Three-dimensional path planning grid minimum unit calculation method, planning method and system

Abstract

The invention provides a three-dimensional path planning grid minimum unit calculation method, planning method and system, and belongs to the field of path planning. The grid minimum unit calculation method comprises the following steps: acquiring scene parameters of path planning, wherein the scene parameters comprise a scene minimum feature scale, an unmanned system safety distance, a system size, a scene space range, a system maximum grid number, system maximum calculation time and a path planning precision factor; calculating the maximum computing power of the system; deriving the minimum side length of the system memory constraint, the minimum side length of the system computing power constraint and the minimum side length of the task precision constraint, which respectively correspond to hardware resource limitation, computing power constraint and scene precision requirements; and finally, taking the maximum value of the three as the minimum unit side length of the grid. According to the method, the system memory constraint, the system computing power constraint and the task precision constraint are comprehensively considered, the minimum unit side length of the grid is accurately calculated, and the dynamic balance of the precision and the computing performance is realized on the premise of guaranteeing the scene feature recognition precision and the obstacle avoidance safety.

Description

Technical Field

[0001] The present invention belongs to the field of path planning technology, and specifically relates to a grid minimum unit calculation method for unmanned system three-dimensional map path planning, an unmanned system path planning method using the calculation method, a grid minimum unit calculation system for unmanned system three-dimensional map path planning, and an unmanned system equipped with the calculation system. Background Art

[0002] In the field of three-dimensional path planning for unmanned systems (such as drones, autonomous vehicles, and robots), gridding is a fundamental and commonly used spatial discretization method. It divides a continuous three-dimensional environment into regularly arranged cubic cells (a grid), thereby transforming the complex continuous-space path search problem into a pathfinding problem in a discrete grid map. The quality of the grid map, particularly the side length d of its smallest cell (cell), is a key factor affecting path planning performance. The choice of grid side length directly determines the accuracy and computational efficiency of path planning: smaller values ​​of d allow for more detailed characterization of environmental details, such as narrow passages and small obstacles, capture smaller minimum feature scales, provide a higher obstacle avoidance safety margin, and meet higher accuracy requirements, thereby planning more accurate and safer paths. However, the total number of cells is inversely proportional to d³. Smaller values ​​of d lead to a sharp increase in the number of cells, significantly increasing the computational complexity and computational time of the path search algorithm. This places higher demands on the system's computing power and may exceed the system's maximum cell count or maximum allowed computation time.

[0003] The main problem with existing technologies is the lack of a scientific and unified calculation method for selecting grid size. These methods rely primarily on experience or simple rule-based settings, failing to systematically quantify the complex relationship between the inherent characteristics of a 3D scene and the required task accuracy. This directly results in difficulty achieving an optimal balance between planning accuracy, computational resource consumption, and real-time requirements. Overly conservative selection of a small grid size can lead to computational resource exhaustion (exceeding the maximum grid count or the maximum allowable computation time), resulting in planning failures or sluggish responses, and failure to meet real-time requirements. Overly aggressive selection of a large grid size can lose environmental details, reduce path accuracy, and potentially overlook critical obstacles or narrow feasible pathways, resulting in unsafe, suboptimal, or even infeasible paths that fail to meet task accuracy requirements. Summary of the Invention

[0004] The purpose of the present invention is to solve the problem that the selection of grid minimum unit size in existing three-dimensional path planning relies on experience, lacks a scientific quantitative model, and is difficult to achieve a balance between environmental characterization accuracy, system computing resource limitations and task accuracy requirements. A grid minimum unit calculation method for unmanned system three-dimensional map path planning, an unmanned system path planning method using the calculation method, a grid minimum unit calculation system for unmanned system three-dimensional map path planning and an unmanned system equipped with the calculation system are provided. The methods can comprehensively weigh the scene characteristics, system computing capabilities and task accuracy requirements to calculate the optimal grid side length, so as to fundamentally resolve the contradiction between accuracy and efficiency and ensure the efficiency, accuracy and real-time performance of the unmanned system path planning in complex three-dimensional environments.

