CN116952249B - Autonomous navigation method for wheel type inspection robot - Google Patents
Autonomous navigation method for wheel type inspection robot Download PDFInfo
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Abstract
The invention relates to the technical field of navigation of inspection robots, in particular to an autonomous navigation method of a wheel type inspection robot. By analyzing the first evaluation indexes of the inspection robot corresponding to each navigation path, the influence of the obstacle in the navigation path on the inspection work of the inspection robot can be effectively identified, meanwhile, the difficulty in the inspection work of the inspection robot caused by uneven pavement can be avoided, and the problem of low inspection efficiency caused by the fact that the robot adopts a poor route is avoided to a great extent; the second evaluation indexes of the routing inspection robot corresponding to each path to be selected are analyzed, and then the optimal path corresponding to the routing inspection robot is screened out, so that the time cost waste caused by manually planning the route is made up to a great extent, the intellectualization and automation of the routing inspection robot are effectively improved, the routing inspection efficiency of the robot is greatly improved, the working risk of personnel in environments such as a narrow space is further avoided, and the working safety is improved.
Description
Technical Field
The invention relates to the technical field of navigation of inspection robots, in particular to an autonomous navigation method of a wheel type inspection robot.
Background
The wheel type inspection robot comprises a two-wheel drive wheel type inspection robot, a four-differential wheel type explosion-proof inspection robot and the like; the four-differential wheel type explosion-proof inspection robot is suitable for the situations of narrow space, uneven ground and the like of a coal mine substation and a water pump room, has the characteristics of small size, flexible walking, strong obstacle crossing capability, attractive appearance and the like, can replace manual work to finish daily inspection tasks, effectively avoids the problems of missing inspection, false inspection, fuzzy archiving pictures and the like in manual inspection, and ensures the comprehensiveness and accuracy of inspection work.
The conventional wheel type inspection robot generally needs to mark the inspection path manually, so that corresponding inspection work is performed, the intelligent and automatic autonomous navigation of the inspection robot cannot be effectively guaranteed, the inspection efficiency of the inspection robot is further reduced, meanwhile, the inspection robot cannot predict and analyze obstacles and road surface flatness in the inspection path, and the problems of low inspection efficiency and low inspection quality are easily caused by various external factors in the actual operation process.
Disclosure of Invention
The invention aims to provide an autonomous navigation method of a wheel type inspection robot, so as to solve the problems of the background technology.
The aim of the invention can be achieved by the following technical scheme: the autonomous navigation method of the wheel type inspection robot comprises the following steps:
p1, modeling a region: and constructing a navigation model of the inspection robot corresponding to the inspection area to obtain the navigation model of the inspection robot corresponding to the inspection area.
As a further improvement of the invention, a navigation model of the inspection robot corresponding to the inspection area is constructed, and the specific implementation steps are as follows:
p1-1: acquiring a patrol area corresponding to the patrol robot, and extracting a three-dimensional model of the patrol area corresponding to the patrol robot from the three-dimensional models corresponding to the areas stored in the database to obtain a three-dimensional model of the patrol area corresponding to the patrol robot;
p1-2: collecting the position of the inspection robot corresponding to the current time point, simultaneously obtaining the entrance position of the inspection robot corresponding to the inspection area, obtaining the position of the inspection robot corresponding to the current time point and the entrance position of the inspection area, and constructing a running model of the inspection robot corresponding to the entrance of the inspection area;
p1-3: and forming a navigation model of the inspection robot corresponding to the inspection area by the three-dimensional model of the inspection robot corresponding to the inspection area and the running model of the inspection area entrance.
P2, path generation: and generating each navigation path of the inspection robot corresponding to the inspection area.
P3, preliminary analysis: dividing each sub-path of each navigation path corresponding to the inspection robot, acquiring a pavement model of each sub-path of each navigation path based on a navigation model of an inspection area corresponding to the inspection robot, analyzing the obstacle density and the pavement evenness of each sub-path of each navigation path corresponding to the inspection robot, and further analyzing a first evaluation index of each navigation path corresponding to the inspection robot based on the obstacle density and the pavement evenness of each sub-path of each navigation path corresponding to the inspection robot to obtain a first evaluation index of each navigation path corresponding to the inspection robot, and analyzing each route to be selected corresponding to the inspection robot to obtain each route to be selected corresponding to the inspection robot.
