Path planning method for joint search and rescue between UAVs and manned aircraft under airspace stratification

Abstract

The present invention discloses a method for joint search and rescue path planning for unmanned aerial vehicles (UAVs) and manned aircraft under airspace stratification, belonging to the field of UAV technology. The method comprises: performing airspace stratification according to a target search and rescue area and establishing an airspace stratification model; performing joint search and rescue path planning for multiple UAVs and multiple manned aircraft based on the airspace stratification model; performing path conflict simulation optimization for the multiple UAVs and the multiple manned aircraft based on the joint search and rescue path planning results; encrypting and sending the optimized search and rescue path planning results to the multiple UAVs and the multiple manned aircraft, and the multiple UAVs and the multiple manned aircraft perform search and rescue in the target search and rescue area according to the optimized search and rescue path planning results. This application solves the technical problem in the prior art of the lack of systematic path planning and airspace management during joint search and rescue between UAVs and manned aircraft, resulting in low search and rescue efficiency and high risk of aircraft conflicts.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a method for joint search and rescue path planning between UAVs and manned aircraft under airspace stratification. Background Art

[0002] In recent years, drone and manned aircraft technology has rapidly developed and is widely used in a variety of fields, including agriculture, surveillance, and logistics. In the rescue sector, the combination of drones and manned aircraft has demonstrated unique advantages. Drones, with their flexibility, low cost, and wide coverage, can quickly reach remote or dangerous areas, while manned aircraft offer greater payload capacity and long-duration flight capabilities. Therefore, in emergency rescue missions in complex terrain or extreme weather, the coordinated operation of drones and manned aircraft can improve search and rescue efficiency and success rates, minimizing casualties and property damage.

[0003] However, the existing UAV and manned aircraft collaborative search and rescue methods still have many shortcomings. Traditional search and rescue models often rely on manual scheduling and lack systematic path planning and airspace management, resulting in insufficient coverage of the search and rescue area and even the risk of conflict between aircraft. At the same time, due to the limited autonomous navigation capabilities of UAVs, existing search and rescue paths are mostly based on static planning, which makes it difficult to cope with sudden terrain changes or dynamic rescue needs. In addition, there is a lack of efficient information transmission methods between UAVs and manned aircraft, resulting in poor coordination in complex search and rescue environments. Therefore, an airspace-layered UAV and manned aircraft joint search and rescue path planning method is proposed to solve the technical problems in the existing technology of the lack of systematic path planning and airspace management in the joint search and rescue of UAVs and manned aircraft, resulting in low search and rescue efficiency and high risk of aircraft conflict. Summary of the Invention

[0004] This application provides a joint search and rescue path planning method for UAVs and manned aircraft under airspace stratification, aiming to solve the technical problems in the existing technology of lack of systematic path planning and airspace management during joint search and rescue between UAVs and manned aircraft, resulting in low search and rescue efficiency and high risk of aircraft conflict.

[0005] In view of the above problems, this application provides a joint search and rescue path planning method for UAVs and manned aircraft under airspace stratification.

[0006] The present application provides a method for joint search and rescue path planning for UAVs and manned aircraft under airspace stratification, the method comprising performing airspace stratification according to a target search and rescue area and establishing an airspace stratification model; performing joint search and rescue path planning for multiple UAVs and multiple manned aircraft based on the airspace stratification model to obtain a joint search and rescue path planning result; performing path conflict simulation optimization for the multiple UAVs and the multiple manned aircraft based on the joint search and rescue path planning result to obtain an optimized search and rescue path planning result; encrypting the optimized search and rescue path planning result and sending it to the multiple UAVs and the multiple manned aircraft, and the multiple UAVs and the multiple manned aircraft perform search and rescue in the target search and rescue area according to the optimized search and rescue path planning result.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] By employing a technical solution for joint search and rescue path planning between drones and manned aircraft based on an airspace hierarchical model, this solution addresses the existing technical issues of low search and rescue efficiency and a high risk of aircraft conflicts caused by the lack of systematic path planning and airspace management in joint search and rescue operations between drones and manned aircraft. This solution achieves the technical effect of improving search and rescue efficiency and success rates. Through clear airspace division and task allocation, this solution enables effective collaboration between multiple aircraft types, reduces the probability of path conflicts, ensures the safety and effectiveness of search and rescue operations, and provides a more efficient solution for emergency rescue scenarios.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A flowchart of a method for joint search and rescue path planning between UAVs and manned aircraft under airspace stratification is provided for an embodiment of the present application.

