CN114035581A

CN114035581A - Traversal path planning method for flaw detection robot with concave obstacle map

Info

Publication number: CN114035581A
Application number: CN202111350902.4A
Authority: CN
Inventors: 诸莹杰; 张克勤; 褚健; 杨根科; 王宏武
Original assignee: Ningbo Institute Of Artificial Intelligence Shanghai Jiaotong University
Current assignee: Ningbo Institute Of Artificial Intelligence Shanghai Jiaotong University
Priority date: 2021-11-15
Filing date: 2021-11-15
Publication date: 2022-02-11

Abstract

The invention discloses a traversal path planning method for a flaw detection robot containing a concave obstacle map, which relates to the technical field of path planning and comprises the following steps: step 1, matrixing a map into a two-dimensional matrix comprising a plurality of grids through a rasterized map; step 2, selecting a starting point; step 3, finding all the angular points of the concave obstacles, and recording an entrance grid; step 4, selecting a traversal path planning method as a basic scheme for traversing the two-dimensional matrix, and enabling the flaw detection robot to advance a grid according to the selected basic scheme; step 5, judging whether the map traversal is finished, if so, ending the traversal; step 6, judging whether the dead zone is entered, if so, escaping the dead zone, reaching the nearest non-traversed grid, and jumping to the step 4 to continue to advance one grid according to the selected basic scheme; step 7, judging whether the grid is positioned at an entrance, if so, preferentially traversing the concave obstacle area; and 8, jumping to the step 4.

Description

Traversal path planning method for flaw detection robot with concave obstacle map

Technical Field

The invention relates to the technical field of path planning, in particular to a traversal path planning method for a flaw detection robot with a concave obstacle map.

Background

According to the use requirements, path planning is mainly divided into two types, namely point-to-point path planning and fully-traversed path planning. The fully traversed path planning is a special path planning, aims to find an optimal continuous path to cover the whole map, and is widely applied to robots for cleaning, flaw detection and the like.

The flaw detection robot is one kind of robot for detecting surface of utensil, wall and pipeline. The flaw detection robot has various types such as a wheel type and an adsorption type, and for the adsorption type flaw detection robot, a path planning scheme for rapidly and completely traversing the whole map is required. Unlike a cleaning robot, since an object to be inspected is often related to various safety problems, a reduction in the missed inspection rate is required as a first optimization target in a traversal path planning scheme of the inspection robot. Most of the current traversal path planning methods need a rasterized map, and in order to simplify the calculation, obstacles in the map are approximated to be convex, which results in that many grids are used as obstacle grids and missed to be detected. Meanwhile, the flaw detection robot generally has the particularity of climbing work, in order to reduce energy consumption and keep the attitude stable, the flaw detection robot needs to enter a dead zone as few as possible, reduce the steering times and obstacle avoidance times during passing, and meanwhile, the flaw detection robot needs to pass through a covered area repeatedly as few as possible, so that the flaw detection efficiency is improved.

There are many existing traversal path planning methods. Wang Ben proposes a traversal path planning method for climbing a robot, which can effectively reduce the energy consumption of the robot in the climbing process while reducing the obstacle crossing times as much as possible, but simplifies the complexity of obstacles in a map and only considers convex obstacles in the rasterization map process. Zheng of university at Zhejiang provides a rapid traversal robot full-coverage path planning method, a concave polygon can be divided into convex polygons, so that an arch full-coverage path can be conveniently planned, areas still need to be divided, the cost value of each grid to a starting point needs to be calculated, and a lot of calculation cost is increased. Many other traversal path planning methods mainly focus on robots in a horizontal plane, and focus more on the efficiency of traversal, rather than low miss rate in an inspection environment. The existing technical scheme focuses more on the construction of the map, and the obstacles in the map are defaulted to be convex so as to simplify the problem. However, due to the diversity of flaw detection environments, concave obstacles are common in a map, the missing rate can be increased by largely simplifying the obstacles into convex obstacles, and safety accidents can be finally caused because some potential safety hazards are not discovered in time.

Therefore, those skilled in the art are dedicated to developing a traversal path planning method for a flaw detection robot with a concave obstacle map, which improves flaw detection efficiency and reduces a missing rate of flaw detection.

Disclosure of Invention

In view of the above defects in the prior art, the technical problem to be solved by the present invention is how to plan the traversal path of the inspection robot in the presence of the concave obstacle map, so that the inspection robot has the lowest possible missing inspection rate and the highest possible inspection efficiency.