[0005] To achieve the above objectives, the technical solutions provided by the present invention are:

[0006] On the one hand, a method for calculating the minimum grid unit for three-dimensional map path planning of an unmanned system is provided, comprising the following steps:

[0007] Step 1: Obtain scene parameters for 3D map path planning, including: scene minimum feature scale, unmanned system safety distance, unmanned system size, scene spatial range, unmanned system maximum grid number, unmanned system maximum calculation time, and path planning precision factor;

[0008] Step 2: Calculate the maximum computing capacity of the unmanned system based on the floating-point computing capacity and computational complexity of the unmanned system;

[0009] Step 3: Based on the scene space range obtained in step 1 and the maximum number of grids in the unmanned system, calculate the minimum side length constrained by the system memory. This value is the minimum grid side length allowed to avoid exceeding the maximum number of grids in the unmanned system.

[0010] Step 4: Based on the scene space range and the maximum computing time of the unmanned system obtained in step 1, as well as the maximum computing power of the unmanned system calculated in step 2, the minimum side length of the system computing power constraint is calculated. This value is the minimum grid side length allowed for path planning by the unmanned system within the maximum computing time and the maximum computing power.

[0011] Step 5: Based on the scene minimum feature scale, unmanned system safety distance, unmanned system size, and path planning precision factor obtained in step 1, calculate the minimum side length of the task accuracy constraint. This value is the minimum grid side length that meets the scene feature recognition and safe obstacle avoidance requirements in the path planning task.

[0012] Step 6: Determine the side length of the minimum grid unit based on the minimum side length constrained by the system memory calculated in step 3, the minimum side length constrained by the system computing power calculated in step 4, and the minimum side length constrained by the task accuracy calculated in step 5.

[0013] Furthermore, in step 2, the calculation formula for the maximum computing power of the unmanned system is:

[0014]

[0015] Where, The maximum computing power for unmanned systems, Floating point computing capability for unmanned systems, , The CPU main frequency of the unmanned system, is the number of CPU cores, The number of CPU single-cycle parallel floating-point operations, is the algorithm efficiency coefficient, is the time complexity of the algorithm.

[0016] Furthermore, in step 3, the method for calculating the minimum side length of the system memory constraint is:

[0017]

[0018] Where, is the minimum side length constrained by system memory, is the maximum number of grids in the unmanned system, 、 、 They are the maximum extents of the scene in the x, y, and z directions respectively.

[0019] Furthermore, in step 4, the calculation method for the minimum side length constrained by the system computing power is:

[0020]

[0021] Where, The minimum side length constrained by the system computing power, The maximum computing time of the unmanned system.

[0022] Furthermore, in step 5, the calculation method of the minimum side length of the task accuracy constraint is:

[0023]

[0024] Where, To constrain the minimum side length for task accuracy, is the minimum characteristic scale of the scene, Safe distance for unmanned systems, For unmanned system size, is the path planning precision factor.

[0025] Furthermore, in step 6, the side length of the smallest grid unit is calculated as follows:

[0026]

[0027] Where, is the side length of the smallest grid unit.

[0028] Another aspect provides a method for unmanned system path planning, comprising the following steps:

[0029] Step (a), calculating the minimum grid unit side length using the above-mentioned minimum grid unit calculation method;

[0030] Step (b), dividing the three-dimensional space into a cubic grid using the calculated value in step (a) as the side length to generate an initial grid map;

[0031] Step (c), executing a path search algorithm based on the generated initial grid map and outputting a planned path;

[0032] Step (d), monitoring path search calculation time and compare it with the maximum computing time of the unmanned system Compare, if , then the path planning precision factor is triggered The dynamic attenuation adjustment is performed and the step (a) is returned to recalculate the minimum grid unit side length, and the grid map is reconstructed with the updated minimum grid unit side length:

[0033]

[0034] Where, is the adjusted path planning precision factor, is the time decay factor.

