A multi-aircraft cooperative search task planning method, system, device and medium

Abstract

This application provides a method, system, device, and medium for multi-aircraft cooperative search mission planning, belonging to the field of aircraft technology. The method includes: acquiring aircraft cluster information, no-fly zone information, and patrol target information; using a greedy algorithm or auction algorithm for initial task allocation, assigning patrol targets to each aircraft and generating an initial task sequence; using a zigzag path planning algorithm to generate a scanning path for the target area, generating a coverage path by intersecting horizontal scan lines with polygonal areas; detecting collision risks between aircraft paths and obstacles, and when a collision risk is detected, using an obstacle avoidance algorithm based on safe distance expansion to generate a detour path; when an aircraft is detected to have completed a task or is in an idle state, triggering dynamic task reassignment, reassigning unfinished tasks to idle aircraft; and outputting the final multi-aircraft cooperative mission planning scheme. This achieves efficient dynamic mission planning for multi-aircraft cooperative operations.

Description

Technical Field

[0001] This invention belongs to the field of aircraft technology, and in particular relates to a method, system, device and medium for planning multi-aircraft cooperative search missions. Background Technology

[0002] With the rapid development of aircraft technology and the continuous expansion of application scenarios, multi-aircraft collaborative operations have shown great potential in many fields. Aircraft swarms can accomplish complex tasks that are difficult for a single aircraft to handle through collaborative work, improving operational efficiency and reducing labor costs. However, with the expansion of application scale and the increase in task complexity, dynamic task planning for multi-aircraft systems faces numerous technical challenges.

[0003] In existing technologies, researchers have proposed various solutions to the multi-aircraft mission planning problem. Some solutions employ static path planning methods, pre-planning fixed flight paths for each aircraft; others use a centralized control architecture, centrally scheduling multiple aircraft through a ground control station; still others are based on heuristic algorithms for task allocation, such as genetic algorithms and particle swarm optimization. These methods can achieve collaborative operation of multiple aircraft in certain scenarios, improving mission execution efficiency.

[0004] However, existing technologies still have significant shortcomings in practical applications. Traditional static planning methods cannot adapt to dynamic environmental changes; when new obstacles appear or tasks change, global planning needs to be re-performed, leading to system response delays. Centralized control architectures are prone to single points of failure, and computational pressure increases dramatically with the number of aircraft. Existing task allocation algorithms often ignore individual differences in aircraft and real-time state changes, making it difficult to achieve true load balancing. In path planning, most methods do not fully consider the impact of sensor parameters on the scanning path, resulting in low area coverage efficiency. Obstacle avoidance algorithms generally lack consideration of aircraft dynamic constraints, and the generated detour paths often do not meet actual flight requirements. Cooperative scheduling mechanisms are not flexible enough and cannot effectively handle unexpected situations during task execution. Summary of the Invention

[0005] This invention provides a method, system, device, and medium for planning multi-aircraft cooperative search missions, in order to at least solve the problems of insufficient adaptability to dynamic environments, low resource allocation efficiency, and difficulty in real-time obstacle avoidance in complex obstacle environments in the prior art.

[0006] In a first aspect, embodiments of this application provide a multi-aircraft cooperative search mission planning method, the method comprising: Step S1: Obtain mission planning data, including aircraft cluster information, no-fly zone information, and patrol target information. The aircraft cluster information includes the initial position, flight speed, range, and sensor parameters of each aircraft. Step S2: Based on the aircraft cluster information and the inspection target information, an initial task allocation is performed using a greedy algorithm or an auction algorithm to allocate the inspection targets to each aircraft and generate an initial task sequence. The objective of the initial task allocation is to minimize the overall task completion time. Step S3: For the initial mission sequence of each aircraft, combined with the sensor parameters and no-fly zone information, a zigzag path planning algorithm is used to generate a scanning path for the target area. The zigzag path planning generates a coverage path by intersecting the horizontal scan line with the polygonal area. Step S4: During the path execution process, the collision risk between the aircraft path and obstacles is detected in real time by combining the flight speed, range and no-fly zone information. When a collision risk is detected, an obstacle avoidance algorithm based on safe distance expansion is used to generate a detour path. Step S5: Monitor the mission execution status of the aircraft through collaborative scheduling. Combined with the initial position and flight speed, when it is detected that the aircraft has completed the mission or is in an idle state, trigger dynamic mission reallocation and reassign the unfinished missions to the idle aircraft. Step S6: Output the final multi-aircraft collaborative mission planning scheme, including the complete flight path and mission time sequence of each aircraft.