In order to achieve the above object, the present invention provides a method for planning a traversal path of a flaw detection robot including a map of concave obstacles, which includes the following steps:

step 1, rasterizing the map and defining a grid state, and matrixing the map into a two-dimensional matrix containing a plurality of grids, wherein the grid state comprises a free grid, an obstacle grid and a traversed grid;

step 2, selecting a traversal starting point on the two-dimensional matrix;

step 3, finding the angular points of all the concave obstacles on the two-dimensional matrix, and recording an entrance grid uniquely corresponding to each concave obstacle;

step 4, selecting a traversal path planning method as a basic scheme for traversing the two-dimensional matrix, wherein when no obstacle exists on the map, all grids can be traversed through the basic scheme without repeating, and the flaw detection robot advances one grid according to the selected basic scheme;

step 5, judging whether the flaw detection robot completes traversal of the map or not, if so, ending traversal;

step 6, judging whether the flaw detection robot enters a dead zone, if so, the flaw detection robot escapes the dead zone, reaches the nearest non-traversed grid, and jumps to the step 4 to continue to advance one grid according to the selected basic scheme;

step 7, judging whether the flaw detection robot is in the entrance grid, if so, preferentially traversing a concave obstacle area, and then jumping to the step 4 to continue to advance one grid according to the selected basic scheme;

and 8, jumping to the step 4, and continuing to advance one grid according to the selected basic scheme.

Further, in step 3, the corner of the concave obstacle is a left concave corner, and the grid corresponding to the two-dimensional matrix is a grid of left concave corners.

Further, the basic scheme in the step 4 is a cattle farming algorithm.

Further, the basic scheme in the step 4 is a WMF algorithm, the WMF algorithm is an improved WMF algorithm, and includes increasing oblique movement, increasing the moving direction of the inspection robot from the original four directions of left, upper, right, and lower with priority to eight directions, and the four new directions are upper left, lower left, upper right, and lower right, respectively.

Further, the improved WMF algorithm further comprises a pre-walking scheme, when the flaw detection robot can walk in the upper and lower directions, a maximum walking grid threshold value is preset for the flaw detection robot, pre-walking is performed for two times respectively upwards and downwards according to the originally set eight direction priorities, and the direction with the small number of steps entering the dead zone is selected as the finally determined walking direction; and if the values of the two pre-walks are equal, randomly selecting one of the upward direction and the downward direction.

Further, the dead zone is disengaged in step 6 using a modified lazy theta algorithm.

Further, the target point departing from the dead zone is a dynamic target point, that is, when the flaw detection robot departs from the target point calculated according to the improved lazy theta algorithm, if a first free grid is encountered, the target point is updated to the first free grid, and the improved lazy theta algorithm is interrupted.

Further, traversing the concave obstacle area in the step 7 comprises the steps of:

step 7.1, entering the inlet grid;

and 7.2, judging whether the grid of the left concave angular point around the position is traversed or not, and if so, completing the traversal of the concave obstacle area.

Further, traversing the concave obstacle area in the step 7 further comprises the steps of:

7.3, moving to the left concave corner grid by using a lazy theta algorithm according to the sequence of right, upper, lower and left in sequence from high to low in priority;

step 7.4, judging whether the next grid is the free grid and the left concave corner grid, if so, walking to the next grid according to the preset priority in the lazy theta algorithm, and repeating the step 7.4;

7.5, judging whether a left reentrant corner point grid visible between two points exists on the right side of the position, if so, moving to the left reentrant corner point grid visible between the two points, and jumping to the step 7.4;

and 7.6, moving the flaw detection robot to the left by one grid, judging whether to retreat to the same row corresponding to the inlet grid, if not, skipping to the step 7.4, and if so, completing the traversal of the concave obstacle area.

Further, in the step 1, if the detection range of the inspection robot is a circle having a radius of r, the detection range of the inspection robot is approximated to a square having a diagonal length of 2 r.

The traversing path planning method for the flaw detection robot with the concave obstacle map, provided by the invention, at least has the following technical effects:

1. the traditional traversal path planning method does not consider the concave obstacle, or directly simplifies the concave obstacle into the convex obstacle, which can generate great influence on the flaw detection robot, thereby generating missed detection to a certain extent, and the technical scheme provided by the invention can realize the traversal of a map containing the concave obstacle;

2. the technical scheme provided by the invention can also realize less repeated traversal while completing traversal, thereby improving the detection efficiency and reducing the omission factor, and enabling the detection of the flaw detection robot to be more reliable.

