Multi-task oriented time slot allocation and role switching method for unmanned aerial vehicle cluster system

Abstract

The application discloses a kind of unmanned aerial vehicle cluster multi-task time slot allocation and role switching method, solve the problem of low resource utilization, poor coordination, insufficient synchronization accuracy.Information collector, information discriminator, decision maker, communication coordinator and action executor are abstracted as task roles, and each role is mapped to a specific hardware module or software algorithm.The optimal task role allocation table is generated by improving the genetic algorithm using role reuse rate and task timeliness as the fitness function, enabling single unmanned aerial vehicle to reuse multiple roles.Based on performance quantization parameters, sub-slots are divided and their lengths are determined.≤20 level periodic synchronization is achieved through PTP or enhanced radio synchronization mechanism.The application can achieve multi-task parallelism without expanding the size of unmanned aerial vehicle, the role reuse rate is ≥80%, the task completion efficiency is improved by more than 30%, the synchronization confusion rate is reduced to less than 1%, and is suitable for complex scenarios such as military reconnaissance and environmental monitoring.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) swarm technology, and in particular to a method for time slot allocation and role switching in a multi-task UAV swarm system. The method aims to improve the efficiency and coordination of UAV swarms in multi-task execution scenarios through specific algorithm models, quantification rules, and technical implementations. It is applicable to practical industrial application scenarios such as military reconnaissance, environmental monitoring, and disaster relief. Background Technology

[0002] With the rapid development of drone technology, drone swarms have become core equipment for performing complex tasks. However, existing technologies have significant technical shortcomings: 1. The existing scheduling method adopts a round-robin scheduling mechanism, which does not take into account the heterogeneity of UAV roles and the need for task reuse. It only uses a simple "one-to-one" task allocation mode, resulting in UAV resource utilization of less than 40% and is unable to adapt to multi-task parallel scenarios. 2. The sub-slot division lacks quantitative standards and the duration is allocated based solely on experience. This results in some sub-slots being interrupted due to insufficient length or wasting resources due to excessive length, with an average data processing delay of more than 1 second. 3. Insufficient accuracy of the synchronization mechanism: The synchronization error of the traditional NTP protocol can reach the millisecond level, which causes coordination chaos in the drone swarm when switching time slots, resulting in a high mission execution failure rate; 4. Role allocation lacks optimization algorithm support and relies solely on manual rule formulation, which cannot maximize resource reuse and is difficult to adapt to dynamic changes in the number of drones or mission types.

[0003] Therefore, there is an urgent need for a technical solution with specific algorithm implementation, quantization rules and high-precision synchronization mechanism to solve the technical problems of low resource utilization, poor coordination and insufficient adaptability in the existing technology. Summary of the Invention

[0004] This invention aims to provide an innovative method for time slot allocation and role switching in a multi-task-oriented UAV swarm system. It overcomes many shortcomings in the existing technology through specific technical means. Its core improvement lies in transforming abstract management logic into an implementable and reproducible technical solution.

[0005] The technical solution of this invention specifically includes the following core technical features: Role-Hardware / Algorithm Binding Design: Five roles are mapped one-to-one with specific hardware modules or software algorithms of the drone, avoiding the risk of "intellectual activity rules" caused by role abstraction. Specifically, the information collector is bound to the sensor control module, directly driving the hardware to collect data; the information discriminator is bound to the onboard edge computing unit, running CNN feature extraction algorithms, Kalman filter outlier detection algorithms, and DS evidence theory data fusion models; the decision maker is bound to the multi-objective optimization decision module, generating decision commands based on the linear weighted sum method; the communication coordinator is bound to the TDMA-based communication scheduling module to achieve orderly communication; and the action executor is bound to the flight control and actuator module to ensure precise command execution.

[0006] Improved Genetic Algorithm for Role Assignment: Addressing the low resource reuse rate in existing technologies, an improved genetic algorithm is designed to generate a task role assignment table. This algorithm, through two-dimensional matrix encoding, a composite fitness function, and specific genetic operations, achieves a role reuse rate of ≥80% and improves task completion time by over 30% while satisfying the constraints of "full role coverage for a single task" and "multiple roles for a single UAV." The specific parameters of the algorithm were determined through extensive experimental optimization. Optimal overall performance was achieved when the number of iterations was set to 50-100 generations, and the weight coefficients α and β were set to 0.7 and 0.3, respectively.

