A method for compressing and transmitting path information of strip-shaped autonomous rescue vehicles in dangerous environments

Abstract

The present invention discloses a method for compressing and transmitting the path information of a strip-shaped autonomous rescue vehicle in a dangerous environment. The method is based on the most commonly used grid method environment modeling in the path planning method, and the optimal path is calculated by the existing path planning method. The application number of the existing path planning method is "201810143863.2" and the name is "a strip-shaped robot path planning method based on a self-learning ant colony algorithm". The steps of the path information compression and transmission method are as follows: step 1, completing environment modeling based on the grid method; step 2, planning a path for the rescue vehicle; step 3, compressing the optimal path; step 4, the central control module transmits the compressed path to the robot; step 5, the robot decompresses; step 6: the robot executes the decompressed path P best The path information compression transmission method reduces storage burden, improves transmission efficiency, and is simple in calculation.

Description

Technical Field

[0001] The invention relates to an information compression transmission method, and in particular to a path information compression transmission method for a strip rescue vehicle used in a complex and dangerous environment. Background Art

[0002] Automated Guided Vehicle (AGV), also known as automatic guided transport vehicle or automatic guided transport vehicle. With the development of artificial intelligence technology in recent years, AGV has developed towards intelligence and autonomy. Today, the development of AGV integrates artificial intelligence, information processing, and image processing, involving multiple disciplines such as computers, automatic control, information communication, mechanical design, and electronic technology, and has become a research hotspot.

[0003] Autonomous rescue vehicles are an important application of AGV. Autonomous rescue vehicles are often used for rescue work in dangerous and complex places such as earthquake disasters, mine collapses, building collapses or nuclear facility leaks. In order to transport more rescue supplies or cross narrow passages for rescue, autonomous rescue vehicles are usually designed as strips. The path planning algorithm of the rescue vehicle is one of the important research contents of the rescue vehicle. Its design goal is: the path planning algorithm can control the strip rescue vehicle to rotate and avoid obstacles and cross narrow passages according to its own appearance, position information and surrounding obstacle information to complete the journey from the starting point to the target point and the path is optimal (usually the shortest path distance). The path planning method of the strip rescue vehicle is different from the ordinary path planning method. The goal of the ordinary planning method is to provide an optimal route for safe collision avoidance without considering the external shape of the moving object itself; the planning method of the strip rescue vehicle needs to consider the appearance characteristics of the rescue vehicle. In addition to completing the optimal route planning, the robot's posture must be controlled, such as choosing the rotation time to avoid obstacles or whether it can cross narrow passages. In order for the rescue vehicle to correctly execute the planned path and posture control information, the data structure expressing the planned path information is particularly important. Summary of the invention

[0004] The purpose of the present invention is to design a method for compressing and transmitting path information of a strip-shaped autonomous rescue vehicle in a dangerous environment, which reduces the storage burden and improves the transmission efficiency.

[0005] The technical solution of the present invention is: based on the most commonly used grid method environment modeling in the path planning method, the optimal path is calculated through the existing path planning method; the application number of the existing path planning method is "201810143863.2" and the name is "Roller robot path planning method based on self-learning ant colony algorithm"; the steps of the path information compression transmission method are as follows:

[0006] Step 1: Complete environmental modeling based on the grid method: The central control module establishes a grid map with X rows and Y columns based on the real workspace image information. The upper left corner of the grid map is used as the origin coordinate, and the density of the generated grid map is X×Y, where the cell side length is designed according to the ratio of the robot's size to the real workspace; the cells in the obstacle area of ​​the grid map are marked as "0", called "obstacle cells"; the cells in the feasible area are marked as "1" and called "feasible cells"; mark the starting point position S and the target point position D;

[0007] Step 2: Plan a path for the rescue vehicle: The central control module calculates the optimal path through the existing path planning method. The storage information of the optimal path includes the coordinates of the rescue vehicle and the posture information of the rescue vehicle. Considering that the rescue vehicle is in the shape of a bar, it is represented by two cells in the grid map, called the head cell and the tail cell. The coordinates of the head cell are used as the coordinates of the entire rescue vehicle. The data structure for storing each path information is a generalized table:

