Multi-target path planning method and related device

Abstract

The invention provides a multi-target path planning method and a related device, and belongs to the technical field of path planning. The method comprises the following steps: in a reference cell space, carrying out integral operation on a central point of the reference cell space to obtain an image point of the central point of each reference cell; in the real cell space, performing interpolation calculation on the image point of the central point of each reference cell obtained by the reference cells around each real cell in the real cell space in the reference cell space to obtain a final cell mapping relation; based on the final cell mapping relation, solving the multi-target path planning model to obtain an optimal solution set; and performing path planning on multiple targets according to the obtained optimal solution set to obtain a multi-target path planning result. According to the method, the problems of low accuracy and calculation efficiency of multi-target path planning are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of path planning, and particularly relates to a multi-objective path planning method and related device. Background Art

[0002] AGV (Automated Guided Vehicle) is an efficient handling device and is widely used in fields such as automobile manufacturing, aerospace, and port logistics.

[0003] At present, AGV path planning has developed from single-objective path planning to multi-objective path planning. This not only requires obstacle avoidance but also often involves multiple optimization metrics, such as the energy consumption of the robot's travel route and the path safety factor, etc. There are mutually contradictory relationships among these metrics, restricting each other. Improving the performance of one metric often leads to the decline of at least one other metric, and it is very difficult for multiple metrics to reach the optimal state simultaneously.

[0004] Therefore, the multi-objective path planning problem has been widely concerned. Traditional multi-objective path planning algorithms are difficult to model in a dynamic and complex environment, and the convergence speed of the algorithms is slow, restricting their application in unknown environments.

[0005] At present, the multi-objective path planning method of AGV is in the stage of continuous improvement. Although the geometric line construction method can quickly generate the optimal path, the discontinuity of its trajectory significantly affects the path accuracy. The graph search method based on the A ＊ search algorithm not only takes a long time during execution but also its effectiveness is restricted. Random sampling techniques (such as the probabilistic roadmap method and the rapidly-exploring random tree method) can effectively find the optimal path in a vast environment, but in a local narrow environment, due to insufficient sampling points, it is often difficult to solve the path planning problem.

[0006] With the continuous development of artificial intelligence technology and computer hardware, various intelligent algorithms (such as artificial bee colony algorithm, simulated annealing algorithm, genetic algorithm, particle swarm algorithm, ant colony algorithm, firefly algorithm, and tabu search algorithm) have been widely applied to the multi-objective path planning of automated guided vehicles (AGV), and significant results have been achieved. However, the complex calculation processes of these intelligent algorithms usually cannot meet the real-time requirements, and there are also obvious deficiencies in terms of stability, accuracy, safety, and computational efficiency. Summary of the Invention

[0007] The purpose of the present invention is to provide a multi-objective path planning method and related device for solving the problems of low accuracy and computational efficiency of multi-objective path planning in the prior art.

[0008] To achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a multi-objective path planning method, including the following steps: Construct a multi-objective path planning model, where the multi-objective path planning model takes minimizing the driving path and maximizing safety as optimization objectives; Discretize the working space of the multi-objective path planning model to obtain a real cell space and a reference cell space; In the reference cell space, perform an integral operation on the center points of the reference cell space to obtain the image points of the center points of each reference cell; In the real cell space, perform interpolation calculation on the image points of the center points of each reference cell obtained from the reference cells around each real cell in the reference cell space to obtain the final cell mapping relationship; Based on the final cell mapping relationship, solve the multi-objective path planning model to obtain an optimal solution set; According to the obtained optimal solution set, perform path planning for multiple objectives to obtain a multi-objective path planning result.

[0009] A further improvement of the present invention is that in the step of performing an integral operation on the center points of the reference cell space in the reference cell space to obtain the image points of the center points of each reference cell, a numerical integration method is specifically used to perform an integral operation on the center points of the reference cell space to obtain the image points of the center points of each reference cell.

