CN114924555B - Path planning method based on full-automatic tunnel guniting robot - Google Patents

Abstract

The invention discloses a path planning method based on a full-automatic tunnel guniting robot, which comprises the steps of firstly establishing a static spray growth model of a guniting machine related to spray time and spray distance, then simulating the movement of the static model of a spray process, obtaining different superposition results according to the speed of the movement, and planning a walking path of the machine with a certain final thickness as a target, so that the final spray thickness can be achieved, the surface of the whole surface to be sprayed is relatively smooth, and the industrial-level requirement is met. The automatic path planning method has the advantages that the automatic path planning of the guniting machine is realized, the spraying effect is optimal, excessive manual interference is not needed, the need that people enter the tunnel to operate the guniting machine is reduced, the probability of danger is reduced, and the tunnel construction efficiency is improved.

Description

A path planning method based on fully automatic tunnel shotcrete robot

Technical Field

The invention relates to a path planning method based on a full-automatic tunnel shotcrete robot.

Background Art

Tunnels are an important form of construction used by people to overcome complex terrain and make full use of geographical space to build roads and railways. There are various support structures on the surface of the tunnel before the initial spraying, such as arches and protective nets. The initial spraying of the tunnel refers to the first layer of spraying on the surface of the tunnel, which changes the surface of the tunnel from the original wall to a relatively smooth and regular surface. Due to the complex construction environment and the low level of automation of the spraying robot, the construction of tunnels by domestic and foreign spraying robots relies too much on manual operation, which consumes a lot of manpower and material resources, and the quality and efficiency of the spraying also mainly depends on the experience of the workers. In actual work, due to the harsh environment of tunnel construction, being in this environment for a long time will seriously affect the health of workers. In addition, it costs a lot of money for enterprises to hire these operators. Due to the above reasons, it is often difficult to find suitable operators, which also seriously reduces the efficiency of tunnel construction.

With the rapid development of artificial intelligence, human society has entered the era of intelligence. It is an irreversible trend for artificial intelligence to lead social development. Using radar or cameras to collect tunnel data and using sensors to obtain joint information of robotic arms are important means to realize the intelligence of construction machinery. Modeling and simulation of robotic arms can simulate the spraying scene of real tunnels, and it is very convenient to obtain experimental results.

Summary of the invention

The present invention mainly solves the deficiencies of the technical problems and provides a method for path planning of a tunnel intelligent spraying robot. Different spraying adhesion models can be established for different machine models. Path planning can be performed based on tunnel information collected by radar, and the path that the machine will travel can be obtained without human intervention, and the standards and coating utilization rate required by the industrial level can be achieved.

In order to achieve the above object, the technical solution of the present invention is:

A path planning method based on a fully automatic tunnel shotcrete robot comprises the following steps:

Step 1, collecting the thickness of concrete attached to the wall after the spray gun sprays at different durations and distances, and then obtaining a functional formula of the wall concrete thickness changing with the spraying time and the distance between the spray gun and the tunnel wall;

Step 2, collect the over-excavation and under-excavation information of the tunnel surface, and based on the function obtained in step 1, combine the path planning algorithm to complete the path planning by spraying the over-excavation area first, then the normal area, and avoiding the under-excavation area during spraying.

The method, in the step 1, collects the thickness of concrete attached to the wall after the spray gun sprays on the wall for different continuous time periods, firstly sprays concrete vertically onto the wall with a spray gun at a fixed position, and measures the thickness of the concrete attached to the wall after a predetermined time interval, thereby fitting the relationship between the thickness of the wall concrete and the spraying time through a β distribution model.

The method, in the step 1, collects the thickness of concrete attached to the wall after the spray gun sprays at different distances, first sprays concrete vertically onto the wall with the spray gun and measures the thickness of the concrete attached to the wall, then changes the distance between the spray gun and the wall and sprays and measures again, thereby fitting the relationship between the thickness of the wall concrete and the spraying distance through the β distribution model.

The method described above uses the β distribution model to fit the relationship between the thickness of the wall concrete and the spraying time as follows:

Where F(r) is the cumulative thickness of the coating, T(t) _max is the function of the spraying thickness of the center point O of the circular spraying area changing with time, the center point O is the projection point of the nozzle of the spray gun on the wall, r is the distance between a certain point S and O in the circular spraying area; β is the adjustment parameter used to adjust the function value, and R is the radius of the circular spraying area.

