Multi-unmanned aerial vehicle cluster path planning method based on lightweight A star algorithm

Abstract

The invention discloses a multi-unmanned aerial vehicle cluster path planning method based on a lightweight A star algorithm, and belongs to the technical field of unmanned aerial vehicle cluster path planning, and the method comprises the steps: carrying out the scanning work of a task environment through an investigation group unmanned aerial vehicle, and constructing a three-dimensional grid map; preliminarily planning a flight path by using a lightweight A star algorithm and calculating a flight distance matrix; under the condition of ensuring that task group unmanned aerial vehicles arrive at all target points, taking the minimum total flight path of the unmanned aerial vehicle cluster as a target, and searching an optimal flight path planning scheme; aiming at the optimal flight path planning scheme, optimizing the flight path of each task group unmanned aerial vehicle by using an improved particle swarm algorithm; and distributing the optimized flight path to the unmanned aerial vehicle of the corresponding task group, and controlling the unmanned aerial vehicle to complete a subsequent task. The method is high in operation speed, good in performance, capable of achieving multi-unmanned-aerial-vehicle cluster path planning in a real environment and high in stability.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-UAV cluster path planning, and in particular to a multi-UAV cluster path planning method based on a lightweight A-star algorithm. Background Art

[0002] Multi-drone swarms are widely used in a variety of fields, such as urban aerial photography, post-disaster emergency rescue, forest fire monitoring and prevention, etc. Due to the high maneuverability of drones and the synergy of multiple drones, multi-drone swarms can often complete tasks efficiently and quickly under special circumstances, and can avoid major risks and ensure human safety. The coordinated planning of multiple drones is a prerequisite for the efficient operation of drone swarms. Reasonable completion of path planning and task allocation can often enhance the efficiency and execution of drone swarms.

[0003] In recent years, with the popularization and application of cutting-edge technologies such as artificial intelligence and autonomous systems and the rapid development of drone systems, the development of drone swarm systems has become a major trend. Their significant execution capabilities, low costs, and little impact from terrain constraints have made their role increasingly prominent in special circumstances.

[0004] The existing multi-UAV swarm path planning methods are highly dependent on the environment. A single obstacle model cannot meet the needs of path planning in real situations. It is necessary to establish a specific obstacle model based on the actual situation so that the multi-UAV swarm can accurately avoid obstacles. The existing multi-UAV swarm path planning methods are prone to falling into local optimality and premature maturity, and cannot effectively solve practical problems with multiple constraints. The extreme value solved is not the minimum point. Summary of the invention

[0005] The present invention provides a multi-UAV cluster path planning method based on a lightweight A-star algorithm to solve the technical problems that the existing multi-UAV cluster path planning method is prone to fall into local optimality and premature maturity, cannot effectively solve practical problems with multiple constraints, and the extreme value solved is not the minimum point.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] On the one hand, the present invention provides a multi-UAV cluster path planning method based on a lightweight A-star algorithm, wherein the UAVs in the UAV cluster are divided into a reconnaissance group UAV and a task group UAV, and the multi-UAV cluster path planning method based on the lightweight A-star algorithm includes:

[0008] The reconnaissance drones in the drone cluster are used to scan the mission environment, obtain obstacle model information and location information, mark target point information, and build a three-dimensional grid map;

[0009] Based on the constructed three-dimensional grid map, the current position information of each task group drone in the drone cluster is used as the starting point, and each target point is used as the end point. The lightweight A-star algorithm is used to preliminarily plan the flight path of each task group drone, and the flight distance matrix is ​​calculated; wherein each row of the flight distance matrix represents a task group drone, and each column of the flight distance matrix represents a target point;

[0010] Based on the flight distance matrix, the total flight paths of the drone cluster corresponding to different flight path planning schemes are calculated respectively, and the optimal flight path planning scheme is searched for with the goal of minimizing the total flight path of the drone cluster while ensuring that all target points are reached by the task group drones;

[0011] For the best flight path planning scheme, the improved particle swarm algorithm is used to optimize the flight path of each task group UAV, find the minimum value of the flight path distance and optimize the flight path smoothness;

[0012] Assign the optimized flight path to the corresponding task group drone, control it to fly over the corresponding target point, and complete subsequent tasks.

[0013] Furthermore, in the process of using the lightweight A-star algorithm to preliminarily plan the flight path of each task group drone, the lightweight A-star algorithm only considers points within a unit length of the current node in six directions: above, below, left, right, in front, and behind.