[0035] Furthermore, the time decay factor The value is 0.5~0.9.

[0036] On the other hand, a grid minimum unit calculation system for three-dimensional map path planning of unmanned systems is provided, comprising a parameter acquisition unit, a calculation processing unit, and a decision-making unit;

[0037] The parameter acquisition unit is used to obtain scene parameters for 3D map path planning, including: scene minimum feature scale, unmanned system safety distance, unmanned system size, scene spatial range, unmanned system maximum grid number, unmanned system maximum calculation time, and path planning precision factor;

[0038] The computing processing unit is used to perform the following: calculating the maximum computing capacity of the unmanned system based on the floating-point computing capacity and computing complexity of the unmanned system; calculating the minimum side length of the system memory constraint based on the acquired scene space range and the maximum number of grids of the unmanned system, which is the minimum grid side length allowed to avoid exceeding the maximum number of grids of the unmanned system; calculating the minimum side length of the system computing power constraint based on the acquired scene space range and the maximum computing time of the unmanned system, and the calculated maximum computing capacity of the unmanned system, which is the minimum grid side length allowed for path planning within the maximum computing time under the maximum computing capacity of the unmanned system; calculating the minimum side length of the task accuracy constraint based on the acquired minimum feature scale of the scene, the safety distance of the unmanned system, the size of the unmanned system, and the path planning precision factor, which is the minimum grid side length that meets the scene feature recognition and safe obstacle avoidance requirements in the path planning task;

[0039] The decision unit is used to determine the side length of the minimum grid unit based on the calculated minimum side length constrained by system memory, minimum side length constrained by system computing power, and minimum side length constrained by task accuracy.

[0040] On the other hand, an unmanned system is provided, which is equipped with the above-mentioned grid minimum unit computing system.

[0041] The advantages of the present invention are:

[0042] 1. The grid minimum unit calculation method proposed in this invention for three-dimensional map path planning of unmanned systems establishes a quantitative mathematical model and comprehensively considers system memory constraints, system computing power constraints, and task accuracy constraints to accurately calculate the side length of the grid minimum unit. This overcomes the blindness of traditional empirical selection methods and achieves a dynamic balance between accuracy and computing performance while ensuring the accuracy of scene feature recognition and obstacle avoidance safety.

[0043] 2. The unmanned system path planning method proposed in this invention uses the calculation of the minimum grid unit as a prerequisite, generates a grid map based on the calculated optimal side length, and performs path search. This ensures the execution efficiency of the path search algorithm by accurately setting the initial grid size, ensuring that planning is completed within the maximum allowable time. At the same time, when a calculation timeout is detected, the attenuation update mechanism of the precision factor is automatically triggered, and the grid size is dynamically increased and re-planning is carried out, which can effectively cope with various complex scenarios.

[0044] 3. The grid minimum unit calculation system for three-dimensional map path planning of unmanned systems proposed in the present invention obtains environmental and task parameters in real time through the parameter acquisition unit, and the calculation processing unit solves the memory-constrained side length, computing power-constrained side length and accuracy-constrained side length in parallel. The decision unit outputs the optimal grid side length. Through hardware collaborative calculation, a scientific balance between resources and accuracy is achieved, memory usage is reduced, and response time is shortened, which significantly improves the efficiency and reliability of the front-end processing of the unmanned system path planning.

[0045] 4. The unmanned system proposed in the present invention, as it is equipped with a grid minimum unit computing system, can ensure path quality and safety through the optimal grid size; at the same time, because the computing system efficiently coordinates hardware resources, it can significantly extend the battery life of the mobile platform and improve the overall task execution efficiency and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The above and / or other features and advantages of the present invention will become more readily understood through the following description with reference to the accompanying drawings, in which:

[0047] Figure 1 It is a flow chart of the method for calculating the minimum grid unit for three-dimensional map path planning of an unmanned system according to the present invention;

[0048] Figure 2 is a flow chart of the unmanned system path planning method of the present invention;

[0049] Figure 3 Schematic diagram of path planning of the method of the present invention and the existing fixed large grid method in an example. DETAILED DESCRIPTION

[0050] The present invention will be described in detail below with reference to the accompanying drawings by means of exemplary embodiments of the present invention. It should be noted that the following detailed description of the present invention is only for the purpose of illustration and is not intended to limit the present invention.