The conception, the specific structure and the technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, the features and the effects of the present invention.

Drawings

FIG. 1 is a schematic overall flow chart of a preferred embodiment of the present invention;

FIG. 2 is a flow chart illustrating a preferred embodiment of the present invention for preferentially traversing a concave region;

FIG. 3 is a schematic diagram of a rasterized map in accordance with a preferred embodiment of the present invention;

FIG. 4 is a schematic view of a left reentrant dot grid and an entrance grid in accordance with a preferred embodiment of the present invention;

FIG. 5 is a diagram illustrating the pre-walking effect according to a preferred embodiment of the present invention;

FIG. 6 is a schematic illustration of entering a deadband in a preferred embodiment of the present invention;

FIG. 7 is a schematic illustration of the disengagement dead band in a preferred embodiment of the present invention;

FIG. 8 is a diagram illustrating a final traversal path in accordance with a preferred embodiment of the present invention.

Detailed Description

The technical contents of the preferred embodiments of the present invention will be more clearly and easily understood by referring to the drawings attached to the specification. The present invention may be embodied in many different forms of embodiments and the scope of the invention is not limited to the embodiments set forth herein.

In the drawings, structurally identical elements are represented by like reference numerals, and structurally or functionally similar elements are represented by like reference numerals throughout the several views. The size and thickness of each component shown in the drawings are arbitrarily illustrated, and the present invention is not limited to the size and thickness of each component. The thickness of the components may be exaggerated where appropriate in the figures to improve clarity.

The invention provides a traversal path planning method for a flaw detection robot containing a concave obstacle map, which comprises the following steps:

step 1, rasterizing a map and defining a grid state, and matrixing the map into a two-dimensional matrix containing a plurality of grids, wherein the grid state comprises a free grid, an obstacle grid and a traversed grid;

step 2, selecting a traversal starting point on the two-dimensional matrix;

step 3, finding angular points of all concave obstacles on the two-dimensional matrix, and recording an inlet grid uniquely corresponding to each concave obstacle;

step 5, judging whether the flaw detection robot completes the traversal of the map, if so, ending the traversal;

step 6, judging whether the flaw detection robot enters the dead zone, if so, escaping the dead zone, reaching the nearest non-traversed grid, and jumping to the step 4 to continue to advance one grid according to the selected basic scheme;

step 7, judging whether the flaw detection robot is in an entrance grid, if so, traversing the concave obstacle area preferentially, and then jumping to the step 4 to continue to advance one grid according to the selected basic scheme;

and 8, jumping to the step 4, and continuing to advance a grid according to the selected basic scheme.

As shown in fig. 1, an overall flowchart of a traversal path planning method for an inspection robot is shown. Before the map is rasterized in step 1, the shape and size of the map are determined, and then the map is rasterized according to the detection range of the inspection robot. In the process of confirming the shape and the range of the map under the required flaw detection environment, the size, the shape and the position of the obstacle are confirmed, the map is stored, and a corresponding path planning map can be generated through SLAM mapping of the flaw detection robot. Determining the detection range of a sensor of the flaw detection robot, and if the range of the flaw detection robot is a circle with the radius of r, approximating the detection range to a square with the length of a diagonal of 2r, namely a square internally tangent to the circle, in order to simplify subsequent path planning under the condition of not generating detection omission.

And then, rasterizing the whole map according to the size of the square, and dividing the grids of the whole map into three types according to the situation: a free grid, a barrier grid, and a traversed grid. The free grid represents a grid where no obstacle exists and the robot has not detected a flaw, the obstacle grid represents a grid where an obstacle exists in all or part of the grid area, and the traversed grid represents a grid where no obstacle exists and the robot has passed. In the technical scheme provided by the embodiment of the invention, the obstacle is not subjected to segmentation processing, namely the original shape of the obstacle is kept, and the map is unfolded and subjected to filling processing and then simplified into a rectangular map, namely the rasterized map is divided into M rows and N columns of grids, grid coordinates of the ith row and the jth column of the map are expressed as (i, j), wherein i and j are positive integers, i is more than or equal to 1 and less than or equal to M, and j is more than or equal to 1 and less than or equal to N.