As a further improvement of the invention, the first evaluation index of each navigation path corresponding to the inspection robot is analyzed, and the specific analysis steps are as follows:
p3-1: dividing each navigation path corresponding to the inspection robot into each sub-path according to a set dividing mode, extracting the number of the obstacles of each sub-path in each navigation path corresponding to the inspection robot and the occupied area of each obstacle from a navigation model of the inspection area corresponding to the inspection robot, and analyzing the obstacle concentration of each sub-path in each navigation path corresponding to the inspection robot to obtain the obstacle concentration of each sub-path in each navigation path corresponding to the inspection robot;
p3-2: extracting a road surface model of each sub-path of each navigation path corresponding to the inspection robot from a navigation model of the inspection area corresponding to the inspection robot, and obtaining the road surface model of each sub-path of each navigation path corresponding to the inspection robot;
p3-3: acquiring the road surface center line of each sub-path in each navigation path corresponding to the inspection robot based on the road surface model of each sub-path in each navigation path corresponding to the inspection robot, so as to obtain the road surface center line of each sub-path in each navigation path corresponding to the inspection robot, and acquiring the road surface center point of each sub-path in each navigation path, so as to obtain the road surface center point of each sub-path in each navigation path;
p3-4: uniformly arranging detection points on the road surface central lines of all the sub-paths corresponding to all the navigation paths of the inspection robot to obtain all the detection points of all the sub-paths corresponding to the road surface central lines of all the navigation paths, and setting the detection surfaces of all the sub-paths corresponding to the road surface central points of all the navigation paths to obtain the detection surfaces of all the sub-paths corresponding to the road surface central points of all the navigation paths;
p3-5: obtaining the vertical distance between each detection point of each sub-path corresponding to the central line of the road surface in each navigation path and the corresponding detection surface, marking the vertical distance as the detection distance, obtaining the detection distance between each detection point of each sub-path in each navigation path, and comparing the detection distances between each detection point of each sub-path in each navigation path, thereby obtaining the road surface flatness of each sub-path in each navigation path of the inspection robot through comparison analysis;
p3-6: the path evaluation indexes of all sub-paths in the navigation paths corresponding to the inspection robot are analyzed based on the obstacle concentration degree and the road surface flatness of all the sub-paths in the navigation paths corresponding to the inspection robot, the path evaluation indexes of all the sub-paths in the navigation paths corresponding to the inspection robot are obtained, summation calculation is carried out on the path evaluation indexes, the comprehensive path evaluation indexes of all the sub-paths in the navigation paths corresponding to the inspection robot are obtained, and the comprehensive path evaluation indexes are used as first evaluation indexes of all the navigation paths corresponding to the inspection robot.
As a further improvement of the invention, each route to be selected corresponding to the inspection robot is analyzed, and the specific analysis mode is as follows:
comparing the first evaluation index of each navigation path corresponding to the inspection robot with a set first evaluation index threshold, and if the first evaluation index of a certain navigation path is larger than the set first evaluation index threshold, marking the navigation path as a path to be selected, thereby counting to obtain each path to be selected corresponding to the inspection robot.
P4, secondary analysis: the navigation model corresponding to the inspection area of the inspection robot is used for acquiring the path length, the turning quantity and the turning angle of each turn of the inspection robot corresponding to each path to be selected, and the second evaluation index corresponding to each path to be selected of the inspection robot is analyzed based on the path length, the turning quantity and the turning angle of each turn of the inspection robot corresponding to each path to be selected, so that the second evaluation index corresponding to each path to be selected of the inspection robot is obtained, and meanwhile, the preferred path corresponding to the inspection robot is analyzed, so that the preferred path corresponding to the inspection robot is obtained.