[0051] The present invention addresses the problem that the selection of grid size in existing unmanned system three-dimensional map path planning lacks a scientific and unified calculation method, mainly relies on empirical settings, and cannot systematically quantify the complex relationship between the inherent characteristics of the three-dimensional scene and the task accuracy requirements, resulting in difficulty in achieving an optimal balance between planning accuracy, computing resource consumption and real-time requirements. The present invention provides a grid minimum unit calculation method for unmanned system three-dimensional map path planning, an unmanned system path planning method using the calculation method, a grid minimum unit calculation system for unmanned system three-dimensional map path planning, and an unmanned system equipped with the calculation system.

[0052] Reference Figure 1 The method for calculating the minimum grid unit for three-dimensional map path planning of an unmanned system provided by the present invention comprises the following steps:

[0053] Step S1, obtaining scene parameters for 3D map path planning, including:

[0054] The minimum characteristic scale of the scene, which indicates the size of the smallest obstacle or the minimum characteristic size of other key structures in the scene, can be obtained through 3D scanning or modeling tools;

[0055] Unmanned system safety distance refers to the minimum safe distance between the unmanned system and obstacles, which can be set by mission requirements or system performance parameters;

[0056] Unmanned system dimensions, such as the wingspan of a drone or the width of an unmanned vehicle, can be obtained through physical measurements;

[0057] The scene spatial extent is the maximum extent of the scene in the x, y, and z directions, which can be defined by the scene modeling tool or the task area;

[0058] The maximum number of grids in the unmanned system indicates the maximum number of grids that the system can tolerate, which is limited by the hardware memory and storage resources;

[0059] The maximum computation time of the unmanned system indicates the maximum computation time allowed for the unmanned system to meet real-time requirements or task execution efficiency when performing a three-dimensional path planning task.

[0060] Path planning precision factor, which quantifies the accuracy requirement of the path planning task. Its typical value range is 0.1-0.5, which is usually related to the accuracy requirement of the task scenario (for example, a value of 0.1 represents low accuracy requirement; a value of 0.3 represents medium accuracy requirement; and a value of 0.5 represents high accuracy requirement).

[0061] Step S2, calculating the maximum computing capacity of the unmanned system based on the floating-point computing capacity and computing complexity of the unmanned system;

[0062] Step S3, based on the scene space range obtained in step S1 and the maximum number of grids of the unmanned system, calculate the minimum side length constrained by the system memory. This value is the minimum grid side length allowed to avoid exceeding the maximum number of grids of the unmanned system.

[0063] Step S4: Based on the scene space range and the maximum computing time of the unmanned system obtained in step S1, and the maximum computing power of the unmanned system calculated in step S2, the minimum side length of the system computing power constraint is calculated. This value is the minimum grid side length allowed for path planning by the unmanned system within the maximum computing time and the maximum computing power.

[0064] Step S5: Calculate the minimum side length of the task accuracy constraint based on the minimum feature scale of the scene, the safety distance of the unmanned system, the size of the unmanned system, and the path planning precision factor obtained in step S1. This value is the minimum grid side length that meets the requirements of scene feature recognition and safe obstacle avoidance in the path planning task.

[0065] Step S6, determining the side length of the minimum grid unit based on the minimum side length constrained by the system memory calculated in step S3, the minimum side length constrained by the system computing power calculated in step S4, and the minimum side length constrained by the task accuracy calculated in step S5.