Through the operation processing, the whole plane map is simplified into a two-dimensional matrix, and each value of the two-dimensional matrix represents the state of the current grid.

In step 2, a grid is selected as a starting point after the map is subjected to two-dimensional matrixing. For the convenience of the subsequent method, the starting point is preferably selected on four boundaries, and if the repeated traversal rate is further reduced, the starting point may be selected on one of the four corner grids without obstacles, and finally the starting point grid state is changed to the traversed grid.

In step 3, the corner of the concave obstacle is a left concave corner, and the grid corresponding to the two-dimensional matrix is a grid of left concave corners. The method is characterized in that a left concave corner point is searched in a global map before a flaw detection robot walks, and the corner point is characterized in that a grid of the corner point is a free grid, a grid on the right side of the corner point is an obstacle grid, and an upper grid or a lower grid is also an obstacle grid. Such grid is then the grid of left concave corner points. And continuously inquiring the grid of each left concave angular point leftwards, interrupting the search if the grid of the grid is an obstacle grid, and marking as the search failure, and stopping the search until the grid of the grid and the upper grid or the lower grid of the grid are not obstacle grids if the grid of the grid is not the obstacle grid. The point at this point is the stopping point and this type of grid is referred to as the entrance grid. If the entrance grid does not exist, deleting the corresponding corner, and recording the left concave corner grid and the entrance grid uniquely corresponding to the left concave corner grid. And the place where the entrance grid exists is judged as the position of the left concave obstacle, so that the left concave obstacle is identified. (finding the left concave intersection corresponding to the highest west priority in step 4. if the priority is changed, the found concave corner is changed.)

In step 4, a traversal method needs to be selected as a basic scheme for traversing the free grid, and the requirement is that when the map has no obstacle, all grids can be traversed by the method without repeated coverage. Alternative methods are the cattle farming algorithm, the WMF algorithm, etc. The embodiment of the invention selects the WMF algorithm which is better for optimizing the obstacle, and because the WMF algorithm has stronger adaptability to the obstacle, the processing of partial follow-up obstacles can be reduced.

The core principle of the WMF algorithm is to propose setting four different priorities for different moving directions. For example, when the robot starting point is located at the west (left) of the map, its priority is in order from high to low: west (left), south (down), normal (up), east (right).

When the robot uses the WMF algorithm to perform traversal, whether the grid of the direction is a free grid or not is detected according to the priority in sequence, and if the grid is the free grid, the robot moves to the direction with the highest priority. If the grid is traversed or is an obstacle grid, judging according to the priority analogy until a feasible direction is found to advance for one grid. If the vehicle cannot travel in all directions, the vehicle enters a dead zone. In general, the robot can traverse according to the priority in the process of traversing, but can preempt the low priority when the walking condition with higher priority is met.

However, since the moving direction of the WMF algorithm is only four, there is no more flexible diagonal movement, and the priorities in the normal and south directions are not equal, which may result in unnecessary repeated traversal. Therefore, the technical scheme selected by the embodiment of the invention provides two improvements to the WMF algorithm: and increasing oblique movement, setting a pre-walking scheme and providing an improved WMF algorithm.

The first point of increasing the oblique movement means that the movement directions are increased from four to eight, namely west (left), not-west (left upper), solution-west (left lower), not-solution (upper and lower), east (right), not-east (right upper) and solution-east (right lower). Four oblique directions of upper left, lower left, upper right and lower right are added, so that the robot is more flexible in flaw detection.

The second point is that when the inspection robot can walk in both the up-down direction, the inspection robot sets a maximum walking grid threshold value M (the value can be set to be half of the grid number of the map columns), then performs two pre-walks in the normal direction and the south direction according to the originally set eight-direction priority, and selects the direction with less steps entering the dead zone as the finally determined walking direction, so that the number of repeated traversal grids in the dead zone can be reduced. If the values of the two are equal, the influence of the movement in the normal direction and the south direction on the repeated traversal is small, so that one direction can be randomly selected.

In step 5, if there is no free grid on the two-dimensional matrix, the inspection robot is considered to complete the traversal of the map.

In step 6, if there is no free grid in the eight possible traveling directions around the position where the inspection robot is located, it is determined that the inspection robot enters the dead zone, and the dead zone escape method in step 6 is executed. After escaping the dead zone, the process jumps to step 4 to continue to advance one grid according to the pre-selected basic scheme.