[0007] Quantitative sub-time slot allocation rules: Based on UAV performance parameters (sensor type, sampling frequency, data processing rate) and quantity thresholds, clear sub-time slot allocation standards are established. For example, the length of the optical image acquisition sub-time slot is directly linked to the sampling frequency; the sub-time slot length is 0.03s at a sampling frequency of 30fps and 0.015s at a sampling frequency of 60fps, ensuring the integrity of data acquisition. The number of communication and coordination sub-time slots is equal to the number of UAVs, and the length of each sub-time slot is dynamically adjusted according to the data transmission volume to avoid information congestion.

[0008] Formulaic calculation of time slot length: through the establishment of a mathematical model Determine the time slot length, where the redundancy coefficient k is dynamically adjusted according to the real-time requirements of the task (k=1.1 for tasks with high real-time requirements, and k=1.3 for complex tasks). The minimum running time threshold for sub-slots is determined through experimental testing, with the maximum running time within a 95% confidence interval, ensuring that each role completes its task within the slot while avoiding resource waste.

[0009] High-precision periodic synchronization mechanism: Employing the PTP protocol or enhanced radio synchronization mechanism, the synchronization error is controlled within 20. Within this range. The PTP protocol achieves high-precision synchronization through master-slave clock interaction. The enhanced radio synchronization mechanism utilizes specific frequency and coded signals, and calculates the time difference using the TOA algorithm to ensure that all UAVs perform tasks under a unified time reference.

[0010] Through the above-described specific technical solutions, the present invention achieves the following beneficial effects: Improved resource utilization: By improving the genetic algorithm, the drone role reuse rate is increased to ≥80%, which is more than 40% higher than the existing technology; Coordination optimization: The high-precision synchronization mechanism ensures that the time slot switching error is ≤20μs, and the occurrence rate of task coordination chaos is reduced to less than 1%; Enhanced adaptability: Quantitative sub-slot partitioning and dynamically adjusted redundancy coefficients can adapt to scenarios with 5-20 drones and 3-10 parallel tasks; Improved execution efficiency: The average data processing latency is reduced to less than 0.5 seconds, and the total task completion time is shortened by 30% compared to existing technologies. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of a specific embodiment of the present invention; Figure 2 Task role allocation table as a specific embodiment of the present invention; Figure 3 This is a schematic diagram of sub-time slot allocation in a specific embodiment of the present invention.

[0012] Figure 4 This is a schematic diagram illustrating the time slot allocation for each UAV in a specific embodiment of the present invention; Detailed Implementation The following detailed description, in conjunction with the accompanying drawings and specific embodiments, provides a more detailed explanation of the time slot allocation and role switching method for a multi-task UAV swarm system proposed in this invention. The advantages and features of this invention will become clearer from the following description. It should be noted that the accompanying drawings are in a very simplified form and use non-precise proportions, intended only to facilitate and clarify the illustration of the embodiments of this invention, and are not intended to limit the implementation conditions of this invention. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in proportions, or adjustments to size, without affecting the effects and objectives achieved by this invention, should still fall within the scope of the technical content disclosed in this invention.

[0013] Example Parameter Settings; Task scenario: Environmental monitoring task in a certain area, divided into 4 monitoring areas (Task 1-Task 4), involving 4 drones (Drone 1-Drone 4). Drone performance parameters: Optical camera sampling frequency 30fps, resolution 1920×1080; Electromagnetic spectrum sensor sampling bandwidth 100MHz, data volume 10MB / s; Infrared thermal imager temperature measurement accuracy ±0.5℃, frame rate 15fps; Communication transmission rate 10Mbps; Algorithm parameters: Improved genetic algorithm with 80 iterations, α=0.7, β=0.3, convergence condition is fitness fluctuation <5% for 10 consecutive generations; time slot length redundancy coefficient k=1.2; Synchronization method: PTP protocol is used, the master clock is UAV 1, and the synchronization message sending period is 0.1s.

[0014] like Figure 1 As shown, Figure 1 This is a schematic diagram of a specific embodiment of the present invention; Character binding and quest number; Specifically, role binding: the information collector of UAVs 1-4 is bound to the sensor control module, the information discriminator runs the CNN feature extraction algorithm + DS evidence fusion model, the decision maker adopts the linear weighted sum method, the communication and coordination are based on the TDMA protocol, and the action executor is bound to the flight control module. Numbering rules: Task numbers are T1-T4, and drone numbers are U1-U4, using a three-dimensional identifier of "drone number-task number-role" (e.g., U1-T1-collector).