The method described above uses the β distribution model to fit the relationship between the thickness of the wall concrete and the spraying distance as follows:

Where R is the radius of the circular spraying area, d is the vertical distance between the nozzle of the spray gun and the wall, is the spray cone angle of the spray.

In the method, in the step 2, the over-excavation and under-excavation information of the tunnel surface is collected by scanning the entire tunnel surface through a radar to obtain the information of the entire tunnel surface, and the entire tunnel surface is divided into three levels according to the degree of over-excavation, and is defined as L3, L2, and L1 from deep to shallow based on the wall surface. L3 indicates over-excavation, that is, the depth is greater than the deepest threshold of the L2 level, the L2 level indicates the standard area, that is, the depth is between the deepest threshold and the shallowest threshold of the L2 level, and the L1 level indicates under-excavation, that is, the depth is less than the shallowest threshold of the L2 level.

The method, when performing hierarchical classification, also includes the following steps: if the depth of a certain point cloud sampling point obtained by scanning is greater than the deepest threshold of the L2 level, first count the other points in the circular area with the point as the center and the radius R of the spraying area whose depth is greater than the deepest threshold of the L2 level; if the number of points in the area greater than the deepest threshold of the L2 level exceeds the preset number N, then this block area is considered to be an L3 level area, otherwise the point cloud sampling point is considered to be a mutation point and is not determined as an L3 area; if the depth of a certain point cloud sampling point obtained by scanning is less than the shallowest threshold of the L2 level, first count the other points in the circular area with the point as the center and the radius R of the spraying area whose depth is less than the shallowest threshold of the L2 level; if the number of points in the area less than the shallowest threshold of the L2 level exceeds the preset number N, then this block area is considered to be an L1 level area, otherwise the point cloud sampling point is considered to be a mutation point and is not determined as an L1 area.

In the method, in step 2, the principle of first spraying the over-excavated area and then spraying the normal area, and avoiding the under-excavated area during spraying is:

Prioritize spraying the L3 level area to make its thickness reach the L2 level, and the planned path avoids passing through the L1 and L2 areas; when the L3 level area becomes the L2 level area, the L2 area is fully covered by spraying, and the L1 level area must still be avoided during the spraying process;

In the method, in step 2, based on the functional formula obtained in step 1, the top view of the concrete sprayed on the tunnel wall is a circle with a radius of R, and the distance between the next spraying area and the current spraying area is d, then the thickness shape function formed by the superposition of the two spraying areas is

Where P ₁ (r) and P ₂ (r) are the thickness distribution functions of the first spraying area and the second spraying area, respectively. Combined with the function of the wall concrete thickness changing with the spraying time and the distance between the spray gun and the tunnel wall, we have According to actual needs, the preset target thickness is _Sp , and the difference between the target thickness and the actual superposition thickness is the part that needs to be sprayed. The sum of the variances of the actual superposition thickness P(r) and the ideal thickness _Sp at a certain point in space is taken as the minimum optimization target to establish an optimization function: minS(r,t)＝( _Sp -P(r)) ² . With the goal of minimizing S(r,t), the optimal d and t are solved to obtain the path planning.

The technical effect of the present invention is that the present invention can be widely applied to various spraying machines. For different spraying robots, it is only necessary to modify the model parameters to achieve the fitting effect, and then combine the surface information of the spraying area to achieve the function of automatic path planning. With this method, people can avoid entering the tunnel observation environment. The machine can plan the path by itself and complete the spraying work, preventing life danger caused by tunnel collapse, and also improving construction efficiency.

BRIEF DESCRIPTION OF THE DRAWINGS

Fig. 1 is a workflow diagram of the present invention;

FIG2 is a schematic diagram of a nozzle spraying paint according to the present invention;

FIG3 is a simulation diagram of tunnel information collected by the present invention;

FIG4 is a simulation diagram of the concrete superposition on the wall surface when the interval between two spraying areas is 140 mm according to the present invention;

FIG. 5 is a schematic diagram of the superposition of two spraying areas of the present invention.

