A robot path planning method suitable for visible light communication environment

Abstract

The present invention discloses a robot path planning method suitable for a visible light communication environment, comprising: constructing a light intensity grid map and an obstacle grid map for a scene to be path planned using visible light communication; fusing the light intensity grid map with the obstacle grid map, dividing the grids in the fused map into multiple levels, and setting different light intensity costs for the grids of each level to obtain a light intensity obstacle grid map; performing path planning based on an A* algorithm in the light intensity obstacle grid map; wherein, when calculating the path cost by using the A* algorithm for path planning, the comprehensive path length cost and the light intensity cost are used as the total cost; and controlling the robot to move along the planned path in the visible light communication environment. The present invention increases the light intensity map by building a virtual scene, optimizes the A* algorithm, and makes the planned path more suitable for the visible light communication scene, broadening the application scenarios of visible light communication and robots.

Description

Technical Field

[0001] The present invention relates to the technical field of visible light communication and robot path planning, and in particular to a robot path planning method suitable for a visible light communication environment. Background Art

[0002] With the rapid development of light-emitting diodes (LEDs) and the continuous update of high-speed wireless access requirements, visible light communication (VLC) technology has achieved rapid development in recent years. VLC is a technology that uses the intensity changes of LED light to transmit information. This technology can be used for information transmission while providing lighting functions. LED visible light communication is to load the data information to be transmitted onto the light beam, use air as the transmission medium, and use a point-to-point method to transmit data. Compared with traditional RF radio communication, visible light communication has the advantages of good human safety, no spectrum license, no electromagnetic interference, and high confidentiality. It can be used in special occasions where RF applications are restricted, such as hospitals, gas stations, ammunition depots, and nuclear power plants.

[0003] In confidential factory environments, visible light communication, as an emerging communication technology, has unique advantages. In visible light communication, light intensity is a key parameter to ensure communication quality. In recent years, the application of intelligent AGVs and robots in factories has become more and more common, which has practical significance for the construction of smart factory systems. Path planning is an inevitable key step in the work process of intelligent robots. Factories require robots at work to overcome complex environmental conditions and have high requirements for path planning efficiency. By giving the robot's target point in a known environment, planning a path planning problem that is safe and feasible without expansion from the starting point to the target point can complete the factory work task.

[0004] As a typical heuristic path planning method, A* has the advantages of simple principle, fast planning speed and easy implementation. It has been widely used in path planning and has achieved rich research results.

[0005] The heuristic function of the traditional A* path planning algorithm generally consists of two parts: the actual cost from the starting point to the current node N, and the cost of the best path from the current node N to the target point.

[0006] In the traditional A* algorithm, only the path cost is considered and it cannot adapt to the requirements of the visible light communication system for light intensity, which is not suitable for confidential factories using visible light communication. For example, the patent application with application number CN202211082515.1 discloses a path planning method for a forest operation robot based on the A* algorithm with dynamic weights, which uses a binocular depth camera and a multi-line laser radar to collect forest map information when the forest operation robot is running and construct a two-dimensional grid map model; based on the two-dimensional grid map model, the starting node S and target node G of the forest operation robot are determined; the A* algorithm based on the search priority neighborhood is executed cyclically to search for paths on the two-dimensional grid map model to obtain the planned path; the planned path is smoothed based on the non-uniform rational B-spline curve with dynamic weights to obtain the final path.

[0007] The above scheme improves the working efficiency of the forest operation robot, reduces the search nodes, and makes the path smoother and more in line with the driving path of the forest operation robot. However, this type of method only optimizes the distance factor and planning efficiency in the path planning algorithm, and is not applicable in the visible light communication system. Summary of the invention

[0008] The present invention provides a robot path planning method suitable for a visible light communication environment, so as to solve the technical problem that the existing path planning method is not suitable for the visible light communication environment.

[0009] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0010] On the one hand, the present invention provides a robot path planning method applicable to a visible light communication environment, and the robot path planning method applicable to a visible light communication environment comprises:

[0011] For a scene to be path planned using visible light communication, construct a light intensity grid map representing the distribution of light intensity in the scene and an obstacle grid map representing the distribution of obstacles in the scene;

[0012] The light intensity grid map is merged with the obstacle grid map, and the grids in the merged map are divided into multiple levels according to the requirements of different modulation modes of visible light communication on light intensity, and different light intensity costs are set for grids of each level to obtain a light intensity obstacle grid map;

[0013] Performing path planning based on the A* algorithm in the light intensity obstacle grid map; wherein, when calculating the path cost by using the A* algorithm for path planning, the path length cost and the light intensity cost are integrated as the total cost;

[0014] The robot is controlled to move along the planned path in a visible light communication environment.