[0008] P best =(((x i ,y i ),θ i )) (i=1,2,...,L)

[0009] Where: x∈{1,2,...,X}; y∈{1,2,...,Y}; θ∈{1,2,3,4}

[0010] Where: P best represents the optimal path planned by the algorithm; L represents the length of the generalized table (the number of cells included in the optimal path); i represents the i-th cell in the path; x i Represents the horizontal coordinate of the i-th cell; y i represents the vertical coordinate of the i-th cell; θ i represents the posture of the rescue vehicle at the i-th cell, θ i =1 means the head is on the left and the tail is on the right, θ i =2 means head up and tail down, θ i =3 means the head is right and the tail is left, θ i =4 means head down and tail up;

[0011] Step 3: Optimal path compression: Before the optimal path is transmitted to the rescue vehicle, the central control module completes the compression calculation. The compression formula is as follows:

[0012] η i =((y i -1)·X+x i )×10+θ i

[0013] Where: η iis the compressed path information; this formula compresses the three-value information of the original planned path into an integer, and the compressed information occupies only 1 / 3 of the memory of the original information;

[0014] Step 4: The central control module transmits the compressed path to the robot; the compressed path to be transmitted is as follows:

[0015]

[0016] Step 5: Robot decompression: The robot obtains the planned path Complete path decompression; the decompression formula is as follows:

[0017]

[0018] Among them: the symbol "%" represents the modulo operation; the symbol Indicates the rounding down operation; after the above operation, the path is compressed Will be converted into the original path P best ;

[0019] Step 6: The robot executes the decompressed path P best .

[0020] The present invention adopts the above technical solution, which has the following advantages:

[0021] (1) High compression efficiency. This method compresses the three-value information generalized table (optimal path) consisting of horizontal coordinates, vertical coordinates, and posture into a linear table planning path with single integers as elements through a formula, which can save 2 / 3 of the storage space.

[0022] (2) Shorten the transmission time of path information. Since the storage space occupied by path information is reduced after compression, the speed of path transmission can be greatly improved.

[0023] (3) Simple calculation. The algorithm's compression and decompression formulas are relatively simple, and the compression and decompression calculations will not cause too much calculation pressure on the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is the execution process of the method of the present invention;

[0025] Figure 2 It is the grid method environment modeling of the present invention;

[0026] Figure 3 is the posture of the autonomous rescue vehicle of the present invention; DETAILED DESCRIPTION

[0027] The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings and embodiments, but it should not be understood as limiting the technical solution. Two grids are used in the embodiment to describe the strip rescue vehicle. In the following description, a large number of specific details are given to provide a more thorough understanding of the present invention. However, for those skilled in the art, the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some technical features known in the art are not described.

[0028] Figure 1 The implementation process of this path information compression transmission method is given. Figure 1 ,The following is a detailed description of each step in the method.

[0029] Step S101: The central control module creates a grid map with X rows and Y columns based on the real workspace image information. Figure 2 As shown: the upper left corner of the grid map is taken as the origin coordinate 100, and the density of the generated grid map is X×Y, where the cell side length is designed according to the ratio of the robot's size to the actual workspace; the cells in the obstacle area of ​​the grid map are marked as "0", called "obstacle cells" 200; the cells in the feasible area are marked as "1" and called "feasible cells" 300; the starting point cells are marked 400, and the symbol is marked as S; the target point cells are marked 500, and the symbol is marked as D;

[0030] Step S102: The central control module calculates the optimal path through the existing path planning method. The storage information of the optimal path includes the coordinates of the rescue vehicle and the posture information of the rescue vehicle. The application number of the existing path planning method is "201810143863.2" and the name is "Path Planning Method for Bar Robot Based on Self-learning Ant Colony Algorithm". Considering that the rescue vehicle is in the shape of a bar, it is represented by two cells in the grid map, namely the head cell and the tail cell. The coordinates of the head cell are used as the coordinates of the entire rescue vehicle. The data structure for storing each path information is a generalized table, as shown in formula (1):

[0031] P best =(((x i ,y i ),θ i )) (i=1,2,...,L) (1)

