Unmanned aerial vehicle group capture type rocket sublevel air recovery method and equipment and storage medium

Abstract

The invention belongs to the technical field of rocket recovery, and discloses an unmanned aerial vehicle group capture type rocket sublevel air recovery method and device and a storage medium. The method comprises the following steps: an unmanned aerial vehicle group hovers in the air, executes global search, and covers a whole rocket sub-level recovery task area; adjusting flight parameters of the unmanned aerial vehicle in real time according to ephemeris and attitude information of the rocket sub-level, accurately locking a target, and encircling at an optimal capture point; the flight state of the unmanned aerial vehicle is changed from a circling mode to a diving mode, and the unmanned aerial vehicle dives rapidly to approach the rocket sublevel to form a precise capture trajectory; path planning and action management are carried out, and a capture task is completed under the condition of minimum energy consumption; and performing performance evaluation on the whole recovery process, and analyzing indexes such as capture percentage, energy consumption and time efficiency of the task. According to the invention, the dependence on a specific landing area is reduced, so that the recovery of the rocket sublevel can be carried out in a wider airspace, and the flexibility and applicability of operation are improved.

Description

Technical Field

[0001] The present invention belongs to the field of rocket sub-stage aerial recovery, and specifically relates to a method, equipment and storage medium for drone swarm capture-type rocket sub-stage aerial recovery. Background Art

[0002] The drone swarm capture rocket sub-stage aerial recovery method has significant advantages over traditional recovery methods, such as vertical landing and sea recovery. Traditional methods usually rely on complex navigation and control systems, are limited to specific landing areas, are very sensitive to environmental conditions, and are easily affected by bad weather. In addition, the vertical recovery of rocket sub-stages is susceptible to fatigue of the landing leg structure, and there have been cases of recovery failures due to this. Therefore, finding a way to replace or assist the vertical recovery of rocket sub-stages is of great significance to ensure the safety, reliability and economy of rocket sub-stage recovery. Summary of the invention

[0003] In order to solve the existing problems, the present invention proposes a method, device and storage medium for aerial recovery of rocket sub-stages captured by drone swarms. The present invention uses the falcon optimization algorithm, the drone swarms efficiently collaborate, optimize the capture path and strategy, and can significantly improve the recovery success rate and reduce costs. This innovative method not only provides higher flexibility, can perform tasks in a variety of environments, and respond to the dynamic changes of rocket sub-stages in real time. At the same time, it also reduces the dependence on dedicated infrastructure, promotes the development of rocket reusable technology, and has broad application prospects.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for recovering rocket sub-stages in the air by capturing them by drone swarms, comprising the following steps:

[0005] Step 1: The drone swarm hovers in the air and performs a global search using the Falcon optimization algorithm. The search area covers the entire rocket sub-stage recovery mission area and monitors the trajectory and position of the rocket sub-stage in real time.

[0006] Step 2: During the recovery process, the flight parameters of the drone are adjusted in real time according to the ephemeris and attitude information of the rocket sub-stage, the target is accurately locked, and the target is surrounded at the best capture point to form an effective collaborative pursuit of the rocket sub-stage;

[0007] Step 3: When the rocket stage enters the capture range of the drone swarm, the Falcon Optimization Algorithm is used to change the flight state of the drone from "hovering" to "diving" mode; the drone is controlled to dive close to the rocket stage to form a precise capture trajectory; at this time, the local search of the Falcon Optimization Algorithm is used to plan the path and manage the action of the drone swarm, completing the capture task with minimal energy consumption;

[0008] Step 4: Collect data to analyze the capture success rate, energy consumption, and time efficiency indicators, conduct performance evaluation of the entire recovery process, and continuously optimize the parameters of the Falcon optimization algorithm based on the evaluation results.

[0009] In step 3, during the recovery process, the capture altitude covers a range of 0.0001 m to 50 km.

[0010] The step 3 also includes the following steps: planning the paths of the remaining drones using the falcon optimization algorithm so that they return to the base.

[0011] In addition, the present invention also provides a terminal device, comprising: a processor and a memory communicatively connected to the processor;

[0012] The memory stores computer-executable instructions;

[0013] The processor executes the computer-executable instructions stored in the memory to implement the method for aerial recovery of rocket substages captured by a swarm of unmanned aerial vehicles as described in claim 1.

[0014] In addition, the present invention also provides a computer-readable storage medium, in which computer execution instructions are stored. When the computer execution instructions are executed by a processor, they are used to implement the method for aerial recovery of rocket substages captured by a drone swarm as described in claim 1.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] 1. Improved flexibility: The aerial capture method of the drone swarm reduces the dependence on a specific landing area, allowing the recovery of rocket sub-stages to be carried out in a wider airspace, increasing the flexibility and applicability of operations.