[0011] Figure 2 The present application provides an embodiment of a process flow diagram for obtaining a joint search and rescue path planning result in a method for joint search and rescue path planning between a UAV and a manned aircraft under airspace layering. DETAILED DESCRIPTION

[0012] The overall idea of ​​the technical solution provided by this application is as follows:

[0013] The embodiments of the present application provide a method for joint search and rescue path planning for UAVs and manned aircraft under airspace stratification. By constructing an airspace stratification model, joint search and rescue path planning for UAVs and manned aircraft is achieved. First, the airspace is divided according to the characteristics of the target search and rescue area, and a model is established to optimize the allocation of mission areas. Then, the model is used for path planning to ensure coordinated operations between multiple aircraft types and reduce path conflicts. Finally, the optimized path planning results are encrypted and sent to each aircraft, ensuring the efficient and safe execution of search and rescue missions in complex environments, thereby improving the overall search and rescue effect.

[0014] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced in conjunction with the drawings in the specification.

[0015] Examples, such as Figure 1 As shown, the embodiment of the present application provides a method for joint search and rescue path planning of UAVs and manned aircraft under airspace layering, the method comprising:

[0016] Step S100: Perform airspace stratification according to the target search and rescue area and establish an airspace stratification model.

[0017] Specifically, the target search and rescue area refers to the specific area where drones and manned aircraft need to perform search and rescue missions, which contains complex terrain, buildings or other obstacles. The target search and rescue area will be determined according to the rescue needs, such as mountainous areas, sea areas or urban buildings. Airspace stratification refers to dividing the airspace of the target area into multiple levels according to altitude, regional characteristics or mission requirements. Each level can be covered by different types of aircraft or different task divisions. The purpose of airspace stratification is to avoid conflicts between aircraft and improve the efficiency of airspace use. The airspace stratification model refers to a structured model after airspace stratification, which contains information such as the area range, altitude range, and task allocation between levels after stratification, providing data support for subsequent path planning. This model is used in search and rescue missions to clearly define the responsibilities and rules of each layer of airspace.

[0018] To build an airspace hierarchical model, the first step is to collect airspace and regional data for the target search and rescue area, including flight restrictions, altitude ranges, and terrain obstacles, to form a regional base dataset. This data can be collected using GPS, remote sensing, or a geographic information system (GIS), followed by data cleaning to remove redundant information. Based on this cleaned regional dataset, a three-dimensional model of the target search and rescue area is generated using modeling tools (such as MATLAB or ArcGIS). This model intuitively reflects the geographic characteristics and airspace information of the search and rescue area.

[0019] Next, the regional model is divided into airspace layers based on preset airspace division rules. Airspace division rules are usually set according to the needs of the rescue mission. For example, the low-altitude layer is used for low-speed searches by drones, the mid-altitude layer is used for manned aircraft searches that quickly cover the area, and the high-altitude layer is used for data relay and signal transmission. Taking maritime search and rescue as an example, the low-altitude layer is responsible for detailed searches for floating objects by drones, the mid-altitude layer is patrolled by helicopters, and the high-altitude layer uses drones to relay image information. After the layering is completed, the resulting airspace layer model includes the altitude range and task assignments of each airspace layer, laying the foundation for joint search and rescue path planning.

[0020] This step, by stratifying the airspace of the target search and rescue area and establishing an airspace stratification model, effectively coordinates the allocation of tasks between drones and manned aircraft, reduces flight conflicts, and improves search and rescue coverage and response speed. In practical applications, this airspace stratification model not only improves airspace utilization efficiency but also enhances dynamic scheduling capabilities in complex environments, significantly increasing the success rate of joint search and rescue missions.

[0021] Step S200: Based on the airspace layering model, joint search and rescue path planning is performed on multiple UAVs and multiple manned aircraft to obtain a joint search and rescue path planning result.

[0022] Specifically, joint search and rescue path planning involves integrated planning of routes for drones and manned aircraft based on a hierarchical airspace model to maximize coverage of the search and rescue area and minimize path conflicts. The resulting flight paths assign drones and manned aircraft tasks at different altitudes and locations, enabling each aircraft to efficiently complete the search and rescue mission. The joint search and rescue path planning results are the final output of the path planning, including detailed flight routes and time schedules for drones and manned aircraft, ensuring efficient coordination among the aircraft and preventing path conflicts.