DETAILED DESCRIPTION

The technical solution of the present invention is further described below in conjunction with specific implementation methods.

As shown in Figure 1: The present invention mainly includes collecting tunnel information, obtaining point cloud data, then establishing a spraying model through a spraying test, and finally combining the over-excavation and under-excavation information of the wall to realize path planning, and finally achieve the purpose of intelligent spraying.

In this embodiment, a laser radar is used to obtain tunnel information. Compared with a camera, it is more suitable for use in dangerous and dimly lit places such as tunnels. The principle is to obtain tunnel information by emitting laser particles.

First, use radar to scan the entire tunnel surface to obtain information about the entire tunnel surface. As shown in Figure 3, it can be seen that there are some over-excavation areas. The entire tunnel surface can be divided into three levels according to the degree of over-excavation, based on the wall surface, from deep to shallow, namely L3, L2, and L1. Among them, L3 represents over-excavation, that is, the depth is greater than the deepest threshold of the L2 level, the L2 level represents the standard area, that is, the depth is between the deepest threshold and the shallowest threshold of the L2 level, and the L1 level represents under-excavation, that is, the depth is less than the shallowest threshold of the L2 level.

In order to make an accurate judgment on over-excavation and under-excavation, the present embodiment also adopts a method for judging whether the L3 and L1 areas are mutation points: if the depth of a certain point cloud sampling point obtained by scanning is greater than the deepest threshold of the L2 level, firstly, other points in a circular area with the point as the center and a radius R of the spraying area whose depth is greater than the deepest threshold of the L2 level are counted; if the number of points in the area greater than the deepest threshold of the L2 level exceeds the preset number N, then this block area is considered to be an L3 level area, otherwise, the point cloud sampling point is considered to be a mutation point and is not determined as an L3 area; if the depth of a certain point cloud sampling point obtained by scanning is less than the shallowest threshold of the L2 level, firstly, other points in a circular area with the point as the center and a radius R of the spraying area whose depth is less than the shallowest threshold of the L2 level are counted; if the number of points in the area less than the shallowest threshold of the L2 level exceeds the preset number N, then this block area is considered to be an L1 level area, otherwise, the point cloud sampling point is considered to be a mutation point and is not determined as an L1 area.

Next, this embodiment establishes a spraying model through a spraying test. A fixed spray gun sprays concrete vertically onto the wall, because the operator believes that spraying perpendicular to the wall is more conducive to the adhesion of concrete to the wall, and can also reduce the influence of concrete gravity. Then use radar scanning to measure the shape of the concrete attached to the wall at regular intervals. This time needs to be determined according to the characteristics of the radar. If the performance of the radar is very high, a shorter time can be selected, so that the function shape that changes with time is more accurate. The shape of the sprayed concrete is roughly as shown in Figure 2, and the β distribution model can be used to fit the relationship between the shape of the wall concrete and the spraying time. The schematic diagram of the plane spraying of the β distribution model of the finite range model is shown in Figure 2. In the figure, there is a spray point Q at a height d from a planar workpiece, which represents the spray gun paint nozzle perpendicular to the workpiece surface. Point O is the projection point of point Q on the plane, and it is also the center point of the circular coating accumulation area. Assuming that the spray cone angle of the spray is φ, the spray gun continuously sprays the plane at point Q within a unit time t. According to the β distribution model, the cumulative thickness of the coating is 0＜r＜R,1＜β, where T(t) _max is the function of the thickness of the coating at the center of the circular coating area changing with time, r is the distance from a certain point in the spraying area to the center O, β is the adjustment parameter used to adjust the function value, and R is the radius of the circular spraying area.

After obtaining the growth function over time, adjust the distance between the spray gun and the wall, repeat the radar scanning, and then obtain the function of the shape of the wall concrete with the distance between the spray gun and the tunnel wall. Since the radius of the concrete attached to the wall will increase with the increase of the distance between the spray gun and the wall, but there is a limit to the distance from the tunnel, if it is too large, the concrete will not adhere to the wall, and if it is too small, the concrete will rebound on the wall, causing paint loss, so the distance here needs to select a suitable value. Generally speaking, the relationship between the spraying distance, spraying angle, and attachment radius is R = d*tan(φ/2), 0＜φ＜π/2.