[0010] A further improvement of the present invention is that in the step of performing interpolation calculation on the image points of the center points of each reference cell obtained from the reference cells around each real cell in the reference cell space in the real cell space to obtain the final cell mapping relationship, the free gradient rule is specifically used to perform interpolation calculation on the image points of the center points of each reference cell obtained from the reference cells around each real cell in the reference cell space to obtain the final cell mapping relationship.

[0011] A further improvement of the present invention is that after performing interpolation calculation on the image points of the center points of each reference cell obtained from the reference cells around each real cell in the reference cell space in the real cell space to obtain the final cell mapping relationship, it further includes: obtaining a periodic cell based on the final cell mapping relationship.

[0012] A further improvement of the present invention is that the space where the periodic cell is located is further subdivided by using a set-oriented subdivision technique.

[0013] A further improvement of the present invention lies in that in the step of solving the multi-objective path planning model based on the final cell mapping relationship to obtain an optimal solution set, the multi-objective path planning model is specifically solved based on a database to obtain the optimal solution set.

[0014] A further improvement of the present invention lies in that the optimal solution set is a Pareto set.

[0015] In a second aspect, the present invention provides a multi-objective path planning system, including a multi-objective path planning model construction module, a discretization module, an integral operation module, a final cell mapping relationship acquisition module, a multi-objective path planning model solving module, and a multi-objective path planning module; The multi-objective path planning model construction module is used to construct a multi-objective path planning model, and the multi-objective path planning model takes minimizing the driving path and maximizing safety as optimization objectives; The discretization module is used to discretize the workspace of the multi-objective path planning model to obtain a real cell space and a reference cell space; The integral operation module is used to perform integral operations on the center points of the reference cell space in the reference cell space to obtain the image points of the center points of each reference cell; The final cell mapping relationship acquisition module is used to perform interpolation calculations on the image points of the center points of each reference cell obtained in the reference cell space for the reference cells around each real cell in the real cell space to obtain the final cell mapping relationship; The multi-objective path planning model solving module is used to solve the multi-objective path planning model based on the final cell mapping relationship to obtain an optimal solution set; The multi-objective path planning module is used to perform path planning on multiple objectives according to the obtained optimal solution set to obtain a multi-objective path planning result.

[0016] In a third aspect, the present invention provides an electronic device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the multi-objective path planning method introduced above are implemented.

[0017] In a fourth aspect, the present invention provides a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the multi-objective path planning method introduced above are implemented.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The present invention belongs to an improved invention. Compared with the existing multi-objective path planning methods, on the one hand, the present invention performs integral operations and interpolation calculations in the reference cell space and the real cell space respectively. The integral operation has high accuracy, and the interpolation calculation has a fast speed. Interpolating based on the image points that have been integrally operated in the reference cell space can not only reduce the discretization error, thereby improving the accuracy of the subsequent path planning, but also improve the operation efficiency, thus effectively solving the problems of low accuracy and low calculation efficiency of multi-objective path planning in the prior art.

[0019] Furthermore, the present invention also discloses that the space where the periodic cell is located is subdivided by using the set-oriented subdivision technology. On the one hand, subdividing the space where the periodic cell is located can improve the accuracy of the subsequent path planning. On the other hand, the set-oriented subdivision technology adopts a parallel acceleration method, with high calculation efficiency, and thus also improves the calculation efficiency of subdividing the space where the periodic cell is located. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flowchart of the multi-objective path planning method of the present invention; Figure 2 is a schematic diagram of the multi-objective path planning system of the present invention; Figure 3 is a flowchart of the multi-objective path planning method in Embodiment 3 of the present invention; Figure 4 is a schematic diagram of the real cell space and the reference cell space in Embodiment 3 of the present invention; Figure 5 is a schematic diagram of the integral operation and interpolation calculation principles of the real cell space and the reference cell space in Embodiment 3 of the present invention; Figure 6 is a flowchart related to the Pareto set in Embodiment 3 of the present invention; Figure 7 is a schematic diagram of the structure of the electronic device of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] To further understand the content of the present invention, the following provides a detailed description of the present invention with reference to the drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention and not for limiting it.

