Patrol robot path intelligent planning method fusing GPR and DWA algorithms

Abstract

The invention discloses an intelligent routing inspection robot path planning method fusing GPR and DWA algorithms, and the method comprises the steps: plant area overall point cloud data acquisition, local map division, global path planning and local path planning, and a simulation plant growth algorithm and a dynamic window method are introduced in robot local path planning in a plant area environment. The optimal obstacle avoidance path of the mobile robot is evaluated, and the path planning obstacle avoidance efficiency and safety of the mobile robot in a dynamic environment are improved. According to the method, the grid map is divided based on the inspection nodes, block path planning is adopted, the algorithm operation time is short, and the global optimal path can be quickly planned; a simulation plant growth algorithm and a dynamic window method are fused to realize local path intelligent planning in a complex dynamic environment, and the situation with obstacles can be effectively handled.

Description

Technical Field

[0001] The present invention belongs to the technical field of robot path planning, and relates to an inspection robot path intelligent planning method integrating a plant growth simulation (GPR) algorithm and a dynamic window method (DWA). Background Art

[0002] With the development and maturity of artificial intelligence technology, as well as the popularization of the Internet of Things, cloud computing, and 5G technology, intelligent inspection robots that aim to replace manual inspections have been applied to more and more fields. Intelligent inspection robots are used in factory production lines, IDC computer rooms, power lines, and many production environments that require regular and frequent inspections. For path planning problems, the environment in which mobile robots are located is unknown and dynamic in most scenarios, and some areas may have problems such as environmental changes. The motion state of dynamic obstacles is difficult to accurately predict, and it is impossible to rely on static maps to plan a safe and reliable path. This is undoubtedly a great challenge for the path planning module. Therefore, how to improve the accuracy of robot path planning and improve the timeliness of inspections is an urgent problem that technicians in this field need to solve. Summary of the invention

[0003] The purpose of the present invention is to provide an intelligent path planning method for an inspection robot that integrates GPR and DWA algorithms. The method includes the acquisition of overall point cloud data of the factory area, local map division, global path planning and local path planning. A simulated plant growth algorithm and a dynamic window method are introduced into the local path planning of the robot in the factory environment to evaluate the optimal obstacle avoidance path of the mobile robot, thereby improving the obstacle avoidance efficiency and safety of the path planning of the mobile robot in a dynamic environment.

[0004] An intelligent path planning method for an inspection robot integrating GPR and DWA algorithms comprises the following steps:

[0005] Step 1: The robot is equipped with a laser radar to scan the factory area. The robot is remotely controlled to fly around the factory area to obtain the overall point cloud data of the factory area.

[0006] Step 2: Establish an overall grid map of the factory area based on the point cloud data obtained in step 1;

[0007] Step 3: Mark the detection nodes, starting points and end points in the overall grid map of the plant area established in step 2 based on the inspection requirements;

[0008] Step 4: According to the inspection plan, the detection nodes marked in step 3 are numbered and sorted, the overall grid map of the factory area is divided into blocks according to the inspection order, some exits are closed, and path planning is performed in blocks to reduce the amount of calculation;

[0009] Step 5. Calculate light intensity:

[0010] Calculate the distance from the 8 grids around the robot to the target grid, where: the 8 grids are recorded as up, down, left, right, upper left, upper right, lower left and lower right. When calculating the distance, calculate the distance from the current grid to the target grid on the x and y axes respectively. The light intensity is inversely proportional to the distance length. The light intensity OA from the current grid to the target grid in the x-axis direction x And the light intensity OA from the current grid to the target point in the y-axis direction y The calculation formulas are:

[0011]

[0012] In the formula, O x is the distance from the current grid to the target grid in the x-axis direction, O y The distance from the current grid to the target point in the y-axis direction;

[0013] Step 6: Calculate the shading intensity:

[0014] Step 61: Set the unit distance of each grid in the four directions of up, down, left, and right to 1, and the unit distance of each grid in the four directions of upper left, upper right, lower left, and lower right to