[0007] Furthermore, in step S2, the initial task allocation is performed based on a greedy algorithm, specifically including: Step S211: Initialize the aircraft mission time array and mission assignment dictionary; Step S212: Traverse the task queue and calculate the estimated completion time for each task after it is assigned to each aircraft. The estimated completion time is calculated based on the initial position, flight speed and task distance. Step S213: Assign the task to the aircraft with the lightest current load and update the aircraft task time; Step S214: Repeat steps S212-S213 until all tasks are assigned.

[0008] Furthermore, in step S2, initial task allocation is performed based on the auction algorithm, specifically including: Step S221: Broadcast mission information to all aircraft; Step S222: Each aircraft calculates its bidding price based on its own status and mission information, whereby its own status includes current load, remaining range, and sensor parameters; Step S223: Select the aircraft with the best bid as the successful bidder; Step S224: Verify the load capacity of the winning aircraft; if it is overloaded, the bidding process will be restarted.

[0009] Further, in step S3, the expression for path generation in the zigzag path planning algorithm is:

[0010] in, Indicates path length. Indicates flight altitude. This indicates the sensor's field of view.

[0011] Furthermore, in step S4, the obstacle avoidance algorithm employs a multi-level detour strategy, specifically including: Step S41: Perform safe distance expansion processing on the vertices of the obstacle polygon, the expression of which is:

[0012] in, Indicates a safe distance. Indicates the speed of the aircraft. Indicates the included angle at the vertices of the obstacle; Step S42: Produce a single detour route and check its feasibility; Step S43: If a single detour is not feasible, proceed with a double detour route; Step S44: If it is still not feasible, select the nearest outward-expanding vertex as the conservative detour point.

[0013] Furthermore, in step S5, the monitoring of the aircraft mission execution status through collaborative scheduling specifically includes: Step S51: Monitor the Euclidean distance between the aircraft and the target point in real time, and determine that the target point has been reached when the distance is less than the threshold; Step S52: Update the aircraft mission status and check the mission completion status; Step S53: When an idle aircraft is detected, combine the initial position and flight speed to locate unfinished area tasks; Step S54: Assign the task to an idle aircraft using the nearest neighbor matching strategy.

[0014] Furthermore, the method also includes a special inspection point processing step: Step S7: For special inspection points, based on the sensor parameters, calculate the aircraft's takeoff position and detour path to ensure that the minimum safe slant distance requirement is met. The formula for calculating the minimum safe slant distance is:

[0015] in, Indicates the minimum safe slant distance. Indicates the altitude of the aircraft. Indicates the vertical field of view.

[0016] Secondly, embodiments of this application also provide a system applied to the multi-vehicle cooperative search mission planning method as described in the above aspects, the system comprising: The data acquisition module is used to acquire mission planning data, including aircraft cluster information, no-fly zone information, and patrol target information. The aircraft cluster information includes the initial position, flight speed, range, and sensor parameters of each aircraft. The task allocation module is used to perform initial task allocation based on the aircraft cluster information and the inspection target information, using a greedy algorithm or an auction algorithm to allocate the inspection targets to each aircraft and generate an initial task sequence. The objective of the initial task allocation is to minimize the overall task completion time. The path planning module is used to generate a scanning path for the target area by combining the sensor parameters and no-fly zone information with the initial mission sequence of each aircraft and using a zigzag path planning algorithm. The zigzag path planning generates a coverage path by intersecting the horizontal scan line with the polygonal area. The obstacle avoidance module is used to detect the collision risk between the aircraft path and obstacles in real time during the path execution process, combining the flight speed, range and no-fly zone information. When a collision risk is detected, an obstacle avoidance algorithm based on safe distance expansion is used to generate an alternative path. The collaborative scheduling module is used to monitor the mission execution status of the aircraft through collaborative scheduling. Combined with the initial position and flight speed, when it is detected that the aircraft has completed the mission or is in an idle state, dynamic mission reallocation is triggered to reassign the unfinished missions to the idle aircraft. The scheme output module is used to output the final multi-aircraft collaborative mission planning scheme, including the complete flight path and mission time sequence of each aircraft.

[0017] Thirdly, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the steps of the multi-aircraft cooperative search mission planning method as described in the preceding aspects.