[0017] 2. Reduce costs: The present invention adopts a distributed drone swarm capture strategy, which significantly reduces the need for expensive infrastructure, thereby reducing the overall recovery cost and making rocket recovery more economical and feasible.

[0018] 3. Simplified control and navigation: Combined with the Falcon optimization algorithm, the path planning and collaborative control of the UAV are optimized, which simplifies the system design and operation process, improves reliability and reduces technical complexity.

[0019] 4. Accurate capture capability: Through the collaborative operation of a swarm of drones, the present invention achieves high-precision capture of rocket sub-stages, improves the success rate of recovery, and ensures the safety and reusability of the rocket.

[0020] 5. Increase the number of reuses: reduce the possibility of structural damage and increase the upper limit of reuse times. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic diagram of a process flow for aerial recovery of rocket substages captured by a swarm of drones provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0023] An embodiment of the present invention provides a method for aerial recovery of rocket sub-stages by capturing them using a drone swarm, which is based on a falcon optimization algorithm and achieves high-precision capture of rocket sub-stages. The falcon optimization algorithm is an intelligent optimization algorithm inspired by the predation behavior of falcons, and is divided into two stages: global exploration and local hunting. It can effectively balance global exploration and local development to find the optimal solution to the problem. In the present invention, the falcon optimization algorithm is used for task allocation and path planning of drone swarms. The drone swarm first determines the position and trajectory of the rocket sub-stage to be recovered through global exploration, and then enters the local search stage, dynamically adjusts the flight path, achieves precise capture and energy optimization, and effectively improves the aerial recovery efficiency of rocket sub-stages and the energy consumption management of drones. Specifically, Figure 1 As shown, an embodiment of the present invention provides a method for recovering rocket substages in the air by capturing them by drone swarms, comprising the following steps:

[0024] Step 1: The drone swarm begins to hover.

[0025] The drone swarm hovers in the air, performs global search, covers the entire rocket sub-stage recovery mission area, and monitors the trajectory and position of the rocket sub-stage in real time. Through global exploration using the Falcon optimization algorithm, the drone swarm determines the best flight path to maximize coverage and minimize energy consumption, adapting it to the data distribution and environmental conditions in a specific scenario, ensuring efficient recovery mission execution.

[0026] Step 2: Target Locking and Tracking

[0027] When some drones in the swarm discover the rocket sub-stage, they will lock the target through the Falcon optimization algorithm, dynamically adjust the flight path for tracking, and adjust the flight parameters of the drone in real time according to the ephemeris and attitude information of the rocket sub-stage to ensure accurate locking of the target. At the same time, other drones will surround the best capture point based on the shared location information, forming an effective collaborative pursuit.

[0028] Step 3: Drone swarm swoops in to capture

[0029] When the rocket stage enters the capture range of the drone swarm, the Falcon Optimization Algorithm changes the drone's flight state from "hovering" to "diving" mode. The drone quickly dives close to the rocket stage to form a precise capture trajectory. Through the Falcon Optimization Algorithm, the drone swarm captures the rocket stage synchronously at the optimal angle and speed. This stage focuses on local search and accurately adjusts the drone's capture path to achieve efficient capture. The capture altitude range can cover 0.0001m to 50km.

[0030] Step 4: Energy Optimization and Return

[0031] The Falcon Optimization Algorithm ensures reliability during the capture process while optimizing the energy consumption of the drone swarm. By performing path planning and motion management, it ensures that the drone swarm completes the capture mission with minimal energy consumption. After the rocket sub-stage is successfully captured, the remaining drones will rely on the shortest path planned by the algorithm to return to the base.

[0032] Step 5: Recycling performance evaluation and optimization feedback

[0033] The performance of the entire recovery process was evaluated, and indicators such as capture percentage, energy consumption, and time efficiency of this mission were analyzed. Based on the evaluation results, the parameters of the Falcon optimization algorithm and the workflow of the drone swarm were continuously optimized to provide quantitative data support for subsequent mission improvements.