[0015] Step 62: When encountering an obstacle or boundary, stop moving forward, record the number N of all grids in each direction of up, down, left, right, upper left, upper right, lower left, and lower right, and then multiply by the corresponding unit distance to obtain the final distance. The distance length is inversely proportional to the shading intensity. The calculation formula of the shading intensity S is as follows:

[0016]

[0017] Where N is the number of grids required from the eight grids around the robot to the obstacle or boundary; S is the shading intensity;

[0018] Step 7: Calculate the total light intensity:

[0019] Calculate the total light intensity of the 8 grids around the robot respectively. The calculation formula for the total light intensity is:

[0020] S x =S×cosθ

[0021] S y =S×sinθ

[0022] R x =OA x -S x

[0023] R=OA-S

[0024]

[0025] Where θ is the angle between the eight grids around the robot and the current position (for example, the grid in the right direction has an angle of 0°, and the grid in the upper right direction has an angle of 45°); S x is the component of the shading intensity in the x-axis direction; S y is the component of the shading intensity in the y-axis direction; R x is the component of the total light intensity on the x-axis; R y is the component of the total light intensity on the y-axis; R is the total light intensity; OA x is the light intensity in the x-axis direction from the current grid to the target grid, OA y The light intensity in the y-axis direction from the current grid to the target grid;

[0026] Step 8: Determine the direction of movement:

[0027] Based on the total light intensity obtained in step 7, the direction with the largest total light intensity is selected as the robot moving direction;

[0028] Step 9: Record the path coordinates. According to the moving direction of each step of the robot, record the robot trajectory coordinates to obtain the robot's global path (x j ,y j );

[0029] Step 10: Determine the speed space:

[0030] Due to the limitations of the robot itself, surrounding obstacles, and motor performance, the linear and angular velocities of the robot will move within a certain range. The specific limitations are as follows:

[0031] When the robot speed is limited by its own speed, its linear speed V m and angular velocity ω m The restrictions are:

[0032]

[0033] Where V min 、V max are the minimum and maximum speeds that the robot can reach respectively; ω min ,ω max are the minimum and maximum angular velocities that the robot can achieve respectively;

[0034] When the robot is limited by the motor performance, its linear speed V a , and angular velocity ω, a The restrictions are:

[0035]

[0036] Where V c ,ω care the current linear velocity and angular velocity of the robot respectively; V a ,ω a are the absolute value of the robot's linear acceleration and the absolute value of its maximum angular acceleration; Δt is the unit time;

[0037] When the robot is restricted by obstacles, its linear velocity V s and angular velocity ω s The restrictions are:

[0038]

[0039] Where d is the distance between the obstacle and the robot;

[0040] Based on the above three conditions, a speed space can be jointly determined. The speed activity space is composed of V m 、V a , and V s Determined together; similarly, the acceleration activity space is determined by ω m ,ω, a and ω s Determine together, randomly sample the linear velocity V and angular velocity ω in the velocity activity space and acceleration activity space respectively, and form a series of velocity groups [V i ,ω i ];

[0041] Step 11: Speed ​​space evaluation:

[0042] An evaluation function based on light intensity, shading intensity, and proximity to the global path is constructed to evaluate the trajectory represented by each group of speeds in the speed space and select the optimal speed group. The formula of the evaluation function is as follows:

[0043] G(V,ω)=σ(β·B(V i ,ω i )+α·S max (V i ,ω i )+γ·P(V i ,ω i ))

[0044] In the formula, α, β and γ are the coefficients of the evaluation function, σ represents normalization, and the formulas for each sub-item are as follows:

[0045]

[0046] Where, d, is the distance between the obstacle and the robot, d is the distance between the robot and the end point, (x a ,y a ) is the coordinate of the robot’s trajectory end point under the current speed group, (x j ,yj ) is the global path coordinate of the robot, n is the number of global path trajectory coordinates; m is the number of random samples in the speed group;

[0047] Step 12: Determine the optimal trajectory:

[0048] The speed group that minimizes G(V,ω) is selected as the optimal speed group, so as to obtain the local path trajectory and complete the global path planning and local path planning tasks.