[0018] Fourthly, a storage medium storing a computer program that, when executed by a processor, implements the steps of the multi-aircraft cooperative search mission planning method as described in the preceding aspects.

[0019] As can be seen from the above technical solutions, the present invention has the following advantages: The multi-aircraft cooperative search task planning method provided in this application effectively solves the load balancing problem of multi-aircraft task allocation by using a greedy algorithm or auction algorithm for initial task allocation. Based on the aircraft cluster information and the inspection target information, the method calculates the estimated completion time and allocates the task to the aircraft with the lightest current load, thereby achieving optimal allocation of system resources. This improves task allocation efficiency, ensures a balanced workload for each aircraft, and avoids situations where some aircraft are overloaded while others are idle.

[0020] This application solves the problem of low regional coverage efficiency by generating scanning paths for target areas using a zigzag path planning algorithm. This algorithm combines sensor parameters and no-fly zone information to generate coverage paths by intersecting horizontal scan lines with polygonal regions, ensuring the integrity and efficiency of scanning. It can automatically adjust the path density according to the sensor's field of view, minimizing duplicate and missed scans while ensuring full coverage.

[0021] An obstacle avoidance algorithm based on safe distance extension is adopted to improve the safety of aircraft flight. This algorithm detects collision risks in real time during path execution and generates a safe detour path by combining flight speed, range, and no-fly zone information. Through a multi-level detour strategy, the reliability of obstacle avoidance is ensured while considering the optimality of the path, effectively avoiding the increased energy consumption caused by sharp turns and frequent adjustments.

[0022] By monitoring the mission execution status of aircraft through a collaborative scheduling mechanism, dynamic mission reassignment is achieved. This method combines initial position and flight speed, and when it detects that an aircraft has completed its mission or is in an idle state, it can promptly reassign unfinished missions to idle aircraft. This ensures that the system can still maintain efficient operation when some aircraft fail or missions change, thereby improving the robustness and adaptability of the system. Attached Figure Description

[0023] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of the multi-aircraft cooperative search mission planning method described in this invention. Detailed Implementation

[0025] To make the purpose, features, and advantages of this application more apparent and understandable, specific embodiments and accompanying drawings will be used to clearly and completely describe the technical solution protected by this application. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0026] This application provides a method, system, device, and medium for multi-aircraft cooperative search mission planning, which solves the current urgent technical problem of needing an efficient dynamic mission planning method for multi-aircraft cooperative operations.

[0027] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0028] Figure 1 A flowchart illustrating a multi-aircraft cooperative search mission planning method provided in this application embodiment. Figure 1 As shown in the figure, the multi-aircraft cooperative search mission planning method provided in this application embodiment specifically includes the following steps: Step S1: Obtain mission planning data, including aircraft cluster information, no-fly zone information, and patrol target information. The aircraft cluster information includes the initial position, flight speed, range, and sensor parameters of each aircraft. Data acquisition is achieved through multiple interfaces: aircraft cluster information is read from airborne sensors and the flight control system; no-fly zone information is loaded from a geographic information system database; and patrol target information is input through the mission planning interface. During implementation, a unified data format standard needs to be established to ensure that data from different sources can be correctly parsed and processed. Data preprocessing includes coordinate system 1, unit conversion, and data validity verification.

[0029] By acquiring mission planning data containing complete information such as the initial position, flight speed, range, and sensor parameters of the aircraft, a comprehensive and accurate input foundation is provided for subsequent algorithms. This ensures that the individual differences and actual performance constraints of each aircraft can be fully considered during mission allocation and path planning, providing reliable data support for achieving precise multi-aircraft cooperative control.

[0030] Step S2: Based on the aircraft cluster information and the inspection target information, an initial task allocation is performed using a greedy algorithm or an auction algorithm to allocate the inspection targets to each aircraft and generate an initial task sequence. The objective of the initial task allocation is to minimize the overall task completion time. The algorithm automatically switches between a greedy algorithm and an auction algorithm based on the selection strategy. The greedy algorithm maintains a task priority queue, sorting tasks according to their urgency. The auction algorithm employs a distributed decision-making mechanism, with each aircraft independently calculating its bid price. The key to successful implementation lies in designing a reasonable cost function that comprehensively considers flight distance, mission value, and the suitability of the aircraft's capabilities.

