path sub-area through the path planning module, and obtains path optimization information of the analysis path, obtains a path optimization coefficient according to the path optimization information through the image analysis module, and obtains a robot navigation path according to the path optimization coefficient, and controls the robot to move along the robot navigation path through the robot navigation module. The robot navigation device can collect and analyze lighting data in real time, and make intelligent adjustments based on the analysis results. It can collect high-quality image information under different lighting conditions, has strong environmental adaptability, and improves the navigation reliability of the robot in complex lighting environments. The robot navigation device can collect and analyze the analysis path in real time, and intelligently select the robot navigation path based on the analysis results. It has a real-time path adjustment function, can re-plan the path in time according to environmental changes, accurately estimate and optimize the robot's position, ensure the safety and feasibility of the path, enable the robot to adapt to different working environments and task requirements, have strong environmental adaptability, and ensure that the robot can reach the target position safely and efficiently.

[0126] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0127] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in similar ways. As long as they do not deviate from the invention or exceed the scope defined in this application, they should all fall within the scope of protection of the present invention.

Claims

1. A robot navigation device based on mechanical visual positioning, characterized in that: Includes the following modules: A map positioning module is used to perform path planning after receiving a map positioning instruction, obtain a path sub-area Hi-Zj, and send the path sub-area Hi-Zj to a path planning module; A path planning module is used to obtain an analysis path o based on the path sub-area Hi-Zj, obtain path optimization information of the analysis path o, and send the path optimization information to the image analysis module; wherein the path optimization information includes road length information, obstacle information, and height information; An image analysis module is used to obtain a path optimization coefficient according to the path optimization information, obtain a robot navigation path according to the path optimization coefficient, and send the robot navigation path to the robot navigation module; The specific process of the image analysis module obtaining the robot navigation path is as follows: Scaling the road length information according to the corresponding scaling ratio to obtain road length adjustment information; Scaling the obstacle information according to the corresponding scaling ratio to obtain obstacle adjustment information; Scaling the height information according to the corresponding scaling ratio to obtain height adjustment information; Obtain the inverse of the sum of the road length adjustment information, the obstacle adjustment information, and the height adjustment information, and mark it as the path optimization coefficient; Sort all analyzed paths o in descending order according to the path optimization coefficient, mark the first analyzed path o as the robot navigation path, and send the robot navigation path to the robot navigation module; The robot navigation module is used to control the robot to travel according to the robot navigation path.

2. A robot navigation device based on mechanical visual positioning according to claim 1, characterized in that: The specific process of the map positioning module obtaining the path sub-area Hi-Zj is as follows: After receiving the map positioning command, the robot's current position is obtained and marked as the positioning point, and the robot's target position is obtained and marked as the target point; Connect the positioning point and the target point with a line segment, mark the connected line segment as the positioning-target line, and magnify the positioning-target line according to a preset multiple to obtain the midpoint of the area; Use the midline of the area as the center line of the rectangular area, draw the rectangular area according to the preset aspect ratio, and mark it as the path planning area; The path planning area is divided into several square areas according to the preset area, and they are marked as path sub-areas Hi-Zj in sequence; Send all path sub-regions Hi-Zj to the path planning module.

3. The robot navigation device based on mechanical visual positioning according to claim 1, characterized in that: The specific process of the path planning module obtaining the path optimization information is as follows: The path sub-region Hi-Zj where the positioning point is located is taken as the starting point, and the path sub-region Hi-Zj where the target point is located is taken as the end point; Starting from the starting point, the adjacent path sub-areas Hi-Zj are sequentially connected until they are connected to the end point, forming several moving paths, which are marked as analysis paths o in turn; Get the total length of the analysis path o and mark it as the path length information; Obtain the total number of path subregions Hi-Zj containing obstacles in the analysis path o and mark them as the obstacle value. Obtain the total volume of all obstacles and mark it as the obstacle body value. Obtain the heights of all obstacles and mark the maximum height as the obstacle height value. Draw an elliptical cylinder using the obstacle value as the height of the elliptical cylinder, and the obstacle body value and obstacle height values as the semi-major axis and semi-minor axis of the base ellipse of the elliptical cylinder, respectively. Mark the volume of the elliptical cylinder as the obstacle information. Get the maximum altitude and minimum altitude in the analysis path o, obtain the difference between the two, and mark it as altitude information; The road length information, obstacle information and height information are sent to the image analysis module.