[0066] In some embodiments, in step S2, the maximum computing capacity of the unmanned system is calculated as follows:

[0067]

[0068] Where, Maximum computing power for unmanned systems; Floating point computing capability for unmanned systems, , The CPU main frequency of the unmanned system, is the number of CPU cores, It is the number of CPU single-cycle parallel floating-point operations; is the algorithm efficiency coefficient, which indicates the utilization rate of hardware resources in algorithm execution and can be determined through testing or estimation; is the time complexity of the algorithm, and the total number of grids The relationship is 、 or .

[0069] In a preferred embodiment of the present invention, in step S3, the method for calculating the minimum side length of the system memory constraint is:

[0070]

[0071] Where, is the minimum side length constrained by system memory, is the maximum number of grids in the unmanned system, 、 、 They are the maximum extents of the scene in the x, y, and z directions respectively.

[0072] Optionally, in step S4, the method for calculating the minimum side length constrained by the system computing power is:

[0073]

[0074] Where, The minimum side length constrained by the system computing power, The maximum computing time of the unmanned system.

[0075] In a specific embodiment, in step S5, the method for calculating the minimum side length constrained by the task accuracy is:

[0076]

[0077] Where, is the minimum characteristic scale of the scene, Safe distance for unmanned systems, For unmanned system size, is the path planning precision factor.

[0078] In particular, in step S6, the method for calculating the side length of the minimum grid unit is:

[0079]

[0080] Where, is the side length of the smallest grid unit.

[0081] As described above, the grid minimum unit calculation method for unmanned system three-dimensional map path planning of the present invention establishes a quantitative mathematical model, comprehensively considers system memory constraints (maximum number of grids), system computing power constraints (computing power and maximum computing time) and task accuracy constraints (minimum feature scale, safety distance, body size and precision factor), and accurately calculates the side length of the grid minimum unit, overcoming the blindness of traditional empirical selection methods and achieving a dynamic balance between accuracy and computing performance while ensuring scene feature recognition accuracy and obstacle avoidance safety.

[0082] Reference Figure 2 The unmanned system path planning method provided by the present invention comprises the following steps:

[0083] Step (a), using the above method to calculate the minimum grid unit side length;

[0084] Step (b), dividing the three-dimensional space into a cubic grid using the calculated value in step (a) as the side length to generate an initial grid map;

[0085] Step (c), executing a path search algorithm based on the generated initial grid map and outputting a planned path;

[0086] Step (d), monitoring path search calculation time and compare it with the maximum computing time of the unmanned system Compare, if , then the path planning precision factor is triggered The dynamic attenuation adjustment is performed and the step (a) is returned to recalculate the minimum grid unit side length, and the grid map is reconstructed with the updated minimum grid unit side length:

[0087]

[0088] Where, The precision factor of the adjusted trigger path planning; is the time decay factor, , with a typical value of 0.5~0.9. This operation makes the calculation formula for the minimum side length of the task accuracy constraint as follows:

[0089]

[0090] The denominator in the formula decreases, resulting in Increase, grid cell size expands, total grid reduce.

[0091] The unmanned system path planning method of the present invention uses the calculation of the minimum grid unit as a prerequisite, generates a grid map based on the calculated optimal side length, and performs path search. This ensures the execution efficiency of the path search algorithm by accurately setting the initial grid size, ensuring that planning is completed within the maximum allowable time. At the same time, when a calculation timeout is detected, the attenuation update mechanism of the precision factor is automatically triggered, the grid size is dynamically increased, and re-planning is carried out, which can effectively cope with various complex scenarios.

[0092] The grid minimum unit calculation system for three-dimensional map path planning of an unmanned system provided by the present invention includes a parameter acquisition unit, a calculation processing unit and a decision unit.