[0049] Compared with the prior art, the present invention has the following advantages:

[0050] The present invention divides the grid map based on inspection nodes and adopts block path planning. The algorithm has a short running time and can quickly plan the global optimal path. The integration of the simulated plant growth algorithm and the dynamic window method realizes the intelligent planning of local paths in complex dynamic environments, which can effectively cope with scenarios with obstacles. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is an overall flow chart of the path planning method of the present invention;

[0052] Figure 2 Divide the map;

[0053] Figure 3 This is a schematic diagram of the eight grids around the robot;

[0054] Figure 4 Calculation result of grid light intensity around the robot;

[0055] Figure 5 The calculation result of the grid shading intensity around the robot;

[0056] Figure 6 The calculation result of the total light intensity of the grid around the robot;

[0057] Figure 7 The direction of robot movement;

[0058] Figure 8 is the overall path of the robot;

[0059] Fig. 9 is the local path of the robot. DETAILED DESCRIPTION

[0060] The technical solution of the present invention is further described below in conjunction with the accompanying drawings, but is not limited thereto. Any modification or equivalent replacement of the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention should be included in the protection scope of the present invention.

[0061] The present invention provides a path planning method that simulates the fusion of GPR algorithm and DWA, such as Figure 1 As shown, the method comprises the following steps:

[0062] Step 1: The robot is equipped with a laser radar to scan the factory area. The robot is remotely controlled to fly around the factory area to obtain the overall point cloud data of the factory area.

[0063] Step 2: Create an overall grid map of the factory area (boundaries, obstacles) based on the point cloud data obtained in step 1.

[0064] Step 3: Based on the inspection requirements, mark the inspection nodes, starting points and end points in the overall grid map of the factory area established in step 2.

[0065] Step 4: According to the inspection plan, the detection nodes marked in step 3 are numbered and sorted, and the overall grid map of the factory area is divided into blocks according to the inspection order, and some exits are closed. The path planning is carried out in blocks to reduce the amount of calculation, such as Figure 2 shown.

[0066] Step 5. Calculate light intensity:

[0067] The light intensity is expressed by distance, and the eight grids around the robot are calculated (respectively marked as up, down, left, right, upper left, upper right, lower left and lower right, such as Figure 3 When calculating the distance, the distance from the current grid to the target grid is calculated on the X and Y axes respectively. The distance from the current grid to the target grid in the x-axis direction is O x , the distance from the current grid to the target point in the y-axis direction is O y The light intensity is inversely proportional to the distance. The farther the current grid is from the target grid, the weaker the light intensity. Therefore, the light intensity in the x-axis direction from the current grid to the target grid is OA. x , the light intensity in the y-axis direction is OA y ,like Figure 4 shown.

[0068]

[0069] Step 6: Calculate the shading intensity:

[0070] Set the unit distance of each grid in the four directions of top, bottom, left, and right to 1, and the unit distance of each grid in the four directions of top left, top right, bottom left, and bottom right to like Figure 5 shown.

[0071] When encountering an obstacle or boundary, stop moving forward, record the number of all grids N in each direction (up, down, left, right, upper left, upper right, lower left, lower right), and then multiply by the corresponding unit distance to get the final distance. Because the distance length is inversely proportional to the shading intensity, the shading intensity S is calculated as follows:

[0072]

[0073] Step 7: Calculate the total light intensity:

[0074] Calculate the total light intensity of the eight grids around the robot (upper, lower, left, right, upper left, upper right, lower left and lower right), such as Figure 6 As shown, the total light intensity calculation formula is:

[0075] S x =S×cosθ

[0076] S y =S×sinθ

[0077] R x =OA x -S x

[0078] R y =OA y -S y

[0079]

[0080] Step 8: Determine the direction of movement:

[0081] Based on the total light intensity obtained in step 7, the direction with the largest total light intensity is selected as the robot moving direction, such as Figure 7 shown.

[0082] Step 9: Record the path coordinates. According to the moving direction of each step of the robot, record the robot trajectory coordinates to obtain the robot's global path (x j ,y j ),like Figure 8 shown.