According to the experience of the machine operator, the L3 level area is sprayed first to reach the L2 level, and it cannot be sprayed directly to the L1 level, because the concrete sprayed on the wall has not solidified in a short time and will be affected by gravity, which will cause the paint to fall and reduce the utilization rate of the paint. Therefore, it is necessary to spray to a certain thickness while ensuring the utilization rate, but it cannot be sprayed too little, which will increase the number of times the machine sprays back and forth, reducing the spraying efficiency.

According to the operator's experience, when spraying L3 and L2 areas, there may be L1 level areas on the wall space, so in the process of path planning, these areas can be abstracted as obstacles so that the planned path does not pass through these areas. Because the L1 level belongs to the under-excavation area, spraying concrete into this area will increase its thickness, resulting in a worse spraying effect.

There are already many specific path planning algorithms, and you can adopt the appropriate algorithm as needed. Here, the DWA algorithm is used to plan the L1 area into obstacles and generate a trajectory path with the final superimposed spraying effect and the minimum arc of the robot arm movement as the goal.

Different overlapping distances will lead to different superposition shapes, as shown in Figure 4. The distance between the two spraying points can be determined according to the flatness of the superposition of the two spraying areas and the target thickness. The spraying thickness can be controlled by adjusting this distance, so that the spraying path of the entire surface to be sprayed can be planned. Specifically, based on the function obtained in step 1, see Figure 5, the top view of the concrete sprayed on the tunnel wall is a circle with a radius of R, and the distance between the next spraying area and the current spraying area is d, then the thickness shape function formed by the superposition of the two spraying areas is

Where P ₁ (r) and P ₂ (r) are the thickness distribution functions of the first spraying area and the second spraying area, respectively. Combined with the function of the wall concrete thickness changing with the spraying time and the distance between the spray gun and the tunnel wall, we have According to actual needs, the preset target thickness is _Sp , and the difference between the target thickness and the actual thickness formed by superposition is the part that needs to be sprayed. The sum of the variances of the actual superposition thickness P(r) at a certain point in space and the ideal thickness _Sp is taken as the minimum optimization target to establish an optimization function: minS(r,t)＝( _Sp -P(r)) ² , where P(r) is obtained by the above formula, and P(r) is only affected by the lateral displacement distance d and the spraying time t at different points. Therefore, with the goal of minimizing S(r,t), solving the optimal d and t can obtain the path planning route, and the answer can be obtained by using the least squares method and other solutions.

The present invention combines tunnel pattern recognition technology and kinematic solution technology of the robot arm, and first identifies the information of the area to be sprayed in the tunnel. During the spraying process, the spraying volume will not change arbitrarily. Therefore, for a certain specific model, the spraying volume is kept constant, and the spraying model is the same. Therefore, by combining the tunnel over-excavation and under-excavation information and the spraying model, the movement path of the robot arm can be accurately planned, and then the movement of each joint can be calculated by combining the robot inverse kinematics, and then combined with the robot control technology, the robot can realize the fully automatic tunnel spraying, and the quality of the spraying can be guaranteed, the spraying efficiency can be improved, and accidents can be reduced.

The above description is only a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any technician familiar with the profession can make some changes or modifications to equivalent embodiments of equivalent changes using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims (7)