[0093] The parameter acquisition unit is used to obtain scene parameters for 3D map path planning, including: scene minimum feature scale, unmanned system safety distance, unmanned system size, scene spatial range, unmanned system maximum grid number, unmanned system maximum calculation time, and path planning precision factor;

[0094] The computing processing unit is used to perform the following: calculating the maximum computing capacity of the unmanned system based on the floating-point computing capacity and computing complexity of the unmanned system; calculating the minimum side length of the system memory constraint based on the acquired scene space range and the maximum number of grids of the unmanned system, which is the minimum grid side length allowed to avoid exceeding the maximum number of grids of the unmanned system; calculating the minimum side length of the system computing power constraint based on the acquired scene space range and the maximum computing time of the unmanned system, and the calculated maximum computing capacity of the unmanned system, which is the minimum grid side length allowed for path planning within the maximum computing time under the maximum computing capacity of the unmanned system; calculating the minimum side length of the task accuracy constraint based on the acquired minimum feature scale of the scene, the safety distance of the unmanned system, the size of the unmanned system, and the path planning precision factor, which is the minimum grid side length that meets the scene feature recognition and safe obstacle avoidance requirements in the path planning task;

[0095] The decision unit is used to determine the side length of the minimum grid unit based on the calculated minimum side length constrained by system memory, minimum side length constrained by system computing power, and minimum side length constrained by task accuracy.

[0096] Therefore, the grid minimum unit calculation system for unmanned system three-dimensional map path planning of the present invention obtains environmental and task parameters in real time through the parameter acquisition unit, the calculation processing unit solves the memory-constrained side length, computing power-constrained side length and accuracy-constrained side length in parallel, and the decision unit outputs the optimal grid side length. Through hardware collaborative calculation, a scientific balance between resources and accuracy is achieved, memory usage is reduced and response time is shortened, which significantly improves the efficiency and reliability of the front-end processing of the unmanned system path planning.

[0097] The unmanned system provided by the present invention is equipped with the aforementioned grid minimum unit calculation system for unmanned system three-dimensional map path planning. Equipped with this grid minimum unit calculation system, the unmanned system can ensure path quality and safety by optimizing the grid size. Furthermore, the computing system's efficient coordination with hardware resources significantly extends the mobile platform's battery life, improving overall mission execution efficiency and reliability.

[0098] The present invention will be further described below with reference to examples.

[0099] Example 1

[0100] A certain unmanned swarm task requires path planning in a complex three-dimensional environment. The scene space range is 100×100×50 meters (L x =100, L y =100, L z =50). Other scene-related parameters are: scene minimum feature scale =0.5m (minimum obstacle size), unmanned system safety distance = 1.0 m (minimum distance between drone and obstacles), size of unmanned system =1.5 meters (wingspan of fixed-wing drone or wheelbase of quadrotor drone), maximum number of grids in the system =10 12 (Computing resource limitations), maximum calculation time for path planning =10 seconds, path planning precision factor =0.3 (accuracy requirement is medium). Related system hardware parameters are: CPU main frequency of unmanned system =3.0 GHz, number of cores =8, single-cycle parallel floating-point computing capability =4, Algorithm efficiency coefficient =0.7 (hardware resource utilization), the algorithm time complexity is linear .

[0101] The optimal grid side length is calculated using the grid minimum unit calculation method for unmanned system three-dimensional map path planning proposed in the present invention as follows:

[0102] (1) Calculate the maximum computing capacity of the unmanned system:

[0103] Floating-point computing capability

[0104] Algorithm time complexity

[0105] Get the maximum computing power of the system

[0106] (2) Calculate the minimum side length of the system memory constraint:

[0107]

[0108] (3) Calculate the minimum side length constrained by the computing power of the system:

[0109]

[0110] (4) Calculate the minimum side length constrained by task accuracy:

[0111]

[0112] (5) Calculate the side length of the smallest grid unit:

[0113]

[0114] Therefore, the calculated minimum grid unit side length is 0.3846 meters. Considering operability, the value can be taken as 0.38 meters.