[0023] When planning joint search and rescue routes, the tasks for UAVs and manned aircraft are first divided based on an airspace stratification model. Since the airspace stratification model already defines the target search and rescue area at different altitude levels and mission areas, the route planning process can rationally allocate mission areas to different aircraft based on this hierarchical information. Generally, UAVs are suited for low-altitude, precision search missions, such as close-up observation of building ruins or complex terrain, while manned aircraft are suited for wide-area patrol missions at medium and high altitudes.

[0024] Specifically, the mission area is first allocated using a planning algorithm (such as the A algorithm or the Dijkstra algorithm) based on the mission requirements and flight capabilities of each aircraft (such as endurance, flight speed, obstacle avoidance, etc.). For example, the A algorithm can be used to calculate the shortest path for a drone in complex terrain, while the Dijkstra algorithm is more suitable for planning long-distance flight routes for manned aircraft in open areas. To improve the adaptability of path planning, deep learning models can also be used to train historical search and rescue data to optimize the path generation process. In mountain search and rescue, drones can be assigned to low-altitude areas to search hillsides and forests, while manned aircraft fly at medium and high altitudes to quickly patrol the entire mountain area.

[0025] After completing the path planning for each UAV and manned aircraft, these paths are fused to generate the final joint search and rescue path planning result. This fusion result includes detailed information such as the specific flight route, takeoff and landing times, and flight altitude for each UAV and manned aircraft, ensuring that there are no spatial or temporal conflicts between them. Path conflict detection can be further performed using simulation software (such as MATLAB or the ROS simulation platform) to ensure that UAVs and manned aircraft do not interfere with each other during actual flight.

[0026] Through joint search and rescue path planning based on an airspace hierarchical model, UAVs and manned aircraft can collaborate within the same search and rescue area, significantly improving the coverage efficiency of search and rescue missions. This path planning method reduces the risk of conflicts between aircraft within the airspace through reasonable path allocation and optimizes the time and space utilization of the search and rescue system. Ultimately, this planning method significantly improves the success rate and completion speed of search and rescue missions, providing safe and efficient technical support for emergency search and rescue in complex environments.

[0027] Step S300: performing path conflict simulation optimization on the multiple UAVs and the multiple manned aircraft based on the joint search and rescue path planning result to obtain an optimized search and rescue path planning result.

[0028] Specifically, conflict simulation and optimization involves simulating aircraft paths to detect and eliminate potential conflicts between aircraft, such as intersections and overlaps. This optimization ensures that each aircraft's flight path is safer and more efficient. The optimized search and rescue path planning result, after conflict simulation and optimization, includes the specific routes and timings for each aircraft in the mission, ensuring they avoid interference or conflicts.

[0029] First, the joint search and rescue path planning results are imported into a simulation platform (such as MATLAB, ROS, or AnyLogic) for path conflict detection. By modeling the flight characteristics, paths, and airspace layering of each drone and manned aircraft, the simulation platform can accurately simulate the intersection, overlap, or mutual interference that may occur between different aircraft during flight. For example, if the paths of two drones overlap significantly at a certain point, the simulation will detect the conflict point and identify its location and time.

[0030] Once the conflict detection is completed, the path will be optimized based on the detection results. During the optimization process, heuristic algorithms (such as genetic algorithms, particle swarm optimization algorithms, etc.) or path avoidance algorithms (such as collision avoidance algorithms) can be used to adjust the path. On the premise of keeping the general direction and target area of ​​the original mission path of the aircraft unchanged, the path conflict is resolved by adjusting the flight altitude, detour or time sequence. For example, if the paths of drone A and manned aircraft B overlap in a certain airspace, the optimization algorithm will guide drone A to slightly increase its flight altitude before the conflict point, or detour at this location to ensure that the two do not meet at the same airspace level.

[0031] Furthermore, the path optimization process considers the characteristics of aircraft. For example, manned aircraft prioritize maintaining straight flight to conserve fuel, while drones can flexibly adjust their routes. For example, in a valley search and rescue mission, if a drone is searching the valley at low altitude while a manned aircraft is flying high above, simulation results indicate a high likelihood of conflict. The optimization solution might schedule the drone to enter the valley's low altitude area after the manned aircraft has passed, or guide the drone to enter from the side to avoid the manned aircraft's route.