As a further improvement of the invention, the second evaluation index of the inspection robot corresponding to each path to be selected is analyzed, and the specific analysis process is as follows:
p4-1: extracting the path length of each path to be selected corresponding to the inspection robot from the navigation model of the inspection area corresponding to the inspection robot, and obtaining the path length of each path to be selected corresponding to the inspection robot;
p4-2: extracting the turning quantity of the routing inspection robot corresponding to each route to be selected from a navigation model of the routing inspection area corresponding to the routing inspection robot, and acquiring the turning angles of the routing inspection robot corresponding to each turn in each route to be selected, so as to acquire the turning quantity of the routing inspection robot corresponding to each route to be selected and the turning angles of the routing inspection robot corresponding to each turn in each route to be selected;
p4-3: comprehensively analyzing the path length, the turning number and the turning angles of the paths to be selected corresponding to the routing inspection robot to obtain a second evaluation index of the paths to be selected corresponding to the routing inspection robot.
As a further improvement of the invention, the preferred path corresponding to the inspection robot is analyzed, and the specific analysis mode is as follows:
and comparing the second evaluation indexes of the paths to be selected corresponding to the inspection robots, and screening the paths to be selected corresponding to the maximum second evaluation index from the second evaluation indexes to be used as the preferred paths corresponding to the inspection robots.
P5, intelligent patrol: and corresponding inspection is carried out based on the optimized path corresponding to the inspection robot.
The invention has the beneficial effects that:
according to the invention, the current position corresponding to the inspection robot and the entrance position of the inspection area corresponding to the inspection robot are acquired, the running model of the inspection robot corresponding to the entrance of the inspection area is intelligently analyzed, and meanwhile, the three-dimensional model of the inspection area is constructed, so that the navigation model of the inspection robot corresponding to the inspection area is obtained, the intellectualization and automation of the inspection robot are realized, the efficiency of the inspection work corresponding to the follow-up inspection robot is improved, the inspection data precision of the inspection robot is also improved, and more accurate basis is provided for the follow-up analysis and decision.
According to the invention, by generating each navigation path of the inspection robot corresponding to the inspection area, the influence of various factors on the navigation path of the inspection robot is fully considered, the possible missing state during manual inspection is avoided to a certain extent, and the inspection quality is improved.
According to the invention, the obstacle density and the road surface flatness of the navigation paths corresponding to the inspection robot are collected and analyzed, and the first evaluation index of the navigation paths corresponding to the inspection robot is analyzed, so that the influence of the obstacle in the navigation paths on the inspection work of the inspection robot can be effectively identified, meanwhile, the difficulty in the inspection work of the inspection robot caused by the uneven road surface can be avoided, and the problem of low inspection efficiency caused by the fact that the robot adopts an poor route is avoided to a great extent.
According to the invention, each route to be selected corresponding to the inspection robot is obtained based on the analysis of the first evaluation index of each navigation route corresponding to the inspection robot, and the second evaluation index of each route to be selected corresponding to the inspection robot is analyzed, so that the optimal route corresponding to the inspection robot is screened out, the waste of time cost caused by manually planning the route is largely compensated, meanwhile, the intellectualization and automation of the inspection robot are effectively improved, the inspection efficiency of the robot is greatly increased, the working risk of personnel in environments such as narrow space is further avoided, and the working safety is improved.
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The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a flow chart of the method steps of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1:
referring to fig. 1, the invention provides an autonomous navigation method of a wheel inspection robot, comprising:
1. region modeling: constructing a navigation model of the inspection robot corresponding to the inspection area:
and acquiring the inspection area corresponding to the inspection robot, and extracting the three-dimensional model of the inspection area corresponding to the inspection robot from the three-dimensional model corresponding to each area stored in the database to obtain the three-dimensional model of the inspection area corresponding to the inspection robot.