[0014] Furthermore, the lightweight A-star algorithm uses Euclidean distance to calculate the distance from the current node to the neighboring node.

[0015] Furthermore, the lightweight A-star algorithm uses Manhattan distance to calculate the distance from the neighbor node to the end point.

[0016] Furthermore, the flight path of each UAV in the task group is optimized using an improved particle swarm algorithm for the best flight path planning scheme, including:

[0017] The improved particle swarm algorithm initializes the particle swarm position according to the flight path of each task group UAV in the optimal flight path planning scheme, and gives it an initialization speed;

[0018] Calculate the fitness value of each particle according to the fitness formula;

[0019] Update the individual extreme value and global extreme value of particles according to the fitness value;

[0020] Update the particle position and velocity according to the improved particle position and velocity update formula;

[0021] Calculate the fitness of the particle at the new position and update the individual extreme value and global extreme value of the particle;

[0022] Determine whether the end condition is met. If so, the algorithm ends. If not, continue to update the particle position and velocity, and cyclically search for the minimum value of the flight path distance until the end condition is met.

[0023] Furthermore, the improved particle position and velocity update formula refers to improving the inertia weight in the particle position and velocity update formula in the particle swarm algorithm so as to achieve adaptive dynamic adjustment.

[0024] Furthermore, the inertia weight in the particle position and velocity update formula in the particle swarm algorithm is improved to achieve adaptive dynamic adjustment, including:

[0025] A logarithmic function is used to control the change of inertia weight, and a random adjustment number obeying Gaussian distribution is added to implement dynamic adjustment of inertia weight during the evolution process.

[0026] Furthermore, the inertia weight ω d The expression is:

[0027]

[0028] Where t is the current iteration number; T is the maximum iteration number; α is the inertia adjustment factor; randn is a random number that satisfies the Gaussian distribution; ω min Indicates the minimum value of the inertia weight without Gaussian random number perturbation, which is used to limit the lower bound of the inertia weight change; ω max It represents the maximum value of the inertia weight without Gaussian random number disturbance, which is used to limit the upper limit of the inertia weight change.

[0029] 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.

[0030] In yet another aspect, the present invention further provides a computer-readable storage medium, wherein at least one instruction is stored in the storage medium, and the instruction is loaded and executed by a processor to implement the above method.

[0031] The beneficial effects brought about by the technical solution provided by the present invention include at least:

[0032] Firstly, the present invention can quickly and efficiently complete the collaborative flight mission of multiple UAV clusters, and has strong stability. The UAV cluster can traverse all target points, find the flight path and allocation method with the least group resource consumption, avoid all obstacles, have good path smoothness, and low spatial complexity. Secondly, the present invention can well solve the UAV flight mission in a set environment, reduce human participation, and improve the reliability and safety of special tasks. Finally, the present invention reduces the time consumption of the algorithm, improves the reliability of the algorithm, and reduces the probability of the algorithm falling into the local optimum by using an improved particle swarm algorithm, and can plan a better path. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] 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.

[0034] Figure 1 It is a schematic diagram of the execution flow of a multi-UAV cluster path planning method based on a lightweight A-star algorithm provided by an embodiment of the present invention;

[0035] Figure 2 is a flow chart of the A-star algorithm provided by an embodiment of the present invention;

[0036] Figure 3 is a flow chart of a particle swarm algorithm provided by an embodiment of the present invention;

[0037] Figure 4 It is a system block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] 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.

[0039] First of all, it should be noted that in the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "exemplarily" is intended to present the concept in a concrete way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0040] First embodiment

[0041] This embodiment provides a multi-UAV cluster path planning method based on a lightweight A-star algorithm, wherein the UAV cluster is divided into a reconnaissance group UAV and a task group UAV, the former is responsible for reconnaissance of the mission environment, determination of obstacle information and location, and search for the target point location; the latter is responsible for flying over the target point and performing the corresponding task; this method can more accurately plan the flight path by modeling obstacles in the real environment; and this method adds an improved particle swarm algorithm to optimize the flight path, and uses variable inertia weights to optimize the convergence process of the particle swarm algorithm, so that it can converge quickly in the first half, and improve the convergence accuracy in the second half, and find the optimal task allocation method. This method can be implemented by an electronic device, which can be a terminal or a server. The execution flow of this method is as follows: Figure 1 As shown, the following steps are included:

[0042] Step 1: Map initialization.