[0022] The multi-objective path planning method proposed by the present invention performs an integral operation on the center points of the reference cell space in the reference cell space to obtain the image points of the center points of each reference cell; in the real cell space, interpolation calculations are performed on the image points of the center points of each reference cell obtained in the reference cell space for the reference cells around each real cell in the real cell space to obtain the final cell mapping relationship; based on the final cell mapping relationship, the multi-objective path planning model is solved to obtain an optimal solution set; and multi-objective path planning is performed according to the obtained optimal solution set to obtain the multi-objective path planning result. Compared with the prior art, the present invention effectively solves the problems of low accuracy and low computational efficiency in multi-objective path planning in the prior art.

[0023] Embodiment 1: The flow chart of the multi-objective path planning method of the present invention is as Figure 1 shown, and the multi-objective path planning method of the present invention includes the following steps: S1. Construct a multi-objective path planning model, where the multi-objective path planning model takes minimizing the driving path and maximizing safety as optimization objectives.

[0024] S2. Discretize the working space of the multi-objective path planning model to obtain a real cell space and a reference cell space.

[0025] S3. In the reference cell space, perform an integral operation on the center points of the reference cell space to obtain the image points of the center points of each reference cell.

[0026] S4. In the real cell space, perform interpolation calculations on the image points of the center points of each reference cell obtained in the reference cell space for the reference cells around each real cell in the real cell space to obtain the final cell mapping relationship.

[0027] S5. Based on the final cell mapping relationship, solve the multi-objective path planning model to obtain an optimal solution set.

[0028] S6. Perform multi-objective path planning according to the obtained optimal solution set to obtain the multi-objective path planning result.

[0029] Embodiment 2: The schematic diagram of the multi-objective path planning system of the present invention is as Figure 2 shown, and the multi-objective path planning system of the present invention includes a multi-objective path planning model construction module, a discretization module, an integral operation module, a final cell mapping relationship acquisition module, a multi-objective path planning model solution module, and a multi-objective path planning module.

[0030] Among them, the multi-objective path planning model construction module is used to construct a multi-objective path planning model, and the multi-objective path planning model takes minimizing the driving path and maximizing safety as optimization objectives.

[0031] The discrete module is used to discretize the working space of the multi-objective path planning model to obtain a real cell space and a reference cell space.

[0032] The integral operation module is used to perform integral operations on the center points of the reference cell space in the reference cell space to obtain the image points of the center points of each reference cell.

[0033] The final cell mapping relationship acquisition module is used to perform interpolation calculations on the image points of the center points of each reference cell obtained from the reference cells around each real cell in the real cell space in the real cell space to obtain the final cell mapping relationship.

[0034] The multi-objective path planning model solving module is used to solve the multi-objective path planning model based on the final cell mapping relationship to obtain an optimal solution set.

[0035] The multi-objective path planning module is used to perform path planning for multiple objectives according to the obtained optimal solution set to obtain a multi-objective path planning result.

[0036] Embodiment 3: The flowchart of the multi-objective path planning method of the present invention is as Figure 3 shown. In this embodiment, the multi-objective path planning is specifically performed taking an AGV as an example. The multi-objective path planning method of the present invention includes the following steps: S1. Construct a multi-objective path planning model, where the multi-objective path planning model takes minimizing the driving path and maximizing safety as optimization objectives.

[0037] First, construct a multi-objective path planning model (specifically an AGV multi-objective path planning model in this embodiment). The multi-objective path planning model takes minimizing the driving path (also called the path length) and maximizing safety as optimization objectives (also called evaluation indicators). Safety is represented by the distance between the driving path and surrounding obstacles.

[0038] S2. Discretize the working space of the multi-objective path planning model to obtain a real cell space and a reference cell space.

[0039] Discretize (coarse discretization) the working space (also called the space) of the multi-objective path planning model to obtain a real cell space (also called the cell space) and a reference cell space.