1. A path planning method based on a fully automatic tunnel shotcrete robot, characterized in that it comprises the following steps: Step 1, collecting the thickness of concrete attached to the wall after the spray gun sprays at different durations and distances, and then obtaining a functional formula of the wall concrete thickness changing with the spraying time and the distance between the spray gun and the tunnel wall; Step 2, collecting the over-break and under-break information of the tunnel surface, and based on the function obtained in step 1, combined with the path planning algorithm, the path planning is completed by spraying the over-break area first, then the normal area, and avoiding the under-break area during spraying; In the step 2, the over-excavation and under-excavation information of the tunnel surface is collected by scanning the entire tunnel surface with a radar to obtain the information of the entire tunnel surface, and the entire tunnel surface is divided into three levels according to the degree of over-excavation, and is defined as L3, L2, and L1 from deep to shallow based on the wall surface. L3 represents over-excavation, that is, the depth is greater than the deepest threshold of the L2 level, the L2 level represents the standard area, that is, the depth is between the deepest threshold and the shallowest threshold of the L2 level, and the L1 level represents under-excavation, that is, the depth is less than the shallowest threshold of the L2 level; In step 2, based on the function obtained in step 1, the top view of the concrete sprayed on the tunnel wall is a circle with a radius of R, and the distance between the next spraying area and the current spraying area is d, then the thickness shape function formed by the superposition of the two spraying areas is Where P ₁ (r) and P ₂ (r) are the thickness distribution functions of the first spraying area and the second spraying area, respectively. Combined with the function of the wall concrete thickness changing with the spraying time and the distance between the spray gun and the tunnel wall, we have According to actual needs, the preset target thickness is _Sp , and the difference between the target thickness and the actual superposition thickness is the part that needs to be sprayed. The sum of the variances of the actual superposition thickness P(r) and the ideal thickness _Sp at a certain point in space is taken as the minimum optimization target to establish an optimization function: minS(r,t)＝( _Sp -P(r)) ² . With the goal of minimizing S(r,t), the optimal d and t are solved to obtain the path planning. 2. The method according to claim 1 is characterized in that, in the step 1, the thickness of the concrete attached to the wall after the spray gun sprays the wall for different continuous time periods is collected, and firstly, the concrete is sprayed vertically onto the wall by a spray gun at a fixed position, and the thickness of the concrete attached to the wall is measured after a predetermined period of time, so as to fit the relationship between the thickness of the wall concrete and the spraying time through a β distribution model. 3. The method according to claim 1 is characterized in that, in the step 1, the thickness of the concrete attached to the wall after the spray gun sprays at different distances is collected by first spraying the concrete vertically onto the wall with the spray gun and measuring the thickness of the concrete attached to the wall, and then changing the distance between the spray gun and the wall and spraying again and measuring, so as to fit the relationship between the thickness of the wall concrete and the spraying distance through the β distribution model. 4. The method according to claim 1, characterized in that the functional formula of the relationship between the thickness of the wall concrete and the spraying time is fitted by the β distribution model: Where F(r) is the cumulative thickness of the coating, T(t) _max is the function of the spraying thickness of the center point O of the circular spraying area changing with time, the center point O is the projection point of the nozzle of the spray gun on the wall, r is the distance between a certain point S and O in the circular spraying area; β is the adjustment parameter used to adjust the function value, and R is the radius of the circular spraying area. 5. The method according to claim 1, characterized in that the functional formula for fitting the relationship between the thickness of the wall concrete and the spraying distance through the β distribution model is: Where R is the radius of the circular spraying area, d is the vertical distance between the nozzle of the spray gun and the wall, is the spray cone angle of the spray. 6. The method according to claim 1 is characterized in that, when performing hierarchical classification, it also includes the following steps: if the depth of a certain point cloud sampling point obtained by scanning is greater than the deepest threshold of the L2 level, first count the other points in the circular area with the point as the center and the radius R of the spraying area whose depth is greater than the deepest threshold of the L2 level; if the number of points in the area greater than the deepest threshold of the L2 level exceeds the preset number N, then this block area is considered to be an L3 level area, otherwise the point cloud sampling point is considered to be a mutation point and is not determined as an L3 area; if the depth of a certain point cloud sampling point obtained by scanning is less than the shallowest threshold of the L2 level, first count the other points in the circular area with the point as the center and the radius R of the spraying area whose depth is less than the shallowest threshold of the L2 level; if the number of points in the area less than the shallowest threshold of the L2 level exceeds the preset number N, then this block area is considered to be an L1 level area, otherwise the point cloud sampling point is considered to be a mutation point and is not determined as an L1 area. 7. The method according to claim 1, characterized in that, in the step 2, the over-excavation area is sprayed first, and then the normal area is sprayed, and the principle of avoiding the under-excavation area during spraying is: Prioritize spraying the L3 level area to make its thickness reach the L2 level, and the planned path avoids passing through the L1 and L2 areas; when the L3 level areas are all transformed into L2 level areas, the L2 areas are fully covered by spraying, and the L1 level areas must still be avoided during the spraying process.