[0031] By using greedy or auction algorithms to perform initial task allocation based on aircraft cluster information and patrol target information, and taking minimizing the overall task completion time as the optimization objective, efficient resource allocation in multi-aircraft, multi-task scenarios is achieved. This effectively avoids the load imbalance problem caused by the traditional fixed allocation mode and significantly improves the overall operational efficiency of the system.

[0032] Step S3: For the initial mission sequence of each aircraft, combined with the sensor parameters and no-fly zone information, a zigzag path planning algorithm is used to generate a scanning path for the target area. The zigzag path planning generates a coverage path by intersecting the horizontal scan line with the polygonal area. The path planning process begins with gridding the target area and determining the scan line spacing based on the sensor's field of view. The zigzag path achieves full coverage through alternating directional scanning, automatically adjusting the path when encountering no-fly zones. Implementation requires handling various special terrain conditions, including convex polygons, concave polygons, and areas containing holes.

[0033] By combining sensor parameters and no-fly zone information, a zigzag path planning algorithm is used to generate a scanning path for the target area. The intersection of the horizontal scan line and the polygonal area is used to generate a coverage path, which ensures that the aircraft can completely cover the target area in complex terrain environments. At the same time, it avoids the repeated scanning and missed scanning phenomena that exist in traditional methods, and greatly improves the quality of area inspection operations.

[0034] Step S4: During the path execution process, the collision risk between the aircraft path and obstacles is detected in real time by combining the flight speed, range and no-fly zone information. When a collision risk is detected, an obstacle avoidance algorithm based on safe distance expansion is used to generate a detour path. By combining flight speed, range, and no-fly zone information to detect collision risks in real time during path execution, and using an obstacle avoidance algorithm based on safe distance expansion to generate detour paths, autonomous and safe obstacle avoidance of aircraft in dynamic environments is achieved. This effectively reduces the risk of flight accidents caused by sudden obstacles and ensures the continuous and stable operation of the aircraft cluster.

[0035] Step S5: Monitor the mission execution status of the aircraft through collaborative scheduling. Combined with the initial position and flight speed, when it is detected that the aircraft has completed the mission or is in an idle state, trigger dynamic mission reallocation and reassign the unfinished missions to the idle aircraft. By coordinating and monitoring the mission execution status of aircraft, and combining initial position and flight speed to trigger dynamic mission reallocation when the aircraft completes its mission or becomes idle, the system achieves real-time optimization of resource allocation, significantly improves the mission response speed and resource utilization of the multi-aircraft system, and ensures the continuous and efficient operation of the system when some aircraft fail or missions change.

[0036] Step S6: Output the final multi-aircraft collaborative mission planning scheme, including the complete flight path and mission time sequence of each aircraft.

[0037] By employing greedy or auction algorithms for initial task allocation, and combining aircraft cluster information and patrol target information to generate an initial task sequence, collision risks are detected in real time during path execution, and an obstacle avoidance algorithm based on safe distance expansion is used to generate detour paths. At the same time, dynamic task reallocation is triggered by monitoring the aircraft task execution status through collaborative scheduling. Finally, a complete multi-aircraft collaborative task planning scheme is output, realizing global optimization scheduling of multi-aircraft systems under multiple constraints, and improving the task completion efficiency of multi-aircraft collaborative operations in complex environments.

[0038] In an exemplary embodiment, step S2, which involves initial task allocation based on a greedy algorithm, specifically includes: Step S211: Initialize the aircraft mission time array and mission assignment dictionary; Step S212: Traverse the task queue and calculate the estimated completion time for each task after it is assigned to each aircraft. The estimated completion time is calculated based on the initial position, flight speed and task distance. Step S213: Assign the task to the aircraft with the lightest current load and update the aircraft task time; Step S214: Repeat steps S212-S213 until all tasks are assigned.

[0039] By initializing the task time array and the allocation dictionary, traversing the task queue to calculate the estimated completion time, and allocating the task to the aircraft with the lightest load, a progressive optimal allocation based on a greedy strategy is achieved, ensuring that an approximate optimal solution is obtained in polynomial time. This achieves good load balancing while ensuring solution efficiency.

[0040] According to another embodiment of the present invention, in step S2, the initial task allocation based on the auction algorithm specifically includes: Step S221: Broadcast mission information to all aircraft; Step S222: Each aircraft calculates its bidding price based on its own status and mission information, whereby its own status includes current load, remaining range, and sensor parameters; Step S223: Select the aircraft with the best bid as the successful bidder; Step S224: Verify the load capacity of the winning aircraft; if it is overloaded, the bidding process will be restarted.