After the inspection robot has entered the dead zone, it is necessary to leave the nearest free grid in as few steps as possible. In the embodiment of the invention, the traditional theta-x algorithm is selected to be separated from the dead zone, and two improvements are made on the basis:

the first point is to use the lazy theta algorithm instead of the traditional theta algorithm. The main difference between the two algorithms is that the time nodes at which grid visibility is judged are different. For the theta algorithm, the visibility of the grid and the parent node is judged every time when the grid enters the open list, but for the lazy theta algorithm, the time point for judging the visibility is delayed until the node is used (opened), namely, the starting point is searched back from the end point according to the parent node, so that the unnecessary visibility judgment of the grid in the open list is reduced, and the operation efficiency is greatly increased.

The second point is that the target point which is separated from the dead zone can be set as a dynamic target point, namely the target point is not fixed when the inspection robot is separated from the dead zone by using the theta algorithm, when the inspection robot is separated from the original target point, if the inspection robot meets the non-traversed grid of the first grid, the target point is updated to the grid, the theta algorithm is interrupted, and finally the path is traced back according to the original theta algorithm.

In step 7, whether a left concave obstacle exists around the exploration robot at the moment is judged, if yes, the concave obstacle area is firstly traversed, and after the concave obstacle area is traversed, the exploration robot jumps to step 4 to continue to advance one grid according to the selected basic scheme. In the technical scheme provided by the embodiment of the invention, whether the concave obstacle area exists is judged according to whether the flaw detection robot reaches the inlet grid recorded in the step 3. And if the flaw detection robot is positioned in the inlet grid, the flaw detection robot is considered to be positioned in the concave obstacle area, and the concave obstacle area is traversed preferentially.

The preferential traversal of the concave obstacle area in step 7 comprises the following steps, as shown in fig. 2:

step 7.1, entering an inlet grid;

7.3, moving the grid to a left concave angular point grid by using a lazy theta algorithm according to the sequence of right, upper, lower and left in sequence from high to low in priority;

7.4, judging whether the next grid is a free grid and a left concave corner grid at the same time, if so, walking to the next grid according to the priority preset in the lazy theta algorithm, and repeating the step 7.4;

7.5, judging whether a left reentrant corner point grid which is visible between two points exists on the right side of the position (visibility judgment), if so, moving to the left reentrant corner point grid which is visible between the two points, and jumping to the step 7.4;

And 8, in the case that no left concave obstacle exists around the exploration robot, jumping to the step 4 to continue to advance one grid according to the selected basic scheme. And when all the values in the grid matrix are not 0, indicating that the flaw detection is finished, and planning the path of the flaw detection robot to a starting point.

To facilitate an understanding of the scheme used by the present invention, a rasterized map as shown in FIG. 3 is given as an example, in which: a denotes a start position of the inspection robot, b denotes an accessible area requiring inspection, and c denotes an inaccessible obstacle area requiring no inspection.

In order to show that the algorithm can effectively process the concave obstacle traversal problem, most obstacles are concave or irregular in shape.

The illustrated steps are divided into three major detailed descriptions:

first, initialization and basic traversal method

Step 1: a square map of 30m by 30m in size in this case was determined, with 5 obstacles of varying shapes.

Step 2: in this case, the detection range of the inspection robot is about 1m, and thus the entire map is divided into 30 × 30 grids, where if an obstacle exists in a certain grid range, the grid is an obstacle grid, and the remaining grids are free grids. In the implementation of the method, for each grid, the state x of each grid cell can be defined_iThe values can be expressed as:

and step 3: the start point is set at the lower left corner, and the coordinates of the start point are defined as (0, 0), i.e., the horizontal and vertical coordinates will increase as one moves to the right and upward, and the coordinates corresponding to the upper right corner are (29, 29). The map has now been reduced to a 30 x 30 matrix where the values in the (i, j) grid are the current state of the grid (where i and j are positive integers, 0 ≦ i ≦ 29, 0 ≦ j ≦ 29).