[0015] Generate a task role allocation table; Initialize the population: Generate 100 individuals, each individual being a 4×(4×5) two-dimensional matrix (4 drones, 4 tasks × 5 roles). Fitness calculation: F = 0.7R + 0.3E, where R = (number of reused roles / total number of roles required), E = (theoretical minimum task duration / actual planned duration); Genetic operations: Select 10 optimal individuals, perform crossover using a single point crossover in column 20, and perform mutation to randomly swap the two role assignment relationships; Optimization result: After 80 iterations, convergence was achieved, yielding the optimal allocation table. Please refer to [link / reference]. Figure 2 , Figure 2 Task role allocation table as a specific embodiment of the present invention; Specifically, UAV 1 acts as an information gatherer in Mission 1, an information screener in Mission 2, a decision maker in Mission 3, and an action executor in Mission 4; UAV 2 acts as an information gatherer in Mission 2, an information screener in Mission 1, a decision maker in Mission 4, and an action executor in Mission 3; UAV 3 acts as an information gatherer in Mission 3, an information screener in Mission 4, a decision maker in Mission 1, and an action executor in Mission 2; UAV 4 acts as an information gatherer in Mission 4, an information screener in Mission 3, a decision maker in Mission 2, and an action executor in Mission 1.

[0016] Regarding the determination of sub-time slots, based on the UAV's performance, within the information collector's time slot, UAV 1's optical image acquisition sub-time slot activates its optical camera to acquire images of area A. The electromagnetic spectrum acquisition sub-time slot can be used to collect information on potential electromagnetic interference sources within area A. The infrared image acquisition sub-time slot acquires infrared thermal imaging information of area A (if there are relevant monitoring needs). UAV 2 performs similar sub-time slot operations for different types of information acquisition in area B, and so on. Depending on the number of UAVs, within the global communication sub-time slot of the communication coordinator's time slot, the four UAVs sequentially broadcast and share their acquired data and task status information, ensuring that all UAVs receive comprehensive information for better collaborative monitoring of these four areas. Please refer to [link to relevant documentation]. Figure 3 , Figure 3 This is a schematic diagram of sub-slot allocation according to a specific embodiment of the present invention; The sub-slot time is set in this embodiment of the invention as follows: Information collector sub-slots: Optical image acquisition sub-slot 0.03s (30fps), electromagnetic spectrum acquisition sub-slot 0.1s (10MB / 10Mbps), infrared image acquisition sub-slot 0.07s (15fps); Information discriminator sub-slots: optical image processing sub-slot 0.2s (1920×1080 image), electromagnetic spectrum processing sub-slot 0.3s (10MB data), infrared image processing sub-slot 0.07s, fusion processing sub-slot 0.5s; Communication Coordinator Sub-time Slots: 4 drones correspond to 4 sub-time slots, each sub-time slot length is... (Total length of communication coordinator time slot: 0.2s).

[0017] In the time slot allocation phase, eight time slots are allocated according to a set operating cycle. In the information collector time slot, UAV 1 activates its optical camera to enter the optical image acquisition sub-slot, capturing optical images of the ground conditions in area A. It then enters the electromagnetic spectrum acquisition sub-slot to collect electromagnetic signal data within area A, and finally enters the infrared image acquisition sub-slot to acquire infrared thermal imaging information of area A. Simultaneously, UAV 2 performs a similar information collection process in area B, UAV 3 in area C, and UAV 4 in area D. In the information screener time slot, each UAV follows a task role allocation table. For example, UAV 1 processes data collected by UAV 2, UAV 2 processes data from UAV 3, UAV 3 processes data from UAV 4, and UAV 4 processes data from UAV 1. This cross-processing method fully utilizes the computing resources of each UAV and achieves comprehensive data analysis. In the decision-maker time slot, all the screened and processed information is synthesized to determine the more precise monitoring priorities for each area and the UAV action adjustment strategies. In the executor time slot, each drone performs corresponding actions according to the decision. For example, drone 1, based on the decision, conducts more detailed image acquisition or adjusts the sensor acquisition frequency at locations in area A that may have anomalies. Drone 2 performs similar operations in area B, and so on. The communication coordinator time slot is distributed after each key link. In a global communication scenario (excluding the communication coordinator time slot after the decision maker time slot), such as after the information collector and information screener time slots, each drone shares the information it has collected or processed with the other three drones to ensure that all drones have a comprehensive understanding of the overall situation in the four areas. In a local communication scenario (only in the communication coordinator time slot after the decision maker time slot), for example, drone 3 accurately conveys the decision information formulated for area D to drone 4, which needs to act based on this decision in mission four.