[0032] Where: x∈{1,2,...,X}; y∈{1,2,...,Y}; θ∈{1,2,3,4}

[0033] Where: P bestrepresents the optimal path planned by the algorithm; L represents the length of the generalized table (the number of cells included in the optimal path); i represents the i-th cell in the path; x i Represents the horizontal coordinate of the i-th cell; y i represents the vertical coordinate of the i-th cell; θ i When representing the i-th cell, the posture of the rescue vehicle is, such as Figure 3 Shown: θ i =1 means head left tail right 600, θ i =2 means head to tail 700, θ i =3 means the head is right and the tail is left 800, θ i =4 means head down tail up 900;

[0034] Step S103: Before the optimal path is transmitted to the rescue vehicle, the central control module completes the compression calculation; the compression formula is as follows:

[0035] η i =((y i -1)·X+x i )×10+θ i (2)

[0036] In formula (2), η i is the compressed path information; this formula compresses the three-value information of the original planned path into an integer, and the compressed information occupies only 1 / 3 of the memory of the original information;

[0037] Step S104: The central control module transmits the compressed path to the robot; the compressed path to be transmitted is represented as follows:

[0038]

[0039] Step S105: The robot obtains the planned path Complete path decompression; the decompression formula is as follows:

[0040]

[0041] In formula (4), the symbol “%” represents the modulo operation; the symbol Indicates the rounding down operation; after the above operation, the path is compressed Will be converted into the original path P best ;

[0042] Step S106: The robot executes the decompressed path P best .

Claims

1. A method for compressing and transmitting the path information of a strip autonomous rescue vehicle in a dangerous environment, based on the grid method environment modeling in the path planning method, and calculating the optimal path through the existing path planning method; the steps of the path information compression transmission method are as follows: Step 1. Complete environmental modeling based on the grid method: The central control module establishes a grid map with X rows and Y columns based on the real workspace image information. The upper left corner of the grid map is used as the origin coordinate, and the density of the generated grid map is X×Y, where the cell side length is designed according to the ratio of the robot's size to the real workspace; the cells in the obstacle area of ​​the grid map are marked as "0", called "obstacle cells"; the cells in the feasible area are marked as "1" and called "feasible cells"; mark the starting point position S and the target point position D; Step 2: Plan a path for the rescue vehicle: The central control module calculates the optimal path through the existing path planning method. The storage information of the optimal path includes the coordinates of the rescue vehicle and the posture information of the rescue vehicle. Considering that the rescue vehicle is in the shape of a bar, it is represented by two cells in the grid map, called the head cell and the tail cell. The coordinates of the head cell are used as the coordinates of the entire rescue vehicle. The data structure for storing each path information is a generalized table: P best =((x i ,y i ),θ i ) (i=1,2,...,L) in: x i ∈{1, 2, ..., X}; and i ∈{1, 2, ..., and}; θ i ∈{1, 2, 3, 4} Where: P best represents the optimal path planned by the algorithm; L represents the length of the generalized table; i represents the i-th cell in the path; x i Represents the horizontal coordinate of the i-th cell; y i represents the vertical coordinate of the i-th cell; θ i represents the posture of the rescue vehicle at the i-th cell, θ i =1 means the head is on the left and the tail is on the right, θ i =2 means head up and tail down, θ i =3 means the head is right and the tail is left, θ i =4 means head down and tail up; Step 3: Optimal path compression: Before the optimal path is transmitted to the rescue vehicle, the central control module completes the compression calculation. The compression formula is as follows: or i =((y i -1)·X+x i )×10+θ i Where: η i is the compressed path information; this formula compresses the three-value information of the original planned path into an integer, and the compressed information occupies only 1 / 3 of the memory of the original information; Step 4: The central control module transmits the compressed path to the robot; the compressed path to be transmitted is as follows: Step 5: Robot decompression: The robot obtains the planned path Complete path decompression; The decompression formula is as follows: Among them: the symbol "%" represents the modulo operation; the symbol Indicates the rounding down operation; after the above operation, the path is compressed will be converted into the optimal path P best ; Step 6: The robot executes the decompressed optimal path P best .

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