[0015] Furthermore, the process of constructing the obstacle grid map includes:

[0016] For a scene to be path planned using visible light communication, obtain a corresponding scene map;

[0017] Based on the scene map, the obstacles on the scene map are expanded to construct a virtual scene corresponding to the scene, and the virtual scene is rasterized to obtain an obstacle grid map.

[0018] Furthermore, the process of constructing the light intensity grid map includes:

[0019] For a scenario where path planning is to be performed using visible light communication, light source parameters of a signal source are obtained;

[0020] Building a virtual scene identical to the scene, and adding a light source component identical to the actual light source parameters obtained in the virtual scene, performing optical simulation, and obtaining a light intensity distribution diagram of the scene;

[0021] According to the grid division in the obstacle grid map, the light intensity distribution map is grid-divided with the same particle density as the obstacle grid map to obtain a light intensity grid map.

[0022] Furthermore, when using the A* algorithm to calculate the path cost for path planning, the heuristic function is:

[0023] f(n)=g(n)+h(n)+l(n)+m(n)

[0024] Among them, f(n) is the total cost of the robot from the starting point to the end point in the planned path, g(n) is the actual path length cost from the starting point to the current node n; h(n) is the estimated path cost from the current node n to the end point; l(n) is the actual path light intensity cost of the current node n; m(n) is the estimated light intensity cost from the current node n to the end point.

[0025] Furthermore, the estimated cost of the path from the current node n to the end point is the Euclidean distance.

[0026] Furthermore, path planning is performed based on the A* algorithm in the light intensity obstacle grid map, including:

[0027] Set the robot's starting and ending points;

[0028] Starting from the grid where the starting point is located, the heuristic function is applied to the surrounding grids respectively, the total cost of each surrounding grid is calculated, and the grid with the smallest total cost is selected as the next moving point;

[0029] Continue to execute path planning until you reach the end point and get the final planned path.

[0030] On the other hand, the present invention further provides an electronic device, comprising a processor and a memory; wherein the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the above method.

[0031] In yet another aspect, the present invention further provides a computer-readable storage medium, wherein the storage medium stores at least one instruction, and the instruction is loaded and executed by a processor to implement the above method.

[0032] The beneficial effects brought about by the technical solution provided by the present invention include at least:

[0033] The present invention optimizes the A* algorithm by building a virtual scene and adding a light intensity distribution map, so that the planned path can ensure the efficiency and stability of visible light communication while staying away from obstacles, thereby increasing the safety and stability of the robot during work. The A* path planning algorithm is improved by the light intensity distribution in the scene. By adding a light intensity parameter to the heuristic function, it can be ensured that the robot moves on a path where the light intensity meets the requirements of visible light communication, and searches for the shortest path while ensuring effective communication. Thereby broadening the application scenarios of visible light communication and robots. Moreover, in terms of map processing, the technical solution of the present invention determines the path of the robot by considering the positions of the starting point and the target point, so that the algorithm can be applied to work tasks with various requirements. It has strong practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0035] Figure 1 is a schematic diagram of an execution flow of a robot path planning method applicable to a visible light communication environment provided by an embodiment of the present invention;

[0036] Figure 2 An obstacle grid map of a virtual scene corresponding to a scene to be path planned using visible light communication provided by an embodiment of the present invention;

[0037] Figure 3 is an optical simulation diagram of a virtual scene corresponding to a scene to be path planned using visible light communication provided by an embodiment of the present invention;

[0038] Figure 4A two-dimensional grid map of virtual scene comprehensive obstacles and light intensity corresponding to a scene to be path planned using visible light communication provided by an embodiment of the present invention;

[0039] Figure 5 It is a virtual scene and a planned path map corresponding to the scene to be path planned using visible light communication provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0041] First embodiment

[0042] First of all, it should be noted that for confidential environments, the use of visible light communication can ensure communication efficiency while meeting confidentiality requirements. Visible light communication requires that the light intensity at the location of the signal receiver reaches a certain standard to ensure the stability of communication. Therefore, for environments using visible light communication systems, in addition to considering the impact of obstacles during the robot path planning process, it is also necessary to consider the light intensity at each location to prevent the robot from entering an area with too low light intensity, resulting in large communication delays or even interruptions.

[0043] In view of the above environment, in order to plan a safe path with short distance and high light intensity for the robot to move, the present embodiment provides a robot path planning method suitable for visible light communication environment based on the improved A* algorithm; this method adds a parameter representing the light intensity to the heuristic function of the traditional A* path planning algorithm, so that when calculating the movement cost, it takes into account whether the surrounding environment can guarantee visible light communication. Using the heuristic function of this embodiment, the robot can move more safely and stably in the visible light communication system without increasing the computational complexity and time complexity of the algorithm.