[0032] Through path conflict simulation and optimization, joint search and rescue path planning results can be further improved, not only eliminating flight conflicts between drones and manned aircraft, but also effectively improving the safety and efficiency of search and rescue operations. The optimized path planning makes the search and rescue process smoother and more coordinated, facilitating the rapid and interference-free completion of search and rescue missions. This conflict optimization technology is particularly important in complex, multi-layered search and rescue environments, ensuring path safety when multiple aircraft are working together, improving search and rescue success rates and time efficiency.

[0033] Step S400: Encrypting the optimized search and rescue path planning result and sending it to the multiple UAVs and the multiple manned aircraft, and the multiple UAVs and the multiple manned aircraft perform search and rescue in the target search and rescue area according to the optimized search and rescue path planning result.

[0034] Specifically, encrypted transmission means that in order to ensure the security of communication, the optimized search and rescue path planning results will be encrypted and transmitted to each aircraft through a secure communication link to prevent the path information from being tampered with or intercepted.

[0035] First, the optimized search and rescue path planning results must be encrypted to prevent malicious attacks or interception during data transmission. Common encryption methods include symmetric encryption (such as AES) or asymmetric encryption (such as RSA). Taking AES as an example, the encryption algorithm encrypts the path data, generating encrypted ciphertext that can only be decrypted and retrieved by the aircraft holding the key. For example, a unique encryption key can be assigned to each drone and manned aircraft, and the optimized path data can be sent encrypted to the receiving module of each aircraft.

[0036] Next, the optimized path planning results are sent from the ground command center to each aircraft in encrypted form through wireless communication modules (such as LoRa, 4G / 5G communication modules, etc.) equipped on each aircraft. To ensure the stability and efficiency of the transmission process, communication protocols (such as TCP / IP and MQTT) are used to ensure the complete transmission of data. Taking mountain search and rescue as an example, the signal strength in mountainous areas is uneven. The path planning results can be transmitted in fragments through the 4G / 5G network, and stable communication links are prioritized in areas with poor signals to ensure that the data successfully reaches each aircraft.

[0037] After receiving and decrypting the path data, the drone and manned aircraft each follow the planned path to enter the target area and conduct search and rescue missions. Drones typically conduct detailed area scans at lower altitudes, such as searching for missing persons and detecting obstacles, while manned aircraft cruise at higher altitudes for wide-area coverage, ensuring support over a wider area. For example, in a maritime search and rescue mission, drones would fly low along the sea surface to search for floating objects and life signals, while helicopters would observe the sea surface from above, enabling rapid response to unexpected discoveries.

[0038] This step significantly improves the safety and efficiency of the entire search and rescue process through the encrypted transmission and precise execution of optimized search and rescue path planning results. This encryption ensures the privacy and security of the search and rescue path, protecting aircraft from external interference or path leaks. Each aircraft can seamlessly receive and strictly execute the planned path, avoiding path deviations, improving the accuracy of search and rescue area coverage and the search and rescue success rate, significantly enhancing the overall efficiency and safety of the search and rescue mission.

[0039] Furthermore, airspace stratification is performed according to the target search and rescue area, and an airspace stratification model is established, including: collecting airspace parameters and geographical parameters of the target search and rescue area to obtain a regional basic data set; modeling is performed based on the regional basic data set to obtain a target search and rescue area model; and airspace division is performed on the target search and rescue area model according to predetermined airspace division rules to generate the airspace stratification model.

[0040] Specifically, airspace parameters describe the parameters of the flight conditions in the target search and rescue area, such as flight altitude, airspace restrictions, weather conditions, etc. These parameters are used to evaluate whether the aircraft can fly safely and effectively in the area. Regional parameters describe the parameters of the geographical characteristics of the target search and rescue area, such as terrain, landforms, obstacles and surface structures. These parameters affect the path planning of the aircraft and the execution of the search and rescue mission. The regional basic data set refers to the data set obtained by collecting airspace parameters and regional parameters, which contains the basic information of the target search and rescue area and serves as a reference for subsequent modeling and path planning. The airspace division rules are rules used to stratify the airspace according to search and rescue needs, usually including altitude division, task allocation basis, etc., to reasonably allocate tasks of different aircraft at different airspace levels.