4. The robot navigation device based on mechanical visual positioning according to claim 1, characterized in that: Also includes: The mechanical visual acquisition module is used to obtain visual acquisition information and send the visual acquisition information to the image analysis module; wherein the visual acquisition information includes light intensity information and color difference information.

5. The robot navigation device based on mechanical visual positioning according to claim 4, characterized in that: The specific process of the mechanical visual acquisition module acquiring visual acquisition information is as follows: Obtain the light intensity of the camera shooting area on the robot and the preset standard light intensity, obtain the difference between the two, and mark it as light intensity information; Obtain the average color RGB value of a preset area in the image captured by the camera and the preset standard color RGB value, obtain the color deviation value between the two, and mark it as color difference information; The light intensity information and color difference information are sent to the image analysis module.

6. The robot navigation device based on mechanical visual positioning according to claim 1, characterized in that: The image analysis module is further configured to generate a light intensity adjustment instruction and a color difference adjustment instruction based on the visual acquisition information, and send the light intensity adjustment instruction and the color difference adjustment instruction to the visual adjustment module.

7. The robot navigation device based on mechanical visual positioning according to claim 6, characterized in that: The specific process of the image analysis module generating the light intensity adjustment instruction and the color difference adjustment instruction is as follows: The light intensity information is compared with the preset light intensity analysis threshold. The comparison results are as follows: If the light intensity information is greater than or equal to the light intensity analysis threshold, a light intensity adjustment instruction is generated and sent to the visual adjustment module; Compare the color difference information with the preset color difference analysis threshold. The comparison results are as follows: If the color difference information is greater than or equal to the color difference analysis threshold, a color difference adjustment instruction is generated and sent to the visual adjustment module; Scaling the light intensity information according to the corresponding scaling ratio to obtain light intensity adjustment information; Scaling the color difference information according to the corresponding scaling ratio to obtain color difference adjustment information; Obtain the sum of the light intensity adjustment information and the color difference adjustment information, and mark it as the lighting analysis value; The daylighting analysis value is compared with the preset daylighting analysis threshold. The comparison results are as follows: If the lighting analysis value is greater than or equal to the lighting analysis threshold, a light intensity adjustment instruction and a color difference adjustment instruction are generated and sent to the visual adjustment module.

8. The robot navigation device based on mechanical visual positioning according to claim 1, characterized in that: Also includes: The visual adjustment module is used to perform visual adjustment according to the light intensity adjustment instruction and the color difference adjustment instruction. After the adjustment is completed, a map positioning instruction is generated and sent to the map positioning module.

9. The robot navigation device based on mechanical visual positioning according to claim 8, characterized in that: The specific process of the visual adjustment module performing visual adjustment is as follows: After receiving the light intensity adjustment command, the light intensity is compared with the preset standard light intensity. The comparison results are as follows: if the light intensity is less than the standard light intensity, the fill light is controlled to start and the brightness of the fill light is adjusted until the light intensity is equal to the standard light intensity; if the light intensity is greater than the standard light intensity, the aperture is controlled to be narrowed to reduce the amount of light entering the lens until the light intensity is equal to the standard light intensity; After receiving the color difference adjustment command, adjust the color of the fill light until the average color RGB value of the preset area in the image is equal to the preset standard color RGB value; After the adjustment is completed, a map positioning instruction is generated and sent to the map positioning module.

Images