[0083] The robot's operating environment is in a dynamic state, and there are often people or objects on the robot's path. At this time, local path planning is required to avoid obstacles. The following describes local path planning when encountering sudden obstacles.

[0084] Step 10: Determine the speed space:

[0085] Due to the limitations of the robot's own conditions, surrounding obstacles, motor performance, etc., the car's linear speed and angular speed will move within a range.

[0086] The robot speed cannot be increased infinitely, it is limited by its own speed, and the linear speed V m and angular velocity ω m The restrictions are:

[0087]

[0088] Where V min 、V max are the minimum and maximum speeds that the robot can reach respectively; ω min ,ω max are the minimum and maximum angular velocities that the robot can achieve respectively;

[0089] The robot is limited by the motor performance, the linear speed V a , and angular velocity ω, a The restrictions are:

[0090]

[0091] Where V c ,ω c are the current linear velocity and angular velocity of the robot, V a ,ω a They are respectively the absolute value of the robot's linear velocity acceleration and the absolute value of the maximum angular velocity acceleration.

[0092] The robot is restricted by obstacles, and the linear speed V s and angular velocity ω s The restrictions are:

[0093]

[0094] Where d is the distance between the obstacle and the robot.

[0095] Based on the above three conditions, a velocity space can be jointly determined. In the velocity space, the linear velocity and angular velocity are randomly sampled to form a series of velocity groups [V i ,ω i ].

[0096] Step 11: Velocity space evaluation: After obtaining the velocity group, the corresponding trajectory can be inferred based on the kinematic model.

[0097] An evaluation function based on light intensity, shading intensity, and proximity to the global path is constructed to evaluate the trajectory represented by each group of speeds in the speed space and select the optimal speed group. The formula of the evaluation function is as follows:

[0098] G(V,ω)=σ(β·B(V i ,ω i )+α·S max (Vi ,ω i )+γ·P(V i ,ω i ))

[0099] In the formula, α, β and γ are the coefficients of the evaluation function, σ represents normalization, and the formulas for each sub-item are as follows:

[0100]

[0101] Where d is the distance between the obstacle and the robot.

[0102]

[0103] Where d is the distance between the robot and the end point.

[0104]

[0105] In the formula, (x a ,y a ) is the coordinate of the robot’s trajectory end point under the current speed group, (x j ,y j ) is the global path coordinate of the robot, and n is the number of global path trajectory coordinates.

[0106] Since the local path planning process requires the collection of multiple sensors, the collected information cannot be continuous, which will lead to large differences after evaluation. Normalization processing (smoothing processing) is performed, where σ represents normalization.

[0107]

[0108] Where m is the number of random samples in the speed group.

[0109] Step 12: Determine the optimal trajectory:

[0110] The speed group that minimizes G(V,ω) is selected as the optimal speed group, so as to obtain the local path trajectory and complete the local path planning tasks such as global path planning and obstacle avoidance. Fig. 9 shown.