[0041] By broadcasting mission information to all aircraft, each aircraft calculates its bid based on its own status and selects the best bidder. Finally, the load capacity is verified, achieving efficient task allocation under a distributed decision-making mechanism. This fully leverages the autonomous decision-making capabilities of each aircraft and improves the system's adaptability and fault tolerance in dynamic environments.

[0042] According to an embodiment of this application, in step S3, the expression for path generation by the zigzag path planning algorithm is:

[0043] in, Indicates path length. Indicates flight altitude. This represents the sensor field of view, which is derived from the sensor parameters.

[0044] A precise path planning quantification model was established to ensure the optimal match between the scanning path spacing and sensor performance, maximizing scanning efficiency while ensuring coverage quality, and providing a scientific basis for path planning for regional inspection missions.

[0045] In one embodiment, in step S4, the obstacle avoidance algorithm employs a multi-level detour strategy, specifically including: Step S41: Perform safe distance expansion processing on the vertices of the obstacle polygon, the expression of which is:

[0046] in, Indicates a safe distance. Indicates the speed of the aircraft. Indicates the included angle at the vertices of the obstacle; Step S42: Produce a single detour route and check its feasibility; Step S43: If a single detour is not feasible, proceed with a double detour route; Step S44: If it is still not feasible, select the nearest outward-expanding vertex as the conservative detour point.

[0047] By adopting an obstacle avoidance algorithm that includes safety distance extension processing and multi-level detour strategies, a progressive obstacle avoidance path generation mechanism from simple to complex was established, ensuring that feasible safe paths can be generated in various complex obstacle environments, which greatly improves the success rate and practicality of the obstacle avoidance algorithm.

[0048] As an example, step S5, which involves monitoring the aircraft mission execution status through collaborative scheduling, specifically includes: Step S51: Monitor the Euclidean distance between the aircraft and the target point in real time, and determine that the target point has been reached when the distance is less than the threshold; Step S52: Update the aircraft mission status and check the mission completion status; Step S53: When an idle aircraft is detected, combine the initial position and flight speed to locate unfinished area tasks; Step S54: Assign the task to an idle aircraft using the nearest neighbor matching strategy.

[0049] By monitoring the Euclidean distance between the aircraft and the target point in real time to determine the mission status, and using the nearest neighbor matching strategy to reallocate the mission when an idle aircraft is found, an efficient mission execution monitoring and dynamic scheduling mechanism has been established, ensuring that the system can respond to changes in mission status in a timely manner and make the most of available resources.

[0050] Furthermore, as a refinement and extension of the specific implementation methods described above, and to fully illustrate the specific implementation process in this embodiment, another multi-aircraft cooperative search mission planning method is provided. This method further includes a special patrol point processing step: Step S7: For special inspection points, based on the sensor parameters, calculate the aircraft's takeoff position and detour path to ensure that the minimum safe slant distance requirement is met. The formula for calculating the minimum safe slant distance is:

[0051] in, Indicates the minimum safe slant distance. Indicates the altitude of the aircraft. This represents the vertical field of view, which is derived from the sensor parameters.

[0052] By combining sensor parameters to calculate the aircraft's takeoff position and detour path, and ensuring that the minimum safe slant distance requirement is met, a special observation scheme is provided for special inspection points, ensuring the best observation effect within a safe distance and expanding the system's application capabilities in special scenarios.