[0018] Please see Figure 4 , Figure 4 This is a schematic diagram illustrating the time slot allocation for each UAV in a specific embodiment of the present invention; The time slot duration is set in this embodiment of the invention as follows: Acquisition time slot ; Identify time slots ; Decision-making time slot ; Execution slot ; Communication gaps ; Total cycle .

[0019] Periodic synchronization is achieved: Master clock U1 sends a message every 0.1 seconds containing... Synchronization messages with precise timestamps; After receiving clock signals from U2-U4, the clock deviation is calculated, and the clock itself is adjusted to keep the synchronization error within 10. within; Time slot switching trigger: All drones synchronize to enter the next time slot based on the master clock timestamp. For example, the T acquisition time slot is 0-0.24s, the C1 time slot is 0.24-0.44s, and so on.

[0020] Through this implementation process, the present invention can effectively improve the execution efficiency and coordination of UAV swarms in multi-task scenarios, give full play to the advantages of UAV swarms, and achieve comprehensive and efficient monitoring of the divided areas.

Claims

1. A method for time slot allocation and role switching in a multi-task unmanned aerial vehicle (UAV) swarm system, characterized in that, Includes the following steps: Step 1: Abstract the roles required to complete the cluster task into five types, and map them to specific hardware modules or software algorithms of the UAV: ​​information collector (corresponding to the sensor control module), information discriminator (corresponding to the intelligent processing algorithm of the airborne edge computing unit), decision maker (corresponding to the multi-objective optimization decision module), communication coordinator (corresponding to the communication scheduling module based on TDMA), and action executor (corresponding to the flight control and actuator module). Each role assumes a clear technical responsibility. Step 2: Uniquely identify and encode the tasks and drones, and use an improved genetic algorithm to generate a task role allocation table for drones. The improved genetic algorithm is constrained by "a single drone undertaking multiple tasks and multiple roles, and a single task covering all roles". By designing a fitness function that includes role reuse rate and task completion time, iterative optimization is performed to obtain the optimal allocation scheme, thereby maximizing the reuse of drone resources. Step 3: Determine sub-time slots based on UAV performance quantification parameters and quantity thresholds: Divide information collectors and information screeners into sub-time slots based on performance parameters such as UAV sensor type and data processing rate; Set the number of sub-time slots for communication coordinators based on the number of UAVs to ensure information sharing efficiency during global communication; Step 4: Divide each UAV into time slots according to the task role allocation table and set an operation cycle including eight time slots, specifically four communication and coordination time slots (located after other types of time slots), one information collector time slot, one information screener time slot, one decision maker time slot, and one action executor time slot, so that one UAV can play a role in multiple tasks. Step 5: Determine the time slot length based on the number of sub-time slots and the role running time threshold, using the formula: (in For the target time slot length, This is the redundancy coefficient. This represents the number of sub-time slots contained in this time slot. (where is the minimum running time threshold for the i-th sub-slot). The value range is 1.1-1.3, and it is dynamically adjusted according to the real-time requirements of the task. Step 6: Use Precise Time Protocol (PTP) or enhanced radio synchronization mechanism to achieve periodic synchronization of multiple UAVs, so that each UAV can switch time slots and execute tasks under the same time reference, avoiding task coordination chaos caused by time asynchrony.

2. The method for time slot allocation and role switching in a multi-task-oriented UAV swarm system according to claim 1, characterized in that, The technical responsibilities of each role include: The information collector's responsibility is to drive devices such as optical cameras, electromagnetic spectrum sensors, and infrared thermal imagers through sensor control modules to collect multi-dimensional raw data required to complete the task; The information screener's responsibility is to use the airborne edge computing unit to run feature extraction algorithms, outlier detection algorithms, and data fusion models to screen, identify, and extract effective information from the collected raw data. The coordinator's role is to ensure smooth collaboration between UAVs by transmitting and coordinating information in an orderly manner through the TDMA-based communication scheduling module. The decision maker's responsibility is to generate decision instructions such as drone movement paths, task priorities, and resource allocation based on the identified effective information and communication and collaboration results through the multi-objective optimization decision module. The executor's responsibility is to use the flight control and actuator module to execute specific tasks such as flight, secondary data acquisition, and material delivery according to decision instructions.