Claims

1. An intelligent path planning method for inspection robots integrating GPR and DWA algorithms, characterized in that The method comprises the following steps: Step 1: The robot is equipped with a laser radar to scan the factory area. The robot is remotely controlled to fly around the factory area to obtain the overall point cloud data of the factory area. Step 2: Establish an overall grid map of the factory area based on the point cloud data obtained in step 1; Step 3: Mark the detection nodes, starting points and end points in the overall grid map of the plant area established in step 2 based on the inspection requirements; Step 4: According to the inspection plan, the detection nodes marked in step 3 are numbered and sorted, the overall grid map of the factory area is divided into blocks according to the inspection order, some exits are closed, and path planning is performed in blocks to reduce the amount of calculation; Step 5. Calculate light intensity: Calculate the distance from the 8 grids around the robot to the target grid, where: the 8 grids are recorded as up, down, left, right, upper left, upper right, lower left and lower right. When calculating the distance, calculate the distance from the current grid to the target grid on the x and y axes respectively. The light intensity is inversely proportional to the distance length. The light intensity OA from the current grid to the target grid in the x-axis direction x And the light intensity OA from the current grid to the target point in the y-axis direction y The calculation formulas are: In the formula, O x is the distance from the current grid to the target grid in the x-axis direction, O y The distance from the current grid to the target point in the y-axis direction; Step 6: Calculate the shading intensity: Step 61: Set the unit distance of each grid in the four directions of up, down, left, and right to 1, and the unit distance of each grid in the four directions of upper left, upper right, lower left, and lower right to Step 62: When encountering an obstacle or boundary, stop moving forward, record the number N of all grids in each direction of up, down, left, right, upper left, upper right, lower left, and lower right, and then multiply by the corresponding unit distance to obtain the final distance. The distance length is inversely proportional to the shading intensity. The calculation formula of the shading intensity S is as follows: Where N is the number of grids required from the eight grids around the robot to the obstacle or boundary; S is the shading intensity; Step 7: Calculate the total light intensity: Calculate the total light intensity of the 8 grids around the robot respectively. The calculation formula for the total light intensity is: S x =S×cosθ S y =S×sinθ R x =OA x -S x Where θ is the angle between the eight grids around the robot and the current position; S x is the component of the shading intensity in the x-axis direction; S y is the component of the shading intensity in the y-axis direction; R x is the component of the total light intensity on the x-axis; R y is the component of the total light intensity on the y-axis; R is the total light intensity; OA x is the light intensity in the x-axis direction from the current grid to the target grid, OA y The light intensity in the y-axis direction from the current grid to the target grid; Step 8: Determine the direction of movement: Based on the total light intensity obtained in step 7, the direction with the largest total light intensity is selected as the robot moving direction; Step 9: Record the path coordinates. According to the moving direction of each step of the robot, record the robot trajectory coordinates to obtain the robot's global path (x j ,y j ); Step 10: Determine the speed space: Due to the limitations of the robot itself, surrounding obstacles, and motor performance, the linear and angular velocities of the robot will move within a certain range. The specific limitations are as follows: When the robot speed is limited by its own speed, its linear speed V m and angular velocity ω m The restrictions are: Where V min 、V max are the minimum and maximum speeds that the robot can reach respectively; ω min ,ω max are the minimum and maximum angular velocities that the robot can achieve respectively; When the robot is limited by the motor performance, its linear speed V a , and angular velocity ω, a The restrictions are: Where V c ,ω c are the current linear velocity and angular velocity of the robot respectively; V a ,ω a are the absolute value of the robot's linear acceleration and the absolute value of its maximum angular acceleration; Δt is the unit time; When the robot is restricted by obstacles, its linear velocity V s and angular velocity ω s The restrictions are: Where d is the distance between the obstacle and the robot; Based on the above three conditions, a speed space can be jointly determined. The speed activity space is composed of V m 、V a , and V s Determined together; similarly, the acceleration activity space is determined by ω m ,ω, a and ω s Determine together, randomly sample the linear velocity V and angular velocity ω in the velocity activity space and acceleration activity space respectively, and form a series of velocity groups [V i ,ω i ]; Step 11: Speed ​​space evaluation: An evaluation function consisting of three factors, namely light intensity, shading intensity and proximity to the global path, is constructed to evaluate the trajectory represented by each group of speeds in the speed space and select the optimal speed group. Step 12: Determine the optimal trajectory: The speed group that minimizes G(V,ω) is selected as the optimal speed group, so as to obtain the local path trajectory and complete the global path planning and local path planning tasks.

2. The inspection robot path intelligent planning method integrating GPR and DWA algorithms according to claim 1 is characterized in that The formula of the evaluation function is as follows: G(V,ω)=σ(β·B(V i ,oh i )+α·S max (V i ,oh i )+γ·P(V i ,oh i )) In the formula, α, β and γ are the coefficients of the evaluation function, σ represents normalization, d, is the distance between the obstacle and the robot, d is the distance between the robot and the end point, (x a ,y a ) is the coordinate of the robot’s trajectory end point under the current speed group, (x j ,y j ) is the global path coordinate of the robot, n is the number of global path trajectory coordinates; m is the number of random samples in the speed group.

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