[0053] This invention also provides a multi-aircraft cooperative search mission planning system. The following are embodiments of the multi-aircraft cooperative search mission planning system provided in this disclosure. This multi-aircraft cooperative search mission planning system belongs to the same inventive concept as the multi-aircraft cooperative search mission planning methods described above. Details not fully described in the embodiments of the multi-aircraft cooperative search mission planning system can be found in the embodiments of the multi-aircraft cooperative search mission planning methods described above. The system includes: The data acquisition module is used to acquire mission planning data, including aircraft cluster information, no-fly zone information, and patrol target information. The aircraft cluster information includes the initial position, flight speed, range, and sensor parameters of each aircraft. The task allocation module is used to perform initial task allocation based on the aircraft cluster information and the inspection target information, using a greedy algorithm or an auction algorithm to allocate the inspection targets to each aircraft and generate an initial task sequence. The objective of the initial task allocation is to minimize the overall task completion time. The path planning module is used to generate a scanning path for the target area by combining the sensor parameters and no-fly zone information with the initial mission sequence of each aircraft and using a zigzag path planning algorithm. The zigzag path planning generates a coverage path by intersecting the horizontal scan line with the polygonal area. The obstacle avoidance module is used to detect the collision risk between the aircraft path and obstacles in real time during the path execution process, combining the flight speed, range and no-fly zone information. When a collision risk is detected, an obstacle avoidance algorithm based on safe distance expansion is used to generate an alternative path. The collaborative scheduling module is used to monitor the mission execution status of the aircraft through collaborative scheduling. Combined with the initial position and flight speed, when it is detected that the aircraft has completed the mission or is in an idle state, dynamic mission reallocation is triggered to reassign the unfinished missions to the idle aircraft. The scheme output module is used to output the final multi-aircraft collaborative mission planning scheme, including the complete flight path and mission time sequence of each aircraft.

[0054] The multi-aircraft cooperative search mission planning method provided in this application can be applied to electronic devices. Those skilled in the art will understand that the electronic device structure involved in the embodiments of this invention does not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. In the embodiments of this invention, the electronic device includes, but is not limited to, laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.

[0055] Electronic devices may include processors, external memory interfaces, internal memory, universal serial bus (USB) interfaces, charging management modules, power management modules, batteries, wireless communication modules, audio modules, speakers, microphones, sensor modules, buttons, cameras, displays, and SIM card interfaces, etc.

[0056] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the electronic device. In other embodiments of this application, the electronic device may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0057] A processor may include one or more processing units, such as: a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors.

[0058] The processor can serve as the nerve center and command center of an electronic device. The controller can generate operation control signals based on the instruction opcode and timing signals to control the fetching and execution of instructions.

[0059] The processor may also include memory for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can store instructions or data that the processor has just used or that are used repeatedly. If the processor needs to use the instruction or data again, it can retrieve it directly from this memory. This avoids repeated accesses, reduces processor latency, and thus improves system efficiency.

[0060] An external storage interface (ESI) can be used to connect external memory cards, such as microSD cards, to expand the storage capacity of electronic devices. The external memory card communicates with the processor through the ESI to perform data storage functions, such as saving music and video files on the external memory card.

[0061] Internal memory can be used to store computer executable program code, which includes instructions. The processor executes various functional applications and data processing of electronic devices by running the instructions stored in internal memory. Internal memory can include a program storage area and a data storage area. Internal memory can include high-speed random access memory, and can also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0062] Wireless communication functionality in electronic devices can be achieved through antennas, wireless communication modules, modem processors, and baseband processors.

[0063] Wireless communication modules can provide solutions for wireless communication applications in electronic devices, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies.

[0064] Electronic devices can implement audio functions through audio modules, speakers, receivers, microphones, headphone jacks, and application processors.

[0065] Electronic devices can achieve shooting functions through ISPs, cameras, video codecs, GPUs, displays, and application processors.

[0066] Electronic devices can achieve display functions through GPUs, displays, and application processors.

[0067] A GPU is a microprocessor for image processing, connected to the display screen and application processor. GPUs are used to perform mathematical and geometric calculations for graphics rendering. A processor may include one or more GPUs, which execute program instructions to generate or modify display information.

[0068] A display screen is used to display images, videos, etc. A display screen includes a display panel.

[0069] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0070] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0071] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, apparatuses, or units, or they may be electrical, mechanical, or other forms of connection.

[0072] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a full understanding of embodiments of the invention. However, those skilled in the art will recognize that the technical solutions of the invention can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of the invention.