The method comprises the steps of collecting the position of the inspection robot corresponding to the current time point, simultaneously obtaining the entrance position of the inspection robot corresponding to the inspection area, obtaining the position of the inspection robot corresponding to the current time point and the entrance position of the inspection area, and constructing a running model of the inspection robot corresponding to the entrance of the inspection area, wherein the specific construction mode is as follows:
extracting an integral three-dimensional model of the area from the database, marking the position of the inspection robot corresponding to the current time point and the entrance position of the inspection area, and taking the area formed by the position of the inspection robot corresponding to the current time point and the entrance position of the inspection area as a running area to obtain the running area corresponding to the inspection robot, and extracting the three-dimensional model of the running area corresponding to the inspection robot from the integral three-dimensional model of the area to serve as the running model of the entrance of the inspection area corresponding to the inspection robot.
It is noted that the running area corresponding to the inspection robot is specifically: the area where the position of the inspection robot corresponding to the current time point is connected with the area where the entrance position of the inspection robot corresponding to the inspection area is, and the area is amplified according to a set amplification proportion until the amplified area wraps the position of the inspection area, and therefore the amplified area is called a running area, and the running area corresponding to the inspection robot is obtained.
And forming a navigation model of the inspection robot corresponding to the inspection area by the three-dimensional model of the inspection robot corresponding to the inspection area and the running model of the inspection area entrance.
In a specific embodiment, the present invention collects the current position corresponding to the inspection robot and the entrance position corresponding to the inspection area, intelligently analyzes the running model of the inspection robot corresponding to the entrance of the inspection area, and constructs the three-dimensional model of the inspection area, thereby obtaining the navigation model of the inspection robot corresponding to the inspection area, realizing the intellectualization and automation of the inspection robot, improving the efficiency of the inspection work corresponding to the subsequent inspection robot, improving the inspection data precision of the inspection robot, and providing more accurate basis for the subsequent analysis and decision.
2. And (3) path generation: generating each navigation path of the inspection robot corresponding to the inspection area:
and generating each travel path of the inspection robot corresponding to the inspection area based on the travel model of the inspection robot corresponding to the inspection area entrance, and obtaining each travel path of the inspection robot corresponding to the inspection area.
And generating each inspection path of the inspection robot corresponding to the inspection area based on the three-dimensional model of the inspection area corresponding to the inspection robot.
And carrying out non-repeated combination on each running path of the inspection robot corresponding to the inspection area and each inspection path to obtain each navigation path of the inspection robot corresponding to the inspection area. For example: the travel paths are a, b and c, the routing inspection paths are 1, 2 and 3, and the navigation paths obtained after non-repeated combination are as follows: a1, a2, a3, b1, b2, b3, c1, c2, c3.
In a specific embodiment, the navigation paths of the inspection robot corresponding to the inspection area are generated, so that the influence of various factors on the navigation paths of the inspection robot is fully considered, the possible missing state during manual inspection is avoided to a certain extent, and the inspection quality is improved.
3. Preliminary analysis: analyzing a first evaluation index of the inspection robot corresponding to each navigation path, wherein the specific analysis process comprises the following steps:
dividing each navigation path corresponding to the inspection robot into sub paths uniformly according to the set road section length, extracting the number of the obstacles and the occupied area of each obstacle in each navigation path corresponding to the inspection robot from the navigation model of the inspection area corresponding to the inspection robot, summing the occupied areas of each obstacle corresponding to each sub path in each navigation path to obtain the total occupied area of each obstacle in each sub path in each navigation path, normalizing the number of the obstacles and the total occupied area of each sub path in each navigation path, and taking the values of the normalized numbers and the total occupied area of the obstacles, and recording the values as respectivelyI is the number of each navigation path, and j is the number of each sub path.
The path occupation area of each sub-path in each navigation path corresponding to the inspection robot is acquired, normalized and then the value is taken and recorded as the sum of the values。
According to the formulaCalculating the obstacle concentration of each sub-path in each navigation path of the inspection robot>Y1 and y2 are respectively represented as set weight factors.
And extracting the road surface model of each sub-path in each navigation path corresponding to the inspection robot from the navigation model of the inspection area corresponding to the inspection robot, and obtaining the road surface model of each sub-path in each navigation path corresponding to the inspection robot.