[0043] The reconnaissance group drones in the drone cluster scan the mission environment, obtain obstacle model information and location information, mark target point information, and establish a three-dimensional model based on the actual obstacle shape and size. At the same time, a three-dimensional grid map is constructed to facilitate subsequent distance calculation and path planning.

[0044] Step 2: Preliminary route planning.

[0045] Taking the current GPS position information of each task group UAV as the starting point and each target point as the end point, the lightweight A-star algorithm is used to preliminarily plan the flight path and calculate the flight distance matrix.

[0046] Among them, it should be noted that the lightweight A-star algorithm is an improvement on the existing A-star algorithm. The improvement is as follows: when searching for feasible points in the neighborhood, only the points within a unit length in six directions: above, below, left, right, in front, and behind are considered, and the Manhattan distance calculation method is used to calculate the distance.

[0047] Specifically, Figure 2 As shown in the figure, the steps to preliminarily plan the flight path using the lightweight A-star algorithm are:

[0048] (a) Initialization of the algorithm model: use the current position of the task group drone to initialize the drone flight starting point, and use the target point position to initialize the drone flight end point; initialize the open list, put only the drone flight starting point in it, for storing the nodes to be expanded; initialize the closed list, set it to an empty list, for storing the expanded nodes; define the cost function f(n), which represents the total distance of the flight path.

[0049] f(n)=g(n)+h(n) (1)

[0050] Among them, g(n) represents the distance from the starting point to the current node; h(n) represents the distance from the current node to the end point.

[0051] (b) Select the node with the smallest f(n) value from the open list as the current node n. First, determine the end condition to determine whether this node is the end point. If it is the end point, the path is found and the loop is exited. If it is not the end point, continue the loop and search for neighbor nodes of the current node in six directions: up, down, left, right, front, and back as expansion nodes. For each expansion node, first determine whether the node is located in the obstacle area or in the closed list. If one of the conditions is met, skip this neighbor node. If neither condition is met, calculate the g(n), h(n) and total distance f(n) values ​​of the neighbor node and update them to the open list.

[0052] g n+1 =g n +Cost(n,n+1) (2)

[0053]

[0054] h n+1 =|x n+1 -x end |+|y n+1 -y end |+|z n+1 -z end | (4)

[0055] Where n represents the current node; n+1 represents the neighbor node. The current node is the father node of the neighbor node. As shown in formula (2), the father node can be used to simplify the calculation when calculating the distance from the starting point; Cost represents the distance from the current node to the neighbor node, which is calculated using the Euclidean distance; h(n+1) is calculated using the Manhattan distance.

[0056] After processing each neighbor node, move the current node to the closed list, then start the next loop and find the next current node to perform this operation.

[0057] (c) If the end condition is met, that is, the end point of the drone flight is found, the end point is used as the starting point, and the points are backtracked through its parent node. These points are saved in a list until the starting point, and the flight path is finally obtained, and the total distance of the flight path points is recorded.

[0058] (d) Reinitialize the algorithm model, take the current position of the mission drone as the starting point and the positions of other target points as the end points, and replan the flight path until all target points are traversed.

[0059] (e) Reinitialize the algorithm model and repeat the above steps for the remaining task group drones until all task group drones are traversed.

[0060] Step 3: Task allocation.

[0061] Under the premise of ensuring that all target points are reached by the task group drones, the optimal task allocation method in the flight path matrix is ​​searched with the goal of minimizing the total path of the drone cluster.

[0062] It should be noted that, assuming that the number of mission drones is m, and the number of target points is n, and m>n. Then the distance from the mission drone to each point can be written as an m*n matrix A, where the mth row in the matrix represents the mth drone, and the nth column represents the nth target point. According to the total path calculation formula of the drone cluster, the flight path distance in each case can be calculated and the minimum flight path distance can be found. Based on this, in order to ensure that all target points have a mission group drone to arrive to perform the task, select an element from each column of the total distance matrix F to calculate the total distance of the drone cluster, and select the smallest total distance value. The allocation method is recorded as the task allocation situation in the subsequent steps. The calculation formula is as follows.

[0063]

[0064] Among them, i(j) is the index of the drone assigned to target point j (to ensure that different target points are paired with different drones); only one element A is selected for each column (i.e., each target point) i(j),j to calculate the distance.

[0065] Step 4: Path optimization.