[0073] The aforementioned electronic device realizes the acquisition of task planning data for the multi-aircraft cooperative search task planning method of this application, including aircraft cluster information, no-fly zone information, and patrol target information. The aircraft cluster information includes the initial position, flight speed, range, and sensor parameters of each aircraft. Based on the aircraft cluster information and patrol target information, a greedy algorithm or auction algorithm is used to allocate initial tasks, assigning patrol targets to each aircraft and generating an initial task sequence. The objective of the initial task allocation is to minimize the overall task completion time. For each aircraft's initial task sequence, combined with the sensor parameters and no-fly zone information, a zigzag path planning algorithm is used to generate a scanning path for the target area. The system generates a coverage path by intersecting horizontal scan lines with polygonal regions. During path execution, it uses flight speed, range, and no-fly zone information to detect collision risks between the aircraft path and obstacles in real time. When a collision risk is detected, an obstacle avoidance algorithm based on safe distance expansion is used to generate a detour path. The system monitors the aircraft's mission execution status through collaborative scheduling. Combining the initial position and flight speed, it triggers dynamic task reassignment when an aircraft completes its mission or is idle, reassigning unfinished tasks to idle aircraft. The final multi-aircraft collaborative mission planning scheme is output, including the complete flight paths and mission time sequences of each aircraft, achieving efficient dynamic mission planning for multi-aircraft collaborative operations.

[0074] The storage medium provided in this application stores a program product capable of implementing a multi-aircraft cooperative search mission planning method.

[0075] Multi-vehicle cooperative search mission planning methods include: Step S1: Obtain mission planning data, including aircraft cluster information, no-fly zone information, and patrol target information. The aircraft cluster information includes the initial position, flight speed, range, and sensor parameters of each aircraft. Step S2: Based on the aircraft cluster information and the inspection target information, an initial task allocation is performed using a greedy algorithm or an auction algorithm to allocate the inspection targets to each aircraft and generate an initial task sequence. The objective of the initial task allocation is to minimize the overall task completion time. Step S3: For the initial mission sequence of each aircraft, combined with the sensor parameters and no-fly zone information, a zigzag path planning algorithm is used to generate a scanning path for the target area. The zigzag path planning generates a coverage path by intersecting the horizontal scan line with the polygonal area. Step S4: During the path execution process, the collision risk between the aircraft path and obstacles is detected in real time by combining the flight speed, range and no-fly zone information. When a collision risk is detected, an obstacle avoidance algorithm based on safe distance expansion is used to generate a detour path. Step S5: Monitor the mission execution status of the aircraft through collaborative scheduling. Combined with the initial position and flight speed, when it is detected that the aircraft has completed the mission or is in an idle state, trigger dynamic mission reallocation and reassign the unfinished missions to the idle aircraft. Step S6: Output the final multi-aircraft collaborative mission planning scheme, including the complete flight path and mission time sequence of each aircraft.

[0076] By employing a greedy algorithm or auction algorithm for initial task allocation, the load balancing problem of multi-aircraft task allocation is effectively solved. Based on aircraft cluster information and patrol target information, the estimated completion time is calculated and tasks are allocated to the aircraft with the lightest current load, achieving optimal allocation of system resources. This improves task allocation efficiency, ensures balanced workload for each aircraft, and avoids situations where some aircraft are overloaded while others are idle.

[0077] In some possible implementations, the multi-vehicle cooperative search mission planning method of this disclosure can be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0078] The storage medium disclosed herein may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0079] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0080] Any changes, modifications, substitutions, and variations made to the embodiments without departing from the principles and spirit of the present invention still fall within the protection scope of the present invention.

Claims

1. A multi-aircraft cooperative search mission planning method, characterized in that, The method includes: Step S1: Obtain mission planning data, including aircraft cluster information, no-fly zone information, and patrol target information. The aircraft cluster information includes the initial position, flight speed, range, and sensor parameters of each aircraft. Step S2: Based on the aircraft cluster information and the inspection target information, an initial task allocation is performed using a greedy algorithm or an auction algorithm to allocate the inspection targets to each aircraft and generate an initial task sequence. The objective of the initial task allocation is to minimize the overall task completion time. Step S3: For the initial mission sequence of each aircraft, combined with the sensor parameters and no-fly zone information, a zigzag path planning algorithm is used to generate a scanning path for the target area. The zigzag path planning generates a coverage path by intersecting the horizontal scan line with the polygonal area. Step S4: During the path execution process, the collision risk between the aircraft path and obstacles is detected in real time by combining the flight speed, range and no-fly zone information. When a collision risk is detected, an obstacle avoidance algorithm based on safe distance expansion is used to generate a detour path. Step S5: Monitor the mission execution status of the aircraft through collaborative scheduling. Combined with the initial position and flight speed, when it is detected that the aircraft has completed the mission or is in an idle state, trigger dynamic mission reallocation and reassign the unfinished missions to the idle aircraft. Step S6: Output the final multi-aircraft collaborative mission planning scheme, including the complete flight path and mission time sequence of each aircraft.