The method comprises the steps of obtaining the road surface center line of each sub-path in each navigation path corresponding to the inspection robot based on the road surface model of each sub-path in each navigation path corresponding to the inspection robot, obtaining the road surface center line of each sub-path in each navigation path corresponding to the inspection robot, and obtaining the road surface center point of each sub-path in each navigation path.
And uniformly distributing detection points on the road surface central lines of all the sub-paths corresponding to all the navigation paths of the inspection robot to obtain all the detection points of the road surface central lines corresponding to all the sub-paths in all the navigation paths.
And vertically moving the corresponding road surface center point of each sub-path in each navigation path according to a set vertical direction, enabling the distance between the corresponding road surface center point of each sub-path in each navigation path after movement and the corresponding non-moving road surface center point to be equal to a set reference distance, simultaneously marking the corresponding road surface center point of each sub-path in each navigation path after movement as a reference point, further taking the reference point corresponding to each sub-path in each navigation path as a parallel line, taking the parallel line as a reference line corresponding to each sub-path in each navigation path, and taking the reference line corresponding to each sub-path in each navigation path as a detection surface, so that the corresponding detection surface is parallel to the reference line corresponding to each sub-path in each navigation path, thereby obtaining the detection surface of the corresponding road surface center point of each sub-path in each navigation path.
The vertical distance between each detection point of each sub-path corresponding to the central line of the road surface in each navigation path and the corresponding detection surface is obtained and is recorded as the detection distance, and the detection distance between each detection point of each sub-path corresponding to each sub-path in each navigation path is obtained and is recorded asF is the number of each detection point.
Extracting the maximum detection distance and the minimum detection distance from the detection distances of the corresponding detection points of each sub-path in each navigation path, and respectively marking asAverage value calculation is carried out on the detection distances of the detection points corresponding to the sub-paths in each navigation path, so as to obtain the average detection distances corresponding to the sub-paths in each navigation path, and the average detection distances are marked as +.>。
According to the formulaCalculating the detection distance uniformity of each sub-path corresponding to each detection point in each navigation path>E is expressed as a natural constant, ">The allowable detected distance dispersion is set, and y3, y4, and y5 are set weight factors.
Comparing the detection distance uniformity of each sub-path corresponding to each detection point in each navigation path with the stored reference detection distance uniformity, if the detection distance uniformity of a certain detection point is greater than the reference detection distance uniformity, marking the detection point as a mark point, counting the number of mark points corresponding to each sub-path in each navigation path, and marking asCounting the number of detection points corresponding to each sub-path in each navigation path, and marking the number as +.>。
According to the formulaAnd calculating the road surface flatness of each sub-path in each navigation path corresponding to the inspection robot.
According to the formulaCalculating a path evaluation index ++of each sub-path in each navigation path corresponding to the inspection robot>Y6 and y7 are respectively represented as set weight factors.
And carrying out summation calculation on path evaluation indexes of all sub-paths corresponding to all navigation paths of the inspection robot to obtain comprehensive path evaluation indexes of all sub-paths corresponding to all navigation paths of the inspection robot, wherein the comprehensive path evaluation indexes are used as first evaluation indexes of all navigation paths corresponding to the inspection robot.
Analyzing each path to be selected corresponding to the inspection robot, wherein the specific analysis is as follows: comparing the first evaluation index of each navigation path corresponding to the inspection robot with a set first evaluation index threshold, and if the first evaluation index of a certain navigation path is larger than the set first evaluation index threshold, marking the navigation path as a path to be selected, thereby counting to obtain each path to be selected corresponding to the inspection robot.
In a specific embodiment, the obstacle density and the road surface flatness of the navigation paths corresponding to the inspection robot are collected and analyzed, and therefore the first evaluation index of the navigation paths corresponding to the inspection robot is analyzed, the influence of the obstacle in the navigation paths on the inspection robot inspection work can be effectively identified, meanwhile, the difficulty in the inspection work of the inspection robot caused by the road surface unevenness can be avoided, and the problem that the inspection efficiency is low due to the fact that the robot adopts an bad route is avoided to a great extent.