[0115] Example 2

[0116] Based on Example 1, the path planning time after generating the grid map is more than 0.38 meters based on the initial calculated grid side length. =1 second later, dynamic feedback adjustment is triggered. Updated according to the iterative mechanism of precision reduction factor And recalculate the grid edge length:

[0117] (1) Calculation of path planning precision factor update:

[0118] Set the time decay factor ,but

[0119] (2) Recalculate the grid side length:

[0120] Task accuracy constraint minimum edge length update:

[0121]

[0122] The final path planning time is less than Therefore, the updated calculated minimum grid unit side length is 0.4348 meters. Considering operability, the value can be taken as 0.43 meters.

[0123] The comparison results of the method proposed by the present invention and the existing fixed grid method are shown in the following table:

[0124]

[0125] In this example, the path planning results of the method proposed by the present invention and the existing fixed large grid method are as follows: Figure 3 As shown, from Figure 3 As can be seen from the table: although the traditional fixed small grid method can accurately depict scene features, the excessive number of grids causes the calculation time to far exceed the maximum allowable time of the system, and ultimately planning fails, reflecting the defect of "high precision but computational overload"; although the traditional fixed large grid method has a short calculation time, due to the large grid size, scene details are lost, resulting in a long planning path, reflecting the problem of "efficient calculation but insufficient precision". In the method proposed in the present invention, although the initial calculation timeout occurs due to the high precision requirement, the grid side length is optimized to 0.43m through a dynamic adjustment mechanism, and the final calculation time is controlled at 0.906s (less than the upper limit of 1s). The path length is also shorter than that of the fixed large grid method, achieving a dynamic balance between precision and computing performance, verifying the technical effect of the present invention in resolving the "contradiction between precision and efficiency" through multi-constrained quantitative calculation of system memory, computing power, and task precision.

[0126] Finally, it should be noted that the features mentioned and / or illustrated in the above description of the exemplary embodiments of the present invention may be incorporated into one or more other embodiments in the same or similar manner, combined with features in other embodiments, or substituted for corresponding features in other implementations. The technical solutions obtained by such combination or substitution shall also be deemed to be included in the scope of protection of the present invention.

Claims

1. A method for calculating the minimum grid unit for three-dimensional map path planning of unmanned systems, characterized in that: The following steps are involved: Step 1: Obtain scene parameters for 3D map path planning, including: scene minimum feature scale, unmanned system safety distance, unmanned system size, scene spatial range, unmanned system maximum grid number, unmanned system maximum calculation time, and path planning precision factor; Step 2: Calculate the maximum computing capacity of the unmanned system based on the floating-point computing capacity and computational complexity of the unmanned system; Step 3: Based on the scene space range obtained in step 1 and the maximum number of grids in the unmanned system, calculate the minimum side length constrained by the system memory. This value is the minimum grid side length allowed to avoid exceeding the maximum number of grids in the unmanned system. Step 4: Based on the scene space range and the maximum computing time of the unmanned system obtained in step 1, as well as the maximum computing power of the unmanned system calculated in step 2, the minimum side length of the system computing power constraint is calculated. This value is the minimum grid side length allowed for path planning by the unmanned system within the maximum computing time and the maximum computing power. Step 5: Based on the scene minimum feature scale, unmanned system safety distance, unmanned system size, and path planning precision factor obtained in step 1, calculate the minimum side length of the task accuracy constraint. This value is the minimum grid side length that meets the scene feature recognition and safe obstacle avoidance requirements in the path planning task. Step 6: Determine the side length of the minimum grid unit based on the minimum side length constrained by the system memory calculated in step 3, the minimum side length constrained by the system computing power calculated in step 4, and the minimum side length constrained by the task accuracy calculated in step 5.

2. The method for calculating the minimum grid unit for three-dimensional map path planning of an unmanned system according to claim 1, characterized in that: In step 2, the formula for calculating the maximum computing capacity of the unmanned system is: Where, The maximum computing power for unmanned systems, Floating point computing capability for unmanned systems, , The CPU main frequency of the unmanned system, is the number of CPU cores, The number of CPU single-cycle parallel floating-point operations, is the algorithm efficiency coefficient, is the time complexity of the algorithm.