2. The method as described in claim 1, characterized in that, In step S2, the initial task allocation is performed based on a greedy algorithm, specifically including: Step S211: Initialize the aircraft mission time array and mission assignment dictionary; Step S212: Traverse the task queue and calculate the estimated completion time for each task after it is assigned to each aircraft. The estimated completion time is calculated based on the initial position, flight speed and task distance. Step S213: Assign the task to the aircraft with the lightest current load and update the aircraft task time; Step S214: Repeat steps S212-S213 until all tasks are assigned.

3. The method as described in claim 1, characterized in that, In step S2, initial task allocation is performed based on the auction algorithm, specifically including: Step S221: Broadcast mission information to all aircraft; Step S222: Each aircraft calculates its bidding price based on its own status and mission information, whereby its own status includes current load, remaining range, and sensor parameters; Step S223: Select the aircraft with the best bid as the successful bidder; Step S224: Verify the load capacity of the winning aircraft; if it is overloaded, the bidding process will be restarted.

4. The method as described in claim 1, characterized in that, In step S3, the expression for path generation in the zigzag path planning algorithm is: in, Indicates path length. Indicates flight altitude. This indicates the sensor's field of view.

5. The method as described in claim 1, characterized in that, In step S4, the obstacle avoidance algorithm employs a multi-level detour strategy, specifically including: Step S41: Perform safe distance expansion processing on the vertices of the obstacle polygon, the expression of which is: in, Indicates a safe distance. Indicates the speed of the aircraft. Indicates the included angle at the vertices of the obstacle; Step S42: Produce a single detour route and check its feasibility; Step S43: If a single detour is not feasible, proceed with a double detour route; Step S44: If it is still not feasible, select the nearest outward-expanding vertex as the conservative detour point.

6. The method as described in claim 1, characterized in that, In step S5, the monitoring of the aircraft mission execution status through collaborative scheduling specifically includes: Step S51: Monitor the Euclidean distance between the aircraft and the target point in real time, and determine that the target point has been reached when the distance is less than the threshold; Step S52: Update the aircraft mission status and check the mission completion status; Step S53: When an idle aircraft is detected, combine the initial position and flight speed to locate unfinished area tasks; Step S54: Assign the task to an idle aircraft using the nearest neighbor matching strategy.

7. The method as described in claim 1, characterized in that, The method also includes a special inspection point processing step: Step S7: For special inspection points, based on the sensor parameters, calculate the aircraft's takeoff position and detour path to ensure that the minimum safe slant distance requirement is met. The formula for calculating the minimum safe slant distance is: in, Indicates the minimum safe slant distance. Indicates the altitude of the aircraft. Indicates the vertical field of view.

8. A system applied to the multi-aircraft cooperative search mission planning method as described in any one of claims 1-7, characterized in that, The system includes: The data acquisition module is used to acquire mission planning data, including aircraft cluster information, no-fly zone information, and patrol target information. The aircraft cluster information includes the initial position, flight speed, range, and sensor parameters of each aircraft. The task allocation module is used to perform initial task allocation based on the aircraft cluster information and the inspection target information, using a greedy algorithm or an auction algorithm to allocate the inspection targets to each aircraft and generate an initial task sequence. The objective of the initial task allocation is to minimize the overall task completion time. The path planning module is used to generate a scanning path for the target area by combining the sensor parameters and no-fly zone information with the initial mission sequence of each aircraft and using a zigzag path planning algorithm. The zigzag path planning generates a coverage path by intersecting the horizontal scan line with the polygonal area. The obstacle avoidance module is used to detect the collision risk between the aircraft path and obstacles in real time during the path execution process, combining the flight speed, range and no-fly zone information. When a collision risk is detected, an obstacle avoidance algorithm based on safe distance expansion is used to generate an alternative path. The collaborative scheduling module is used to monitor the mission execution status of the aircraft through collaborative scheduling. Combined with the initial position and flight speed, when it is detected that the aircraft has completed the mission or is in an idle state, dynamic mission reallocation is triggered to reassign the unfinished missions to the idle aircraft. The scheme output module is used to output the final multi-aircraft collaborative mission planning scheme, including the complete flight path and mission time sequence of each aircraft.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the multi-aircraft cooperative search mission planning method as described in any one of claims 1-7.

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-aircraft cooperative search mission planning method as described in any one of claims 1-7.

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