[0034] Specifically, in this embodiment, the falcon optimization algorithm includes the following input parameters:

[0035] 1. Mission area coordinates: A

[0036] 2. Number of drones: N

[0037] 3. Capture height range: H

[0038] 4. Maximum flight speed: V

[0039] 5. Battery life: t

[0040] 6. Rocket stage estimated trajectory: Trocket

[0041] 7. Wind speed: W

[0042] 8. Temperature: T

[0043] 9. Air pressure: P

[0044] 10. Energy consumption model: E

[0045] 11. Safe zone radius: R

[0046] 12.Data update frequency: F

[0047] 13. Path planning algorithm parameters: B

[0048] 14. Capture timing threshold: C

[0049] 15. Drone initial state: S

[0050] 16. Target detection accuracy: D

[0051] 17. Communication delay: T comm

[0052] 18. Effective range of capture device: R capture

[0053] 19. Capture success rate threshold: P success

[0054] 20. Flight mission priority: P priority

[0055] The following is part of the implementation source code:

[0056] import numpy as np

[0057] import threading

[0058] import time

[0059] import random

[0060]

[0061]

[0062] distance_to_rocket=np.linalg.norm(drone.position-self.rocket_position)

[0063] # Check if it is within the capture range

[0064] if distance_to_rocket<=self.capture_range:

[0065] print(f"Drone{drone.drone_id}is within capture range.Attemptingcapture...")

[0066] lon_min,lon_max,lat_min,lat_max=self.area_coordinates

[0067] random_position = np.array([random.uniform(lon_min,lon_max),#randomly generate longitude

[0068] random.uniform(lat_min,lat_max),#randomly generate latitude

[0069] random.uniform(0,self.drones[0].max_altitude)])#Randomly generate height

[0070] return random_position

[0071] def return_to_base(self):

[0072] """The drone returns to base after completing the capture"""

[0073] for drone in self.drones:

[0074] print(f"Drone{drone.drone_id}returning to base from position{drone.position}.")

[0075] # Return to base logic

[0076] drone.fly_to(np.array([0.0,0.0,0.0]))#Return to the origin

[0077] # Initialize example parameters

[0078] drone_swarm=DroneSwarmRecovery(

[0079] area_coordinates = (10.0, 20.0, 30.0, 40.0), #define the task area

[0080] num_drones=5,#Number of drones

[0081] max_altitude=3000,#Maximum flight altitude

[0082] max_speed=150,#maximum flight speed

[0083] endurance_time=120,#endurance time

[0084] rocket_position = np.array([15.0,35.0,1000.0]), #Current position of rocket stage

[0085] capture_range=50,#capture range

[0086]

[0087] In an exemplary embodiment, the present invention further provides a terminal device. The terminal device includes: a processor and a memory in communication with the processor, the memory storing computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the above-mentioned drone swarm capture rocket sub-stage aerial recovery method.

[0088] In an exemplary embodiment, a computer-readable storage medium is also provided, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the method for aerial recovery of rocket substages captured by a swarm of drones as described above.

[0089] The feasibility of the present invention has been demonstrated through a series of simulations.

[0090] The present invention uses numerical simulation technology to carry out simulation research and verifies the effectiveness and feasibility of the invention.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for recovering rocket substages in the air by capturing them by drone swarms, characterized in that: The following steps are involved: Step 1: The drone swarm hovers in the air and performs a global search using the Falcon optimization algorithm. The search area covers the entire rocket sub-stage recovery mission area and monitors the trajectory and position of the rocket sub-stage in real time. Step 2: During the recovery process, the flight parameters of the drone are adjusted in real time according to the ephemeris and attitude information of the rocket sub-stage, the target is accurately locked, and the target is surrounded at the best capture point to form an effective collaborative pursuit of the rocket sub-stage; Step 3: When the rocket sub-stage enters the capture range of the drone swarm, the flight state of the drone is changed from "hovering" to "diving" mode through the Falcon Optimization Algorithm; the drone is controlled to dive close to the rocket sub-stage to form a precise capture trajectory; at this time, the local search of the Falcon Optimization Algorithm is used to plan the path and manage the action of the drone swarm, completing the capture task with minimal energy consumption; Step 4: Collect data to analyze the capture success rate, energy consumption, and time efficiency indicators, conduct performance evaluation of the entire recovery process, and continuously optimize the parameters of the Falcon optimization algorithm based on the evaluation results.

2. The method for recovering rocket substages captured by drone swarm according to claim 1, characterized in that: In step 3, during the recovery process, the capture altitude covers a range of 0.0001 m to 50 km.

3. The method for recovering rocket sub-stages captured by drone swarm according to claim 1, characterized in that: The step 3 also includes the following steps: planning the paths of the remaining drones using the falcon optimization algorithm so that they return to the base.

4. A terminal device, characterized in that: include: A processor and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method for aerial recovery of rocket substages captured by a swarm of unmanned aerial vehicles as described in claim 1.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement a method for aerial recovery of rocket substages captured by a swarm of unmanned aerial vehicles as described in claim 1.

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