[0066] According to the optimal task allocation method determined in step three, the flight path of each task group drone is optimized using the improved particle swarm algorithm to find the minimum value of its flight path distance and optimize the smoothness of the flight path.

[0067] Among them, it should be noted that the improved particle swarm algorithm initializes the particle swarm position according to the preliminary planned flight path and assigns it an initialization speed; calculates the fitness of each particle according to the fitness formula; updates the individual extreme value and global extreme value of the particle according to the fitness value; updates the position and velocity of the particle according to the improved particle position and velocity update formula; calculates the fitness of the particle at the new position and updates the individual extreme value and global extreme value of the particle; determines whether the calculation accuracy is met or the maximum number of iterations is reached. If so, the algorithm ends. If not, the particle position and velocity continue to be updated, and the minimum value of the flight path distance is cyclically searched until the end condition is met. Specifically, the execution steps are as follows: Figure 3 As shown, including:

[0068] (a) Set the number of iterations nMax and the number of particles m, and initialize the population position according to the initial flight path of the task group drone, and randomly initialize the population speed. Generate enough particle number sample points to provide enough sample solution space for algorithm iteration. At the same time, determine the upper and lower boundaries of the particles for algorithm iteration, and limit all particles in the particle swarm to be within the specified upper and lower bounds.

[0069] (b) Calculate the fitness value F(x i ), the fitness value is used to calculate the degree of feasible solutions of all individuals, providing a priori calculation basis for the iteration of subsequent steps. The size of the fitness value is constructed according to the actual problem, converting the abstract problem into a computable mathematical model, and using the size of the fitness value to indicate the quality of the feasible solution.

[0070] (c) According to the fitness value F(x i ) Update the individual optimal solution p best and the group optimal solution g best The core idea of ​​the particle swarm algorithm is to use the individual optimal solution and the group optimal solution to continuously iterate and update, so that the feasible solution of the algorithm is close to the optimal solution in the actual situation. By simulating the sociological properties of the particle swarm, that is, the individual bird, the optimal solution is continuously replaced and iterated to seek benefits and avoid harm.

[0071] F=Cost o +cost l +cost a +cost h +cost maxh +cost minh +cost v +cost hor (6)

[0072] Among them, Cost o Indicates the risk of whether the path passes through obstacles; Cost l Indicates the total length of the path. Usually, the shorter the path, the lower the cost. a Indicates the smoothness of the path. The larger the turning angle of the path, the higher the cost. h Cost represents the change in path height, which is related to the energy consumption of the drone climbing; maxh Used to limit the maximum height of the path; Cost minh Used to limit the minimum height of the path; Cost v Used to control the vertical change of the path to avoid additional energy consumption caused by frequent up and down flights; Cost hor Indicates the optimization requirements of the path in the horizontal direction, such as avoiding twists and turns.

[0073] (d) Update the particle's velocity and position according to formulas (6), (7), and (8), and detect and limit all particle positions at the same time. For particle attributes that exceed the boundary, the attributes of that dimension are uniformly treated as boundaries.

[0074]

[0075]

[0076] In this case, let the search space be D-dimensional space, the number of particles be n, and the position of the i-th particle be The speed change rate of the ith example is expressed as Indicates that each particle has two properties: position and speed. The best position (individual best position) searched by the i-th particle so far is Denoted as p best , the best position (global best position) searched by the entire population so far is Denoted as g best ; ω is called the inertia factor; the position and velocity range of the dth dimension is and If in a certain dimension the iteration and If it exceeds the boundary value, it is considered to be a boundary value; c1 and c2 are learning factors, which are generally random numbers between (0,2); rand() is a random number between (0,1).

[0077] The convergence speed of the particle swarm algorithm depends on ω. When ω is large, the algorithm has a strong global search capability and is suitable for global search; when ω is small, the algorithm has a high search accuracy and is more suitable for local search. Therefore, a logarithmic function is used to control the change of the inertia weight ω, and a random adjustment number that obeys the Gaussian distribution is added to implement dynamic adjustment of the inertia weight during the evolution process, enhance the global search capability of the algorithm in the later stage, and reduce the possibility of falling into the local optimum. The improved inertia weight expression is:

[0078]

[0079] Among them, t is the current iteration number; T is the maximum iteration number; α is the inertia adjustment factor, and its value range is 0~(ω max -ω min ) / 2, the smaller its value is, the smaller the influence of the random number of Gaussian distribution on the dynamic change of inertia weight is, and the larger its value is, the greater the influence of the random number of Gaussian distribution on the dynamic change of inertia weight is; randn is a random number that satisfies Gaussian distribution; ω min is the minimum value of the inertia weight without Gaussian random number disturbance, which is used to limit the lower bound of the inertia weight change; ω maxIt is the maximum value of the inertia weight without Gaussian random number disturbance, which is used to limit the upper limit of the inertia weight change.