3. The method for calculating the minimum grid unit for three-dimensional map path planning of an unmanned system according to claim 2, characterized in that: In step 3, the calculation method for the minimum side length of the system memory constraint is: Where, is the minimum side length constrained by system memory, is the maximum number of grids in the unmanned system, 、 、 They are the maximum extents of the scene in the x, y, and z directions respectively.

4. The method for calculating the minimum grid unit for three-dimensional map path planning of an unmanned system according to claim 3, characterized in that: In step 4, the calculation method for the minimum side length constrained by system computing power is: Where, The minimum side length constrained by the system computing power, The maximum computing time of the unmanned system.

5. The method for calculating the minimum grid unit for three-dimensional map path planning of an unmanned system according to claim 4, characterized in that: In step 5, the calculation method for the minimum side length of the task accuracy constraint is: Where, To constrain the minimum side length for task accuracy, is the minimum characteristic scale of the scene, Safe distance for unmanned systems, For unmanned system size, is the path planning precision factor.

6. The method for calculating the minimum grid unit for three-dimensional map path planning of an unmanned system according to claim 5, characterized in that: In step 6, the side length of the smallest grid unit is calculated as follows: Where, is the side length of the smallest grid unit.

7. A path planning method for an unmanned system, characterized in that: The following steps are involved: Step (a), calculating the minimum unit side length of the grid using the method described in any one of claims 1 to 6; Step (b), dividing the three-dimensional space into a cubic grid using the calculated value in step (a) as the side length to generate an initial grid map; Step (c), executing a path search algorithm based on the generated initial grid map and outputting a planned path; Step (d), monitoring path search calculation time and compare it with the maximum computing time of the unmanned system Compare, if , then the path planning precision factor is triggered The dynamic attenuation adjustment is performed and the step (a) is returned to recalculate the minimum grid unit side length, and the grid map is reconstructed with the updated minimum grid unit side length: Where, is the adjusted path planning precision factor, is the time decay factor.

8. The unmanned system path planning method according to claim 7, characterized in that: Time decay factor The value is 0.5~0.

9.

9. A grid minimum unit calculation system for three-dimensional map path planning of unmanned systems, characterized by: It includes parameter acquisition unit, calculation processing unit and decision making unit; The parameter acquisition unit is used to obtain scene parameters for three-dimensional map path planning, including: minimum feature scale of the scene, safety distance of the unmanned system, size of the unmanned system, spatial range of the scene, maximum number of grids of the unmanned system, maximum calculation time of the unmanned system, and precision factor of the path planning; The computing processing unit is used to perform the following: calculating the maximum computing capacity of the unmanned system based on the floating-point computing capacity and computing complexity of the unmanned system; calculating the minimum side length of the system memory constraint based on the acquired scene space range and the maximum number of grids of the unmanned system, which value is the minimum grid side length allowed to avoid exceeding the maximum number of grids of the unmanned system; calculating the minimum side length of the system computing power constraint based on the acquired scene space range and the maximum computing time of the unmanned system, and the calculated maximum computing capacity of the unmanned system, which value is the minimum grid side length allowed for path planning within the maximum computing time under the maximum computing capacity of the unmanned system; calculating the minimum side length of the task accuracy constraint based on the acquired minimum feature scale of the scene, the safety distance of the unmanned system, the size of the unmanned system, and the path planning precision factor, which value is the minimum grid side length that meets the scene feature recognition and safe obstacle avoidance requirements in the path planning task; The decision unit is used to determine the side length of the minimum grid unit based on the calculated minimum side length constrained by system memory, the minimum side length constrained by system computing power, and the minimum side length constrained by task accuracy.

10. An unmanned system, characterized in that: The utility model is provided with the grid minimum unit computing system as claimed in claim 9.

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