[0041] When establishing an airspace layering model, the airspace and geographical parameters of the target search and rescue area must first be collected to form a regional basic data set. Airspace parameters include flight altitude, airspace restrictions, and weather conditions, which can be obtained through methods such as weather radar and satellite remote sensing data. For example, search and rescue missions in mountainous areas require understanding wind speeds and cloud cover at different altitudes to ensure that drones can safely fly in the low-altitude layer. At the same time, geographical parameters such as terrain undulations and obstacle distribution can be collected through methods such as geographic information systems (GIS) and laser radar (LiDAR) scanning. After data cleaning and preprocessing, this information forms a consistent regional basic data set, providing accurate data support for subsequent modeling.

[0042] Based on this dataset, a modeling tool (such as MATLAB or ArcGIS) is used to construct a target search and rescue area model. This model reflects the three-dimensional geographic structure, climate, and ground features of the target search and rescue area. For example, the 3D terrain model generated by ArcGIS can clearly show the ups and downs of mountainous areas, which helps plan the search path of aircraft in low-altitude areas.

[0043] After modeling is completed, the target search and rescue area model is divided according to the preset airspace division rules to generate an airspace layered model. The airspace division rules can be set according to mission requirements. For example, it can be divided into three layers: low altitude, medium altitude, and high altitude according to the type of aircraft and mission requirements. Taking maritime search and rescue as an example, the low altitude layer can be used by drones for low-altitude precision searches, the medium altitude layer can be used by manned helicopters for large-scale searches, and the high altitude layer is used for signal relay and information transmission. After the division is completed, the airspace layered model contains specific information such as the altitude range and task allocation of each layer, providing a clear structured framework for joint search and rescue path planning.

[0044] By establishing a hierarchical airspace model, hierarchical management of target search and rescue areas is achieved, enabling drones and manned aircraft to work efficiently together in different airspace layers. This hierarchical model not only effectively reduces path conflicts between aircraft but also improves the comprehensive coverage and resource utilization of search and rescue missions. The hierarchical airspace model enables the search and rescue system to dynamically allocate tasks, improving the response speed and accuracy of search and rescue missions, and providing greater reliability and safety for search and rescue operations.

[0045] Furthermore, the airspace parameters and regional parameters of the target search and rescue area are collected to obtain a regional basic data set, including: collecting the airspace parameters of the target search and rescue area to obtain airspace parameter collection results; collecting the regional parameters of the target search and rescue area to generate regional parameter collection results; performing data cleaning based on the airspace parameter collection results and the regional parameter collection results to generate the regional basic data set.

[0046] Specifically, airspace parameter collection results refer to the specific data on airspace parameters obtained through collection methods, such as flight altitude range and wind speed. This data reflects flight conditions in the target area and guides aircraft operations within that area. Regional parameter collection results refer to the specific data on regional parameters obtained through collection methods, displaying the geographic characteristics of the target area and providing a topographic reference for path planning. Data cleaning involves filtering, denoising, processing, and converting the collected airspace and regional parameter data to remove redundant and erroneous data and ensure data accuracy and consistency.

[0047] When collecting the airspace and regional parameters of the target search and rescue area, it is first necessary to obtain the airspace parameters and regional parameters respectively. For airspace parameters, tools such as weather radar, satellite remote sensing, and air traffic control systems can be used to obtain flight altitude ranges and meteorological information (such as wind speed, cloud cover, and visibility). For example, in mountain search and rescue missions, the collected airspace parameters include cloud height, low-altitude wind speed, and visibility restrictions in the area to ensure that the drone can fly stably within the appropriate airspace.

[0048] For regional parameters, data on terrain, landforms, and obstacle distribution can be collected using geographic information systems (GIS), laser radar (LiDAR), and remote sensing technologies. For example, in urban search and rescue missions, LiDAR scans can capture regional parameters such as the three-dimensional position of buildings and ground relief, helping to identify obstacles that drones need to avoid or locations that require focused search. These collected data are divided into airspace parameter collection and regional parameter collection. These raw data often contain noise, redundant information, or outliers.

[0049] Next, the airspace and regional collection results are cleaned, and data processing software (such as Pandas and NumPy libraries in Python, or dedicated data cleaning tools such as OpenRefine) is used to denoise the data, fill in missing data, and remove outliers. For example, if certain values ​​in the collected wind speed data are significantly higher or lower, which is an error during collection, the outliers need to be removed or corrected through data cleaning to ensure data accuracy. The cleaned data forms a regional basic data set, which contains structured airspace and regional information and becomes an accurate basis for subsequent path planning and model building.