[0040] The schematic diagrams of the real cell space and the reference cell space are as Figure 4 shown, Figure 4 where the black solid line grid represents the cell state space (real cell space) used in the actual generalized cell mapping. Multiple sampling points are set in each cell and represented by black solid dots. Figure 4The dashed grid represents the reference cell space. Using the idea of simple cell mapping, the center point of each reference cell is selected as the sampling point, which is represented by a solid gray dot.

[0041] S3. In the reference cell space, perform an integration operation on the center points of the reference cell space to obtain the image points of the center points of each reference cell.

[0042] In this step, the numerical integration method is specifically used to perform the integration operation on the center points of the reference cell space to obtain the image points of the center points of each reference cell. The relevant principles of the integration operation are as Figure 5 shown.

[0043] S4. In the real cell space, perform interpolation calculation on the image points of the center points of each reference cell obtained in the reference cell space for the reference cells around each real cell in the real cell space to obtain the final cell mapping relationship.

[0044] In this step, the free gradient rule is specifically used to perform interpolation calculation on the image points of the center points of each reference cell obtained in the reference cell space for the reference cells around each real cell in the real cell space to obtain the final cell mapping relationship (also called the interpolated cell mapping). The relevant schematic diagram of the interpolation calculation is as Figure 5 shown.

[0045] After obtaining the final cell mapping relationship in this step, it further includes: obtaining the periodic cell based on the final cell mapping relationship.

[0046] In this step, the space where the periodic cell is located is also subdivided by using the set-oriented subdivision technology. S5. Based on the final cell mapping relationship, solve the multi-objective path planning model to obtain the optimal solution set.

[0047] Based on the final cell mapping relationship (also called the global database for multi-objective optimization of the AGV path), solve the multi-objective path planning model to obtain the optimal solution set. The optimal solution set is the Pareto set (also called the Pareto set).

[0048] Specifically, in this step, first construct the global database for multi-objective optimization of the path. The search strategy is to update the non-dominated cost set of the cell based on the cost set from the cell to the target cell and the cost transfer information between the cell and its adjacent cells. Then calculate the optimal path based on the global database for multi-objective optimization of the path (also called the database). Input the position of the starting cell, and read all the non-dominated cost sets from the starting cell to the target cell from the global database for multi-objective optimization of the path, so that the optimal solution set can be found.

[0049] The flow chart related to the Pareto set finally obtained in this embodiment is as Figure 6 shown. The following will elaborate on the process related to the finally obtained Pareto set: 1. Coarsely discretize the workspace area (also called the work space); 2. Perform interpolation calculation on the image points of the center points of each reference cell obtained from the reference cells around each real cell in the real cell space in the reference cell space (also called constructing a one-step interpolation cell mapping); 3. Extract the periodic cells; 4. Subdivide the space where the periodic cells are located and repeat steps 1 to 3; 5. Check the dominance relationship of the periodic cells; 6. Obtain the Pareto set.

[0050] S6. Perform path planning for multiple objectives according to the obtained optimal solution set to obtain the multiple-objective path planning result.

[0051] Embodiment 4: Please refer to Figure 7 As shown, the present invention also provides an electronic device 100 for a multi-objective path planning method; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0052] The memory 101 can be used to store the computer program 103. The processor 102 realizes the steps of the multi-objective path planning method described in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the electronic device 100 (such as audio data, etc.). In addition, the memory 101 may include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0053] The at least one processor 102 may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or the processor 102 may also be any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and connects various parts of the entire electronic device 100 through various interfaces and lines.

[0054] The memory 101 in the electronic device 100 stores multiple instructions to implement a multi-objective path planning method, and the processor 102 can execute the multiple instructions to implement: Construct a multi-objective path planning model, where the multi-objective path planning model takes minimizing the driving path and maximizing safety as optimization objectives; Discretize the workspace of the multi-objective path planning model to obtain a real cell space and a reference cell space; In the reference cell space, perform an integration operation on the center points of the reference cell space to obtain the image points of the center points of each reference cell; In the real cell space, perform interpolation calculation on the image points of the center points of each reference cell obtained from the reference cells around each real cell in the reference cell space to obtain the final cell mapping relationship; Based on the final cell mapping relationship, solve the multi-objective path planning model to obtain an optimal solution set; According to the obtained optimal solution set, perform path planning for multiple objectives to obtain a multi-objective path planning result.