4. Secondary analysis: analyzing a second evaluation index of each routing inspection robot corresponding to each route to be selected, wherein the specific analysis process comprises the following steps:
and extracting the path length of each path to be selected corresponding to the inspection robot from the navigation model of the inspection area corresponding to the inspection robot, and obtaining the path length of each path to be selected corresponding to the inspection robot.
And extracting the turning quantity of the inspection robot corresponding to each route to be selected from the navigation model of the inspection area corresponding to the inspection robot, and obtaining the turning quantity of the inspection robot corresponding to each route to be selected.
Extracting a path model of each route to be selected corresponding to the inspection robot from a navigation model of the inspection area corresponding to the inspection robot, and performing turning marking on the path model of each route to be selected corresponding to the inspection robot to obtain a marking path of each turn in each route to be selected corresponding to the inspection robot.
Intercepting the bending paths of the inspection robot corresponding to the turns in the paths to be selected from the marking paths of the inspection robot corresponding to the turns in the paths to be selected, obtaining the lengths of the bending paths of the inspection robot corresponding to the turns in the paths to be selected, and recording asN is the number of each candidate path, and m is the number of each turn.
Acquiring the starting point and the end point of each turn corresponding to the camber path in each candidate path based on the camber path of each turn in each candidate path corresponding to the routing inspection robot, connecting the starting point and the end point of each turn in each candidate path in a straight line, marking the straight line as a straight line path, acquiring the straight line path of each turn in each candidate path corresponding to the routing inspection robot, and simultaneously acquiring the length of the straight line path of each turn in each candidate path corresponding to the routing inspection robot, marking the straight line path as。
According to the formulaCalculating the curvature of each turn in each alternative path>And multiplying the curvature of each turn in each path to be selected by a scale factor of the angle corresponding to the set curvature to be used as the turning angle of each turn in each path to be selected corresponding to the inspection robot.
The path length, the number of turns and the turning angles of each turn corresponding to each path to be selected of the inspection robot are respectively recorded as。
According to the formulaCalculating turning angles of the inspection robot corresponding to all turns in all the paths to be selectedDegree evaluation index->D is expressed as a natural constant, ">Expressed as a set reference turning angle +.>Indicated as the set allowable turning angle difference.
Summing the turning angle evaluation indexes of the inspection robot corresponding to each turn in each candidate path to obtain a comprehensive turning angle evaluation index of the inspection robot corresponding to each candidate path, and recording the comprehensive turning angle evaluation index as。
According to the formulaCalculating a second evaluation index of the inspection robot corresponding to each route to be selected>W1, w2, w3 are respectively denoted as set influencing factors, +.>Respectively expressed as a set reference path length, a reference number of turns.
Analyzing the optimized path corresponding to the inspection robot, wherein the specific analysis mode is as follows: and comparing the second evaluation indexes of the paths to be selected corresponding to the inspection robots, and screening the paths to be selected corresponding to the maximum second evaluation index from the second evaluation indexes to be used as the preferred paths corresponding to the inspection robots.
And selecting the optimal paths corresponding to the rejector from the paths corresponding to the paths to be selected by the inspection robot, and extracting the second evaluation indexes of the paths corresponding to the paths to be selected by the inspection robot from the second evaluation indexes of the paths corresponding to the paths to be selected by the inspection robot.
And sequentially arranging the second evaluation indexes of the alternative paths corresponding to the inspection robots in sequence from large to small to obtain the ordering of the alternative paths corresponding to the inspection robots.
In a specific embodiment, the method and the system obtain each route to be selected corresponding to the inspection robot based on the first evaluation index analysis of each navigation route corresponding to the inspection robot, analyze the second evaluation index of each route to be selected corresponding to the inspection robot, and further screen out the preferred route corresponding to the inspection robot, so that the waste of time cost caused by manually planning routes is largely compensated, meanwhile, the intellectualization and automation of the inspection robot are effectively improved, the inspection efficiency of the robot is greatly improved, the working risk of personnel in environments such as narrow spaces is further avoided, and the working safety is improved.