[0044] Specifically, the execution process of the robot path planning method of this embodiment is as follows: Figure 1 As shown, including:

[0045] S1, for a scene to be path planned using visible light communication, construct a light intensity grid map representing the distribution of light intensity in the scene and an obstacle grid map representing the distribution of obstacles in the scene;

[0046] Specifically, in this embodiment, the process of constructing the obstacle grid map includes:

[0047] For a scene to be path planned using visible light communication, obtain a corresponding scene map;

[0048] Based on the scene map, the obstacles on the scene map are expanded, a virtual scene is constructed in Unity3D to construct a virtual scene that is 1:1 restored with the scene, and the virtual scene is rasterized to obtain an obstacle grid map that is easy to observe and highly operable, such as Figure 2 shown.

[0049] Furthermore, the process of constructing the light intensity grid map includes:

[0050] For a scenario where path planning is to be performed using visible light communication, light source parameters of a signal source are obtained;

[0051] A virtual scene identical to the scene is constructed by using the DIALux software, and a light source component identical to the actual light source parameters obtained is added to perform optical simulation to obtain a light intensity distribution diagram of the scene;

[0052] According to the grid division in the obstacle grid map, the light intensity distribution map is divided into grids with the same particle density as the obstacle grid map to obtain a light intensity grid map, such as Figure 3 shown.

[0053] S2, fusing the light intensity grid map with the obstacle grid map, and dividing the grids in the fused map into multiple levels according to the requirements of different modulation modes of visible light communication on light intensity, and setting different light intensity costs for grids of each level, to obtain a light intensity obstacle grid map;

[0054] Specifically, in this example, the implementation process of the above S2 is as follows:

[0055] The two-dimensional light intensity grid map obtained by DIALux software simulation is imported into Unity3D and combined with the two-dimensional obstacle grid map to obtain a two-dimensional grid map of comprehensive obstacles and light intensity, such as Figure 4 shown.

[0056] Furthermore, according to the requirements of different modulation modes of visible light communication for light intensity, this embodiment divides the grids in the light intensity obstacle grid map into four levels, each level corresponds to a different light intensity cost, which is set to 1.1, 2.1, 3.1 and impassable respectively, and the path length cost uses the Manhattan distance - the distance between two points in the north-south direction plus the distance in the east-west direction, that is, d(i, j) = |x i -x j |+|y i -y j |.

[0057] S3, performing path planning based on the A* algorithm in the light intensity obstacle grid map; wherein, when the A* algorithm is used to calculate the path cost for path planning, the path length cost and the light intensity cost are integrated as the total cost;

[0058] Specifically, in this example, the implementation process of the above S3 is as follows:

[0059] Through Unity3D software, add the target robot, and load the path planning algorithm script onto the simulation robot component to perform path planning. The path planning algorithm used in this embodiment is the A* path planning algorithm, and this embodiment uses a heuristic function with a light intensity parameter added to the traditional A* path planning algorithm to perform path planning on a two-dimensional plane; the heuristic function in this embodiment is:

[0060] f(n)=g(n)+h(n)+l(n)+m(n)

[0061] Among them, f(n) is the total cost of the robot from the starting point to the end point in the planned path, g(n) is the actual path length cost from the starting point to the current node n; h(n) is the estimated path cost from the current node n to the end point, which is the Euclidean distance; l(n) is the actual path light intensity cost of the current node n; m(n) is the estimated light intensity cost from the current node n to the end point; that is, the light intensity is added to the heuristic function of the traditional A* algorithm;

[0062] Based on the above, this embodiment performs path planning on a two-dimensional map in Unity3D based on the improved A* algorithm, comprehensively considers the two parts of the map, calculates the path length cost and the light intensity cost to obtain the total cost; thereby completing the path planning that comprehensively considers the light intensity information and obstacle information, and obtains the optimal path.

[0063] The process of executing path planning is as follows: first set the starting point and end point of the robot, start from the grid where the starting point is located, apply the above heuristic function to the grids of the surrounding nine-square grid respectively, calculate the total cost of the surrounding grids, and select the grid with the smallest total cost as the next moving point; loop the calculation and continue to execute the A* path planning algorithm until reaching the end point, completing the path planning with the smallest total cost suitable for the visible light communication system.

[0064] The path obtained in this embodiment is as follows Figure 5 The planned paths are basically located where the light intensity meets the communication requirements, allowing the robot to stay away from obstacles and communication blind spots. Compared with the traditional A* algorithm, the robot's stability in the visible light communication system is further improved.

[0065] S4, controlling the robot to move along the planned path in the visible light communication environment.

[0066] Among them, when the distance between the robot's moving point and the destination point is less than a grid size (the robot's own size is close to the grid size), the robot is considered to have reached the destination point and the mission is completed.