[0050] By collecting airspace and geographical parameters and cleaning the data to form a regional basic dataset, we ensure the accuracy and completeness of the airspace and geographic information of the search and rescue area, providing solid data support for subsequent airspace stratification modeling. After data cleaning, the regional basic dataset is free of noise and erroneous information, thereby improving the accuracy of the airspace stratification model and ensuring the precision and effectiveness of the search and rescue mission.

[0051] Further, such as Figure 2 As shown, based on the airspace hierarchical model, joint search and rescue path planning is performed for multiple UAVs and multiple manned aircraft to obtain a joint search and rescue path planning result, including: based on the airspace hierarchical model, search and rescue mission areas are allocated for the multiple UAVs and the multiple manned aircraft to obtain UAV search and rescue mission areas and manned aircraft search and rescue mission areas; based on the UAV search and rescue mission areas, search and rescue path planning is performed for the multiple UAVs to generate UAV search and rescue path planning results; based on the manned aircraft search and rescue mission areas, search and rescue path planning is performed for the multiple manned aircraft to generate a manned aircraft search and rescue path planning result; and the UAV search and rescue path planning results and the manned aircraft search and rescue path planning results are integrated to generate the joint search and rescue path planning result.

[0052] Specifically, search and rescue mission area allocation refers to the division of search and rescue areas into separate areas for drones and manned aircraft based on an airspace stratification model, ensuring that each aircraft performs its mission in the appropriate area to maximize mission efficiency. A drone search and rescue mission area refers to a search and rescue area specifically for drones, typically located at lower altitudes or in difficult-to-access complex terrain to leverage the drone's flexibility and ability to conduct detailed low-altitude searches. A manned aircraft search and rescue mission area refers to a search and rescue area for manned aircraft, typically located at higher altitudes to cover a larger search and rescue area and enable rapid patrols.

[0053] First, mission areas for multiple drones and manned aircraft are assigned based on the airspace stratification model. Because the airspace stratification model divides the search and rescue airspace into different altitude and area levels, these areas can be appropriately allocated based on the aircraft's performance characteristics and mission requirements. Drones typically operate in lower-altitude areas, making them suitable for detailed searches in complex terrain or confined spaces.

[0054] Next, paths are planned for both the UAV and manned aircraft in the search and rescue mission area. For UAV path planning, a path planning algorithm (such as the A algorithm) combined with a deep learning model can be used to train the drone to select the optimal path in complex terrain. After the regional path planning is complete, a UAV search and rescue path planning result is generated, which includes the detailed flight path, flight altitude, and search strategy for each drone in its mission area.

[0055] At the same time, shortest path algorithms such as the Dijkstra algorithm are used to generate manned aircraft search and rescue path planning results. Manned aircraft are responsible for large-scale patrols in the mid- and high-altitude layers. Path planning takes into account the aircraft's speed and flight time to ensure that it can complete wide-area coverage in the shortest possible time.

[0056] Finally, the path planning results of the UAV and manned aircraft are fused to generate a joint search and rescue path plan. During the path fusion process, simulation software (such as MATLAB or ROS) can be used to detect spatial and temporal conflicts in the paths, ensuring that the UAV and manned aircraft do not interfere at the intersection. This fusion result includes detailed flight paths, mission sequences, and altitude levels for the UAV and manned aircraft, enabling efficient collaboration within the search and rescue area.

[0057] This step, through joint search and rescue path planning, maximizes the unique capabilities of both drones and manned aircraft, achieving more efficient search and rescue area coverage and more detailed target searches. The allocation of mission areas and the fusion of paths for drones and manned aircraft enable the synchronization of search and rescue missions across multiple airspace levels, improving search and rescue speed and comprehensive coverage. The final joint search and rescue path planning effectively avoids path conflicts between aircraft, ensuring the efficiency, safety, and coordination of the search and rescue mission.

[0058] Furthermore, search and rescue paths are planned for the multiple drones based on the drone search and rescue mission area to generate drone search and rescue path planning results, including: collecting basic characteristic parameters of the multiple drones to obtain a drone characteristic data set; performing deep learning based on the drone search and rescue path planning set of the multiple drones to build a drone search and rescue path planning model; based on the drone search and rescue mission area and the drone characteristic data set, search and rescue paths are planned for the multiple drones according to the drone search and rescue path planning model to obtain the drone search and rescue path planning results.