5. Intelligent patrol: and corresponding inspection is carried out based on the optimized path corresponding to the inspection robot.
Example 2:
based on the embodiment 1, the five-intelligent patrol in the autonomous navigation method of the wheel type patrol robot further comprises the following steps:
if a complex situation occurs in the process of the inspection robot inspecting on its corresponding preferred path, the complex situation includes but is not limited to: several pedestrians are present on the preferred path, and unavoidable obstacles are present on the preferred path.
The method comprises the steps of extracting first alternative paths from the sorting of the alternative paths corresponding to the inspection robots to serve as second selection paths corresponding to the inspection robots, and carrying out corresponding inspection based on the second selection paths corresponding to the inspection robots.
It is noted that if the difference between the secondary selection path corresponding to the inspection robot and the current position corresponding to the inspection robot is too large, so that the inspection robot repeatedly inspects, the secondary selection path corresponding to the inspection robot is selected again.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
Claims (6)
1. The autonomous navigation method of the wheel type inspection robot comprises the following steps: z01, region modeling and Z02, path generation, characterized by the further steps of:
z1, preliminary analysis: dividing each sub-path of each navigation path corresponding to the inspection robot based on the navigation model of the inspection area corresponding to the inspection robot and each navigation path, and acquiring the pavement model of each sub-path in each navigation path based on the navigation model of the inspection area corresponding to the inspection robot, so as to analyze the obstacle density and the pavement evenness of each sub-path in each navigation path corresponding to the inspection robot, and further analyze the first evaluation index of each sub-path in each navigation path corresponding to the inspection robot based on the obstacle density and the pavement evenness of each sub-path in each navigation path corresponding to the inspection robot, so as to obtain the first evaluation index of each navigation path corresponding to the inspection robot, so as to analyze each standby path corresponding to the inspection robot, and obtain each standby path corresponding to the inspection robot;
the specific analysis steps for analyzing the first evaluation index of each navigation path corresponding to the inspection robot are as follows:
1-1: dividing each navigation path corresponding to the inspection robot into each sub-path according to a set dividing mode, extracting the number of the obstacles of each sub-path in each navigation path corresponding to the inspection robot and the occupied area of each obstacle from a navigation model of the inspection area corresponding to the inspection robot, and analyzing the obstacle concentration of each sub-path in each navigation path corresponding to the inspection robot to obtain the obstacle concentration of each sub-path in each navigation path corresponding to the inspection robot;
1-2: extracting a road surface model of each sub-path of each navigation path corresponding to the inspection robot from a navigation model of the inspection area corresponding to the inspection robot, and obtaining the road surface model of each sub-path of each navigation path corresponding to the inspection robot;
1-3: acquiring the road surface center line of each sub-path in each navigation path corresponding to the inspection robot based on the road surface model of each sub-path in each navigation path corresponding to the inspection robot, so as to obtain the road surface center line of each sub-path in each navigation path corresponding to the inspection robot, and acquiring the road surface center point of each sub-path in each navigation path, so as to obtain the road surface center point of each sub-path in each navigation path;
1-4: uniformly arranging detection points on the road surface central lines of all the sub-paths corresponding to all the navigation paths of the inspection robot to obtain all the detection points of all the sub-paths corresponding to the road surface central lines of all the navigation paths, and setting the detection surfaces of all the sub-paths corresponding to the road surface central points of all the navigation paths to obtain the detection surfaces of all the sub-paths corresponding to the road surface central points of all the navigation paths;
1-5: obtaining the vertical distance between each detection point of each sub-path corresponding to the central line of the road surface in each navigation path and the corresponding detection surface, marking the vertical distance as the detection distance, obtaining the detection distance between each detection point of each sub-path in each navigation path, and comparing the detection distances between each detection point of each sub-path in each navigation path, thereby obtaining the road surface flatness of each sub-path in each navigation path of the inspection robot through comparison analysis;
1-6: analyzing path evaluation indexes of all sub-paths in the navigation paths corresponding to the routing inspection robot based on the obstacle concentration and the road surface flatness of all sub-paths in the navigation paths corresponding to the routing inspection robot to obtain path evaluation indexes of all sub-paths in the navigation paths corresponding to the routing inspection robot, and summing the path evaluation indexes to obtain comprehensive path evaluation indexes of all sub-paths in the navigation paths corresponding to the routing inspection robot as a first evaluation index of all navigation paths corresponding to the routing inspection robot;
z2, secondary analysis: acquiring the path length, the turning number and the turning angle of each turn of the routing inspection robot corresponding to each path to be selected based on a navigation model of the routing inspection area corresponding to the routing inspection robot, analyzing a second evaluation index of the routing inspection robot corresponding to each path to be selected based on the path length, the turning number and the turning angle of each turn of the routing inspection robot corresponding to each path to be selected, obtaining a second evaluation index of the routing inspection robot corresponding to each path to be selected, and simultaneously analyzing a preferred path corresponding to the routing inspection robot to obtain a preferred path corresponding to the routing inspection robot;
z3, intelligent patrol: and corresponding inspection is carried out based on the optimized path corresponding to the inspection robot.