[0055] Embodiment 5: If the modules / units integrated in the electronic device 100 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, and read-only memory (ROM, Read-Only Memory).

[0056] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, system, or computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0057] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0058] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0059] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the steps for realizing the functions specified in one process or a plurality of processes and / or boxes Figure 1 one process or a plurality of processes and / or boxes Figure 1 steps for realizing the functions specified in one box or a plurality of boxes.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific implementation manners of the present invention, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A multi-objective path planning method, characterized in that, Including the following steps: Construct a multi-objective path planning model, where the multi-objective path planning model takes minimizing the driving path and maximizing safety as the optimization objectives; Discretize the working space of the multi-objective path planning model to obtain a real cell space and a reference cell space; In the reference cell space, perform an integral operation on the center points of the reference cell space to obtain the image points of the center points of each reference cell; In the real cell space, perform interpolation calculation on the image points of the center points of each reference cell obtained from the reference cells around each real cell in the reference cell space to obtain the final cell mapping relationship; Based on the final cell mapping relationship, solve the multi-objective path planning model to obtain an optimal solution set; According to the obtained optimal solution set, perform path planning for multiple objectives to obtain the multi-objective path planning result.

2. The multi-objective path planning method according to claim 1, characterized in that In the step of performing an integral operation on the center points of the reference cell space in the reference cell space to obtain the image points of the center points of each reference cell, specifically use the numerical integration method to perform the integral operation on the center points of the reference cell space to obtain the image points of the center points of each reference cell.

3. The multi-objective path planning method according to claim 1, characterized in that In the step of performing interpolation calculation on the image points of the center points of each reference cell obtained from the reference cells around each real cell in the reference cell space in the real cell space to obtain the final cell mapping relationship, specifically use the free gradient rule to perform interpolation calculation on the image points of the center points of each reference cell obtained from the reference cells around each real cell in the reference cell space to obtain the final cell mapping relationship.

4. The multi-objective path planning method according to claim 1, wherein After performing interpolation calculation on the image points of the center points of each reference cell obtained from the reference cells around each real cell in the reference cell space in the real cell space to obtain the final cell mapping relationship, it further includes: obtaining periodic cells based on the final cell mapping relationship.

5. The multi-objective path planning method according to claim 4, wherein Also use the set-oriented subdivision technology to subdivide the space where the periodic cells are located.

6. The multi-objective path planning method according to claim 1, characterized in that In the step of solving the multi-objective path planning model based on the final cell mapping relationship to obtain the optimal solution set, specifically solve the multi-objective path planning model based on the database to obtain the optimal solution set.

7. The multi-objective path planning method according to claim 1, characterized in that, The optimal solution set is the Pareto set.

8. A multi-objective path planning system, characterized in that, Including a multi-objective path planning model construction module, a discretization module, an integral operation module, a final cell mapping relationship acquisition module, a multi-objective path planning model solving module, and a multi-objective path planning module; The multi-objective path planning model construction module is used to construct a multi-objective path planning model, where the multi-objective path planning model takes minimizing the driving path and maximizing safety as the optimization objectives; The discretization module is used to discretize the working space of the multi-objective path planning model to obtain a real cell space and a reference cell space; The integral operation module is used to perform an integral operation on the center points of the reference cell space in the reference cell space to obtain the image points of the center points of each reference cell; The final cell mapping relationship obtaining module is used to perform interpolation calculation on the image points of the central points of each reference cell obtained from the reference cells around each real cell in the real cell space in the real cell space, so as to obtain the final cell mapping relationship; The multi-objective path planning model solving module is used to solve the multi-objective path planning model based on the final cell mapping relationship to obtain an optimal solution set; The multi-objective path planning module is used to perform path planning on multiple objectives according to the obtained optimal solution set to obtain a multi-objective path planning result.

9. An electronic device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the multi-objective path planning method described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-objective path planning method described in any one of claims 1 to 7.

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