[0059] Specifically, basic characteristic parameters describe a set of parameters that describe the performance of a drone, such as flight speed, maximum flight time, obstacle avoidance capability, and payload capacity. These parameters determine the drone's actual flight capabilities and mission execution effectiveness in search and rescue missions. The drone characteristic dataset is a dataset composed of the characteristic parameters of multiple drones. It contains performance data for all drones involved in search and rescue missions and is used to guide the path planning process. The drone search and rescue path planning set stores drone path planning data from historical search and rescue missions for training deep learning models. By learning from this path data, the model can optimize the drone's path planning results for different missions. The drone search and rescue path planning model is based on a model trained through deep learning and is used to predict and optimize the drone's path selection, ensuring that the drone can efficiently perform its mission within the search and rescue area.

[0060] First, we need to collect basic characteristic parameters for each drone, such as flight speed, range, and obstacle avoidance sensor performance, to form a drone characteristic dataset. For example, in mountain search and rescue missions, some drones have long flight times and are suitable for long-distance missions, while others are equipped with sophisticated obstacle avoidance sensors and are suitable for complex terrain. Therefore, using this drone characteristic dataset, we can develop customized path planning solutions based on the performance characteristics of different drones.

[0061] Next, a deep learning model is trained using a set of drone search and rescue path planning data to build a drone search and rescue path planning model. This set of path planning data typically contains optimal drone paths from past search and rescue missions under various terrain, weather, and airspace conditions. Deep learning frameworks such as TensorFlow and PyTorch can be used to train this set of plans, enabling the model to automatically identify and generate optimal paths under similar mission conditions.

[0062] Finally, based on the UAVs' mission area and characteristic dataset, the path planning model is applied to actual search and rescue path planning, generating a UAV search and rescue path planning result. This result includes each UAV's specific flight path, altitude, obstacle avoidance strategy, and time schedule within the mission area. The planning model predicts and generates a path based on the UAV's characteristic data and the terrain characteristics of the search and rescue area. For example, in a valley search and rescue mission, the path planning result will enable UAVs with precise obstacle avoidance capabilities to fly low along the valley, while long-endurance UAVs will cruise long distances outside the valley.

[0063] This step utilizes a drone-specific dataset and a deep learning path planning model to generate a search and rescue path planning result that is highly adaptable to the specific drone's performance and the complexity of the mission area. This path planning method optimizes the drone's search coverage and safety in complex search and rescue environments, improving its search and rescue efficiency and mission completion, making the entire search and rescue process more intelligent and efficient.

[0064] Furthermore, path conflict simulation optimization is performed on the multiple UAVs and the multiple manned aircraft based on the joint search and rescue path planning result to obtain an optimized search and rescue path planning result, including: modeling the multiple UAVs and the multiple manned aircraft to construct a human-machine integration model; based on the joint search and rescue path planning result, search and rescue simulation is performed according to the airspace layered model and the human-machine integration model to obtain a search and rescue simulation result; path conflict identification is performed based on the search and rescue simulation result to obtain a search and rescue path conflict identification result; path optimization is performed on the joint search and rescue path planning result according to the search and rescue path conflict identification result to generate the optimized search and rescue path planning result.

[0065] Specifically, the human-machine integration model refers to a model used to simulate the collaborative operation of UAVs and manned aircraft in the airspace. It combines the flight characteristics, communication features and mission requirements of UAVs and manned aircraft to achieve efficient coordination of multiple aircraft types in the same airspace. Search and rescue simulation refers to simulating the flight paths of various UAVs and manned aircraft in a simulation environment based on existing path planning and human-machine models, observing their interactive behaviors, and predicting path conflicts or mission interference. Path conflict identification refers to identifying the areas of intersection, overlap or conflict between the flight paths of UAVs and manned aircraft by analyzing the flight trajectories in the simulation to determine the path nodes that need to be optimized. Path optimization refers to replanning or fine-tuning the paths of some aircraft based on the results of path conflict identification to eliminate conflicts and optimize the efficiency of search and rescue missions.