[0080] (e) Calculate the fitness of all particles and update the individual optimal solution and the global optimal solution.

[0081] (f) If the calculation accuracy or number of iterations is met, the algorithm ends; otherwise, jump to step 3 and continue searching.

[0082] (g) Perform particle swarm optimization on the other task group drones in the best task allocation method according to the above steps until all task group drones in the task allocation method are optimized.

[0083] Step 5: Task execution.

[0084] The final flight path is assigned to the task group drones that need to perform the mission, and they are controlled to fly over the target point and complete subsequent tasks.

[0085] In summary, this embodiment provides a multi-UAV cluster path planning method based on the lightweight A-star algorithm. First, the method can quickly and efficiently complete the multi-UAV cluster collaborative flight mission, and has strong stability. The UAV cluster can traverse all target points, find the flight path and allocation method with the least group resource consumption, avoid all obstacles, have good path smoothness, and low spatial complexity; secondly, the method can well solve the UAV flight mission in the set environment, reduce personnel participation, and improve the reliability and safety of special tasks; finally, the method reduces the time consumption of the algorithm, improves the reliability of the algorithm, and reduces the probability of the algorithm falling into the local optimum by using the improved particle swarm algorithm, and can plan a better path.

[0086] Second embodiment

[0087] This embodiment provides an electronic device, such as Figure 4 As shown, the electronic device includes: a processor and a memory; wherein the processor and the memory can be connected via a communication bus; 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. In addition, the electronic device may also include a transceiver, the processor and the transceiver can be connected via a communication bus, and the transceiver is used to communicate with other devices.

[0088] Next, combine Figure 4 The following is a detailed introduction to the various components of the electronic device:

[0089] Among them, the processor is the control center of the electronic device, and the electronic device may include multiple processors, each of which may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may be a processor or a general term for multiple processing elements. For example, the processor is one or more central processing units (CPUs), or other general-purpose processors, application specific integrated circuits (ASICs), or one or more integrated circuits configured to implement an embodiment of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (field programmable gate arrays, FPGAs), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor may execute various functions of the electronic device by running or executing software programs stored in the memory and calling data stored in the memory.

[0090] In a specific implementation, as an embodiment, the processor may include one or more CPUs, such as Figure 4 The CPU0 and CPU1 shown in the figure are, of course, only exemplary.

[0091] The memory is used to store the software program for executing the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0092] Optionally, the memory may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may be integrated with the processor or exist independently and accessed through the interface circuit ( Figure 4 (not shown) is coupled to the processor, which is not specifically limited in this embodiment of the present invention.

[0093] The transceiver may include a receiver and a transmitter ( Figure 4 The receiver is used to implement the receiving function, and the transmitter is used to implement the sending function. The transceiver can be integrated with the processor or exist independently and communicate with the electronic device through the interface circuit ( Figure 4 (not shown) is coupled to the processor, which is not specifically limited in this embodiment of the present invention.

[0094] In addition, it should be noted that Figure 4 The structure of the electronic device shown in the figure does not constitute a limitation on the device, and the actual device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. In addition, the technical effects achieved by the electronic device when executing the method of the first embodiment above can refer to the technical effects described in the first embodiment above, so they are not repeated here.

[0095] Third embodiment

[0096] 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.

[0097] 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 embodiment of the present invention can be in the form of a full or partial hardware embodiment, a full or partial software embodiment or an embodiment combining software and hardware. Moreover, when implemented using software, the embodiment of the present invention can be in the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program codes. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center containing one or more available media sets. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD), or a semiconductor medium. The semiconductor medium may be a solid state hard disk.

[0098] 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.

[0099] 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 1These 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.

[0100] It should also be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements. In addition, the term "and / or" is only an association relationship describing the associated objects, indicating that there can be three relationships, for example, A and / or B, which can represent: A exists alone, A and B exist at the same time, and B exists alone, wherein A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding. "At least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can be represented by: a, b, c, ab, ac, bc or abc, where a, b, c can be single or plural.