3. The method for time slot allocation and role switching in a multi-task-oriented UAV swarm system according to claim 1, characterized in that, The specific implementation of the improved genetic algorithm includes: Population initialization: Each individual corresponds to a set of task-role allocation schemes, and the encoding method adopts a two-dimensional matrix (rows represent drones, and columns represent task-role combinations); Fitness function: (in For character reuse rate, For the time limit for task completion, , These are the weighting coefficients. ,and ; Genetic operations: Selection is performed using roulette wheel selection, crossover is performed using single-point crossover, and mutation is performed by randomly swapping the task-role assignments of two individuals. The number of iterations is set to 50-100 generations, and the convergence condition is that the fitness function value fluctuates by less than 5% for 10 consecutive generations.

4. The method for time slot allocation and role switching in a multi-task-oriented UAV swarm system according to claim 1, characterized in that, The process of determining sub-time slots based on UAV performance quantification parameters includes: Information acquisition time slots: Sub-time slots are divided according to sensor type and sampling frequency, including optical image acquisition sub-time slots (sampling frequency ≥ 30fps, sub-time slot length ≥ 0.03s), electromagnetic spectrum acquisition sub-time slots (sampling bandwidth ≥ 100MHz, sub-time slot length ≥ 0.1s), and infrared image acquisition sub-time slots (temperature measurement accuracy ≤ ±0.5℃, sub-time slot length ≥ 0.05s). Information discriminator time slots: Sub-time slots are divided according to data processing complexity, including optical image processing sub-time slots (sub-time slot length ≥ 0.2s when image resolution ≥ 1920×1080), electromagnetic spectrum processing sub-time slots (sub-time slot length ≥ 0.3s when data volume ≥ 10MB), infrared image processing sub-time slots (sub-time slot length ≥ 0.07s when frame rate ≥ 15fps), and fusion processing sub-time slots (sub-time slot length ≥ 0.5s when fused data types ≥ 3).

5. The method for time slot allocation and role switching in a multi-task-oriented UAV swarm system according to claim 1, characterized in that, The process of determining sub-time slots based on the number of drones includes: Let the number of drones be m, then the number of global communication sub-time slots in the communication coordinator time slot is m, and the length of each sub-time slot is m. (in (Total length of time slots for communication coordinator), ensuring that each drone has its own dedicated sub-time slot for information transmission, and that the transmission duration meets the maximum data transmission requirements of a single drone (when the transmission rate is ≥10Mbps). ).

6. The method for time slot allocation and role switching in a multi-task-oriented UAV swarm system according to claim 1, characterized in that, The eight time slots are arranged in the following sequence: Information collector time slot → Communication coordinator time slot 1 (global communication) → Information screener time slot → Communication coordinator time slot 2 (global communication) → Decision maker time slot → Communication coordinator time slot 3 (local communication) → Action executor time slot → Communication coordinator time slot 4 (global communication).

7. The method for time slot allocation and role switching in a multi-task-oriented UAV swarm system according to claim 6, characterized in that, The specific implementation of the communication coordinator time slot includes: Global communication: The processing results (collected data, identification information, execution status) of the current time slot are sent to all drones via broadcast communication mode. The communication protocol is UDP, and the transmission delay is ≤50ms. Local communication: Decision commands are sent to the drones designated in the task role allocation table via point-to-point communication mode. The TCP protocol is used to ensure the reliability of command transmission, with a packet loss rate of ≤0.1%.

8. The method for time slot allocation and role switching in a multi-task-oriented UAV swarm system according to claim 1, characterized in that, The minimum running time threshold of the sub-slot Experimental testing determined that, under standard environmental conditions (temperature 0-40℃, wind speed ≤5m / s), 100 repeated tests were conducted on typical tasks for each role, and the maximum runtime within the 95% confidence interval was taken as the baseline. .

9. The method for time slot allocation and role switching in a multi-task-oriented UAV swarm system according to claim 1, characterized in that, The specific implementation of the periodic synchronization includes: If the PTP protocol is used: designate one UAV with the best performance as the master clock. The master clock periodically sends synchronization messages (sending period = 0.1s). The synchronization message contains a timestamp (accuracy ≤ 1μs). After receiving the message from the clock UAV, the clock deviation is calculated and the master clock is adjusted. The synchronization error is ≤ 10μs. If an enhanced radio synchronization mechanism is adopted: the master UAV sends a radio signal of a specific frequency (433MHz) and encoding (Manchester encoding), calculates the time difference from the UAV's time of arrival (TOA), and calibrates its own mission cycle based on the master UAV's signal, with a synchronization error ≤20μs.

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