And 4, step 4: all left reentrant point grids in the map are found. The left reentrant dot grid is characterized in that its own grid is a free grid, while its right side grid is a barrier grid and the upper or lower grid is also a barrier grid at the same time. As shown in fig. 4, in this case, there are eleven left reentrant corner point grids, and the numbers 1 to 11 in fig. 4 are used to mark the grids, then each left reentrant corner point grid is continuously queried to the left, if its own grid is a barrier grid, the search is interrupted, and the search is marked as a search failure, if no search failure occurs, the search is stopped until the left grid is reached and the grid itself and its upper or lower grid are not barrier grids, and this point is the entry grid. As shown in fig. 4, only the left reentrant corner points 1 and 11 can find the corresponding entry grids corresponding to the corner points marked by the

numbers

13 and 12 in fig. 4, respectively. If the entrance grids do not exist, deleting the corresponding corner points. And recording the left reentrant point grid and the entry grid uniquely corresponding to the left reentrant point grid, and storing the grid coordinate of the left reentrant point grid in the queue correspondingly. That is, left reentrant point coordinate No. 1 (9, 22), left reentrant point coordinate No. 1 corresponding to entry grid coordinate (4, 22) and left reentrant point coordinate No. 11 (12, 5), and entry grid coordinate No. 11 corresponding to entry grid coordinate (10, 5) are recorded.

And 5: the traversal is performed using a modified WMF method. The principle of the improved WMF algorithm is to set priorities for eight different directions of movement. For example, in this case, since the starting point is located at the lower left corner, the setting of the priority with better effect from high to low is as follows: west (left), not-west (upper left), south-west (lower left), not south (upper and lower), east (right), not-east (upper right), and south-east (lower right).

When the robot uses the WMF algorithm to perform traversal, whether the grid of the direction is a free grid or not is detected according to the priority in sequence, and if the grid is the free grid, the robot moves to the direction with the highest priority. When the robot travels according to the priority of the present embodiment, the robot first detects the west (left) direction, and if the robot is a free grid, the robot moves in the west (left) direction until the robot cannot travel in the west direction. Then, the normal-west (upper left) and the south-west (lower left) directions are detected, and if there is no obstacle in the directions, the robot moves in the normal-west (upper left) and the south-west (lower left) directions until the robot cannot move as described above. When none of the three highest priority directions can be moved, the normal south (up and down) direction is selected to move one cell. However, the difference is that after moving by one step, the robot continues to detect whether the west direction is a free grid, and if there is no obstacle in the west direction, the robot interrupts the travel in the normal direction and continues to move in the west direction. Move by analogy with the priority when the high priority cannot move until the dead zone is entered.

In this case, the priorities of not-west (upper left) and south-west (lower left), not-east and south (lower right), not-east and south-east (upper right) are equal two by two. When walking in both directions, a pre-walking scheme will be adopted. For example, as shown in fig. 5, when the robot is capable of walking in both the up-down direction, the robot sets a maximum walking grid threshold 15 (the value of which may be set to be half of the grid number of the map rows), and then performs two pre-walks in the normal direction and the south direction according to the eight-direction priorities that were set previously. When the two directions enter the dead zone within the preset threshold grid number, the dead zone is stopped, the number Mn of the upward walking grids is 4, the number Ms of the downward walking grids is 2, and if the dead zone is not entered, the value of the number of the walking grids is set to be 15. And finally, comparing the pre-walking grid numbers in the two directions of south and normal, and selecting the direction corresponding to the small number as the finally determined walking direction. Since Ms < Mn, in the case of the present embodiment, the user chooses to walk down preferentially, which reduces the number of repeated traversal grids when exiting the dead zone.

Step 6: every time the user moves one grid by the WMF method, whether the traversal is finished or not is judged by judging whether 0 still exists in the two-dimensional map matrix, namely the free grid. If the two-dimensional matrix does not contain 0, the traversal is finished, and if the matrix still contains 0, the judgment of the second stage is carried out.

Second, dead zone processing method

And 7: first, a determination is made whether to enter a dead zone. The state of the dead zone, that is, all the eight grids around the grid are not free grids, indicates that the dead zone is entered, as shown in fig. 6. If the dead zone is not entered, the subsequent third part of judgment is carried out; if the dead zone is entered, the following dead zone escape method is performed.

First, find the free grid closest to the point of interest, and then use this as the target point to move using the lazy theta algorithm. The target point may be set to a dynamic target point when the dead zone is exited. When the robot breaks away from the dead zone by using the lazy theta algorithm, the target point is not fixed any more, when the robot breaks away from the original target point and encounters the first grid and does not traverse the grid, the target point is updated to the grid, the lazy theta algorithm is interrupted in advance, and finally the path is traced back by using the original theta algorithm. The effect is shown in fig. 7 below (where the dashed gray line indicates the path out of the dead zone).