[0101] In addition, it can be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0102] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0103] In several embodiments provided by the present invention, it should be understood that the disclosed equipment, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of functional modules / units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0104] If the method is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0105] Finally, it should be noted that the above is only 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 ordinary technicians in this technical field, 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. 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 multi-UAV cluster path planning method based on lightweight A-star algorithm, where: The drones in the drone cluster are divided into a reconnaissance group drone and a task group drone, and the multi-drone cluster path planning method based on the lightweight A-star algorithm includes: The reconnaissance drones in the drone cluster are used to scan the mission environment, obtain obstacle model information and location information, mark target point information, and build a three-dimensional grid map; Based on the constructed three-dimensional grid map, the current position information of each task group drone in the drone cluster is used as the starting point, and each target point is used as the end point. The lightweight A-star algorithm is used to preliminarily plan the flight path of each task group drone, and the flight distance matrix is ​​calculated; wherein each row of the flight distance matrix represents a task group drone, and each column of the flight distance matrix represents a target point; Based on the flight distance matrix, the total flight paths of the drone cluster corresponding to different flight path planning schemes are calculated respectively, and the optimal flight path planning scheme is searched for with the goal of minimizing the total flight path of the drone cluster while ensuring that all target points are reached by the task group drones; For the best flight path planning scheme, the improved particle swarm algorithm is used to optimize the flight path of each task group UAV, find the minimum value of the flight path distance and optimize the flight path smoothness; Assign the optimized flight path to the corresponding task group drone, control it to fly over the corresponding target point, and complete subsequent tasks.

2. The multi-UAV cluster path planning method based on lightweight A-star algorithm as claimed in claim 1 is characterized in that: In the process of using the lightweight A-star algorithm to preliminarily plan the flight path of each task group drone, the lightweight A-star algorithm only considers points within a unit length of the current node in six directions: above, below, left, right, in front, and behind.

3. The multi-UAV cluster path planning method based on lightweight A-star algorithm as claimed in claim 2 is characterized in that: The lightweight A-star algorithm uses the Euclidean distance to calculate the distance from the current node to the neighboring node.

4. The multi-UAV cluster path planning method based on lightweight A-star algorithm as claimed in claim 3 is characterized in that: The lightweight A-star algorithm uses Manhattan distance to calculate the distance from the neighbor node to the end point.

5. The multi-UAV cluster path planning method based on lightweight A-star algorithm as claimed in claim 1, characterized in that: The flight path of each UAV in the task group is optimized using an improved particle swarm algorithm for the best flight path planning solution, including: The improved particle swarm algorithm initializes the particle swarm position according to the flight path of each task group UAV in the optimal flight path planning scheme, and gives it an initialization speed; Calculate the fitness value of each particle according to the fitness formula; Update the individual extreme value and global extreme value of particles according to the fitness value; Update the particle position and velocity according to the improved particle position and velocity update formula; Calculate the fitness of the particle at the new position and update the individual extreme value and global extreme value of the particle; Determine whether the end condition is met. If so, the algorithm ends. If not, continue to update the particle position and velocity, and cyclically search for the minimum value of the flight path distance until the end condition is met.

6. The multi-UAV cluster path planning method based on lightweight A-star algorithm as claimed in claim 5, characterized in that: The improved particle position and speed update formula refers to improving the inertia weight in the particle position and speed update formula in the particle swarm algorithm so as to achieve adaptive dynamic adjustment.

7. The multi-UAV cluster path planning method based on lightweight A-star algorithm as claimed in claim 6, characterized in that: The improvement of the inertia weight in the particle position and velocity update formula in the particle swarm algorithm to achieve adaptive dynamic adjustment includes: A logarithmic function is used to control the change of inertia weight, and a random adjustment number obeying Gaussian distribution is added to implement dynamic adjustment of inertia weight during the evolution process.

8. The multi-UAV cluster path planning method based on lightweight A-star algorithm as claimed in claim 7, characterized in that: The inertia weight ω d The expression is: Where t is the current iteration number; T is the maximum iteration number; α is the inertia adjustment factor; randn is a random number that satisfies the Gaussian distribution; ω min Indicates the minimum value of the inertia weight without Gaussian random number perturbation, which is used to limit the lower bound of the inertia weight change; ω max It represents the maximum value of the inertia weight without Gaussian random number disturbance, which is used to limit the upper limit of the inertia weight change.

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