[0067] In summary, this embodiment provides a robot path planning method suitable for visible light communication environment based on the improved A* algorithm. By building a virtual scene and adding a light intensity distribution map, the A* algorithm is optimized so that the planned path can ensure the efficiency and stability of visible light communication while staying away from obstacles, thereby increasing the safety and stability of the robot during work. Through the distribution of light intensity in the scene, the A* path planning algorithm is improved. By adding a light intensity parameter to the heuristic function, it can be ensured that the robot moves on a path where the light intensity meets the requirements of visible light communication, and searches for the shortest path while ensuring effective communication. Thereby broadening the application scenarios of visible light communication and robots. Moreover, in terms of map processing, this method determines the path of the robot by considering the positions of the starting point and the target point, so that the algorithm can be applied to work tasks with various requirements. It has strong practicality.

[0068] Second embodiment

[0069] This embodiment provides an electronic device, which includes a processor and a memory; wherein the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method of the first embodiment.

[0070] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) and one or more memories, wherein the memory stores at least one instruction, and the instruction is loaded by the processor to execute the above method.

[0071] Third embodiment

[0072] This embodiment provides a computer-readable storage medium, which stores at least one instruction, and the instruction is loaded and executed by a processor to implement the method of the first embodiment. The computer-readable storage medium may be a ROM, a random access memory, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc. The instructions stored therein may be loaded by a processor in a terminal to execute the method.

[0073] In addition, it should be noted that the present invention can be provided as a method, an apparatus or a computer program product. Therefore, the embodiments of the present invention can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.

[0074] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0075] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable terminal device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0076] It should also be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0077] Finally, it should be noted that the above is a preferred embodiment of the present invention. It should be pointed out that although the preferred embodiment of the present invention has been described, for those skilled in the art, once the basic creative concept of the present invention is known, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention. Therefore, the attached claims are intended to be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the embodiments of the present invention.

Claims

1. A robot path planning method suitable for a visible light communication environment, characterized in that: include: For a scene to be path planned using visible light communication, construct a light intensity grid map representing the distribution of light intensity in the scene and an obstacle grid map representing the distribution of obstacles in the scene; The light intensity grid map is merged with the obstacle grid map, and the grids in the merged map are divided into multiple levels according to the requirements of different modulation modes of visible light communication on light intensity, and different light intensity costs are set for grids of each level to obtain a light intensity obstacle grid map; Performing path planning based on the A* algorithm in the light intensity obstacle grid map; wherein, when calculating the path cost by using the A* algorithm for path planning, the path length cost and the light intensity cost are integrated as the total cost; The robot is controlled to move along the planned path in a visible light communication environment.

2. The robot path planning method applicable to a visible light communication environment according to claim 1, characterized in that: The process of constructing the obstacle grid map includes: For a scene to be path planned using visible light communication, obtain a corresponding scene map; Based on the scene map, the obstacles on the scene map are expanded to construct a virtual scene corresponding to the scene, and the virtual scene is rasterized to obtain an obstacle grid map.

3. The robot path planning method applicable to a visible light communication environment as claimed in claim 1, characterized in that: The process of constructing the light intensity grid map includes: For a scenario where path planning is to be performed using visible light communication, light source parameters of a signal source are obtained; Building a virtual scene identical to the scene, and adding a light source component identical to the actual light source parameters obtained in the virtual scene, performing optical simulation, and obtaining a light intensity distribution diagram of the scene; According to the grid division in the obstacle grid map, the light intensity distribution map is grid-divided with the same particle density as the obstacle grid map to obtain a light intensity grid map.

4. The robot path planning method applicable to a visible light communication environment according to claim 1, characterized in that: When using the A* algorithm to calculate the path cost for path planning, the heuristic function is: f(n)=g(n)+h(n)+l(n)+m(n) Among them, f(n) is the total cost of the robot from the starting point to the end point in the planned path, g(n) is the actual path length cost from the starting point to the current node n; h(n) is the estimated path cost from the current node n to the end point; l(n) is the actual path light intensity cost from the starting point to the current node n; m(n) is the estimated light intensity cost from the current node n to the end point.

5. The robot path planning method applicable to a visible light communication environment as claimed in claim 4, characterized in that: The estimated cost of the path from the current node n to the end point is the Euclidean distance.

6. The robot path planning method applicable to a visible light communication environment as claimed in claim 4, characterized in that: Path planning is performed based on the A* algorithm in the light intensity obstacle grid map, including: Set the robot's starting and ending points; Starting from the grid where the starting point is located, the heuristic function is applied to the surrounding grids respectively, the total cost of each surrounding grid is calculated, and the grid with the smallest total cost is selected as the next moving point; Continue to execute path planning until you reach the end point and get the final planned path.

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