Three, concave filling method

And 8: when the grid is not the dead zone, whether the grid is the entrance grid or not is judged, and the grid coordinate is compared with the grid coordinate in the queue. If not, returning to the step 4 to continue traversing; if the grid is an entry grid, it indicates that a left concave obstacle is encountered, and at this time, if the corresponding left reentrant corner point grid is not traversed, the groove filling problem of the left concave obstacle will begin to be dealt with.

The specific method comprises the following steps: first, the robot will seek from the entrance grid to the corresponding left reentrant point grid using the lazy theta algorithm. At this time, the priority of the original walking is changed from high to low: east (right), normal south (up and down), west (left). Meanwhile, the walking condition is not only that the grid is a free grid, and before walking, whether the grid is a left reentrant corner point grid (the grid on the right side is a non-free grid, and the upper grid or the lower grid is also a non-free grid) needs to be judged. And judging according to the priority sequence, and if the two conditions are met simultaneously, walking. If none of the normal (upper), south (lower) and east (right) grids meet the conditions, searching whether a left reentrant point grid which is located on the right side and visible between two points (visibility judgment) exists in the left reentrant point list, if yes, moving to the grid, otherwise, moving one grid to the left until returning to the same row corresponding to the entry grid from the left, at the moment, finishing traversing of the left groove, and finally returning to the step 5 to continue traversing.

The final traversal effect is shown in fig. 8, where the gray straight line represents the robot travel path and the gray dashed line represents the path when leaving the dead zone.

The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims

1. A traversal path planning method for a flaw detection robot containing a concave obstacle map is characterized by comprising the following steps of:

step 2, selecting a traversal starting point on the two-dimensional matrix;

2. The method for planning the traversal path of the inspection robot including the map of concave obstacles according to claim 1, wherein in the step 3, the corner point of the concave obstacle is a left concave corner point, and the grid corresponding to the two-dimensional matrix is a grid of left concave corner points.

3. The traverse path planning method for flaw detection robots containing concave obstacle maps according to claim 2, characterized in that the basic scheme in step 4 is a cattle farming algorithm.

4. The method for planning the traverse path of the inspection robot having the concave obstacle map according to claim 2, wherein the basic solution in the step 4 is a WMF algorithm, the WMF algorithm is a modified WMF algorithm, and includes adding a diagonal movement, the moving direction of the inspection robot is increased from four original prioritized left, upper, right, and lower directions to eight directions, and the four new directions are upper left, lower left, upper right, and lower right, respectively.

5. The method for planning the traversal path of the inspection robot including the concave obstacle map according to claim 4, wherein the modified WMF algorithm further includes setting a pre-walking scheme, when the inspection robot is capable of walking in both the up and down directions, presetting a maximum walking grid threshold for the inspection robot, performing pre-walking twice according to the eight previously set direction priorities, respectively upward and downward, and selecting a direction with a small number of steps entering the dead zone as a final walking direction; and if the values of the two pre-walks are equal, randomly selecting one of the upward direction and the downward direction.

6. The traverse path planning method for flaw detection robots containing concave obstacle maps according to claim 5, characterized in that the dead zone is disengaged using a modified lazy theta algorithm in the step 6.

7. The method for planning a traversal path of an inspection robot including a concave obstacle map according to claim 6, wherein the target points departing from the dead zone are dynamic target points, that is, when the inspection robot departs from the target points calculated by the modified lazy theta algorithm, if a first free grid is encountered, the target points are updated to the first free grid, and the modified lazy theta algorithm is interrupted.

8. The traverse path planning method for an inspection robot including a concave obstacle map according to claim 7, wherein traversing the concave obstacle area in the step 7 includes the steps of:

step 7.1, entering the inlet grid;

9. The traverse path planning method for an inspection robot including a concave obstacle map according to claim 7, wherein traversing the concave obstacle area in the step 7 further includes the steps of:

10. The method for planning a traversal path of a flaw detection robot including a concave obstacle map according to claim 1, wherein in step 1, if the detection range of the flaw detection robot is a circle having a radius of r, the detection range of the flaw detection robot is approximated to a square having a diagonal length of 2 r.