2. The autonomous navigation method of a wheel inspection robot according to claim 1, wherein the Z01, region modeling constructs a navigation model of an inspection region corresponding to the inspection robot, and specifically comprises the following steps:
01: acquiring a patrol area corresponding to the patrol robot, and extracting a three-dimensional model of the patrol area corresponding to the patrol robot from the three-dimensional models corresponding to the areas stored in the database to obtain a three-dimensional model of the patrol area corresponding to the patrol robot;
02: collecting the position of the inspection robot corresponding to the current time point, simultaneously obtaining the entrance position of the inspection robot corresponding to the inspection area, obtaining the position of the inspection robot corresponding to the current time point and the entrance position of the inspection area, and constructing a running model of the inspection robot corresponding to the entrance of the inspection area;
03: and forming a navigation model of the inspection robot corresponding to the inspection area by the three-dimensional model of the inspection robot corresponding to the inspection area and the running model of the inspection area entrance.
3. The autonomous navigation method of a wheel inspection robot according to claim 1, wherein the Z02 and path generation generates each navigation path of the inspection robot corresponding to the inspection area.
4. The autonomous navigation method of a wheel inspection robot according to claim 1, wherein the analyzing the paths to be selected corresponding to the inspection robot comprises the following specific analysis modes:
comparing the first evaluation index of each navigation path corresponding to the inspection robot with a set first evaluation index threshold, and if the first evaluation index of a certain navigation path is larger than the set first evaluation index threshold, marking the navigation path as a path to be selected, thereby counting to obtain each path to be selected corresponding to the inspection robot.
5. The autonomous navigation method of a wheel inspection robot according to claim 1, wherein the analyzing the second evaluation index of the inspection robot corresponding to each path to be selected comprises the following specific analysis process:
2-1: extracting the path length of each path to be selected corresponding to the inspection robot from the navigation model of the inspection area corresponding to the inspection robot, and obtaining the path length of each path to be selected corresponding to the inspection robot;
2-2: extracting the turning quantity of the routing inspection robot corresponding to each route to be selected from a navigation model of the routing inspection area corresponding to the routing inspection robot, and acquiring the turning angles of the routing inspection robot corresponding to each turn in each route to be selected, so as to acquire the turning quantity of the routing inspection robot corresponding to each route to be selected and the turning angles of the routing inspection robot corresponding to each turn in each route to be selected;
2-3: comprehensively analyzing the path length, the turning number and the turning angles of the paths to be selected corresponding to the routing inspection robot to obtain a second evaluation index of the paths to be selected corresponding to the routing inspection robot.
6. The autonomous navigation method of a wheel inspection robot according to claim 1, wherein the analyzing the preferred path corresponding to the inspection robot comprises the following specific analysis modes:
and comparing the second evaluation indexes of the paths to be selected corresponding to the inspection robots, and screening the paths to be selected corresponding to the maximum second evaluation index from the second evaluation indexes to be used as the preferred paths corresponding to the inspection robots.
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