[0066] First, models for both UAVs and manned aircraft need to be constructed to form a human-machine integration model. This model integrates the physical characteristics, flight constraints, and communication features of each aircraft. For example, UAVs have high maneuverability and are suitable for low-altitude missions, while manned aircraft typically have higher altitude restrictions and longer flight times, making them suitable for high-altitude missions. To build this human-machine integration model, professional simulation platforms such as MATLAB Simulink and Gazebo can be used to facilitate simulation and testing. Specifically, characteristic data (such as flight altitude, speed, and turning radius) for each UAV and manned aircraft is collected and then input into a simulation platform (such as MATLAB Simulink or Gazebo) to simulate the aircraft's flight behavior. Next, through system modeling and dynamic simulation techniques, the flight characteristics and path requirements of the UAV and manned aircraft are integrated, enabling collaborative simulation of the aircraft in multiple airspaces, providing support for subsequent search and rescue path planning.

[0067] Next, using the joint search and rescue path planning results and the airspace layering model, a search and rescue simulation was conducted using a human-machine integrated model. During the simulation, the path data of the UAV and manned aircraft were loaded into the simulation environment and monitored in real time through virtual operations. This simulation can detect intersections or overlapping paths between aircraft.

[0068] Based on the results of the search and rescue simulation, path conflict identification is performed to detect any path conflicts between aircraft, including areas of convergence, overlap, or potential collision. Identified conflict areas are further analyzed to determine specific optimization requirements. Each conflict area is marked with the specific time point, path overlap location, and conflict type, generating a search and rescue path conflict identification result.

[0069] Finally, based on the path conflict identification results, the joint search and rescue path planning results are optimized. The optimization algorithm uses particle swarm optimization or obstacle avoidance algorithms to replan the path based on the characteristics of each aircraft.

[0070] By implementing path conflict simulation and optimization, we can effectively avoid path conflicts between different aircraft during the search and rescue process, thereby improving the safety and efficiency of the overall search and rescue mission. The optimized search and rescue path planning results ensure coordinated cooperation between drones and manned aircraft, significantly reducing mission interruptions and efficiency reductions caused by path conflicts. This human-machine integrated path optimization technology is particularly important in complex terrain or airspace, providing strong support for rapid response and high coverage of search and rescue missions.

[0071] Furthermore, the multiple UAVs and the multiple manned aircraft conduct search and rescue in the target search and rescue area according to the optimized search and rescue path planning results, including: establishing search and rescue path planning grids for each man-machine based on the optimized search and rescue path planning results; performing real-time monitoring based on the multiple UAVs and the multiple manned aircraft to obtain real-time flight monitoring data of each man-machine; performing path deviation detection on the real-time flight monitoring data of each man-machine based on the search and rescue path planning grids for each man-machine, and generating a man-machine path deviation warning signal.

[0072] Specifically, the search and rescue path planning grid refers to dividing the target search and rescue area into grids to facilitate the refined management and allocation of search and rescue missions. Each grid area corresponds to a specific drone or manned aircraft to perform the mission, ensuring clear division of the mission area and uniform coverage. Real-time flight monitoring data refers to aircraft data collected in real time via wireless transmission, including the aircraft's current coordinates, speed, altitude and other status information, which is used to monitor whether it is executing the mission according to the planned route. Path deviation detection is used to identify the deviation between the aircraft's actual flight path and the planned path, ensuring that the aircraft follows the established route within the designated grid area. If the deviation exceeds the preset value, an early warning signal will be triggered.

[0073] First, based on the optimized search and rescue path planning results, the target search and rescue area is gridded to form a search and rescue path planning grid. This gridding process divides the large area into multiple smaller areas, and each grid is assigned to a specific UAV or manned aircraft to perform a mission.

[0074] Next, each drone and manned aircraft will be monitored in real time, using GPS and communication modules (such as 4G / 5G) configured on the aircraft to obtain real-time flight status data such as location, speed, and altitude. This data will be regularly transmitted to the command center to provide real-time information on the aircraft's execution status.

[0075] Path deviation detection is performed based on real-time collected flight data and the search and rescue path planning grid. Specifically, the actual flight data of each aircraft will be compared with the planned path. Once a deviation from the planned path is found and the deviation value exceeds a preset threshold (such as more than 10 meters), an offset warning signal will be automatically generated and the information will be fed back to the aircraft control end. For example, in maritime search and rescue, if the drone deviates from the route due to wind and waves, the offset warning signal will promptly remind the drone to make adjustments to avoid the creation of search and rescue blank areas.

[0076] Through path planning grids, real-time monitoring, and deviation detection, this process ensures the accuracy and real-time adjustment capabilities of each drone and manned aircraft during search and rescue missions. Deviation warning signals quickly respond to path deviations, enabling the aircraft to automatically adjust their flight paths or for the operator to remotely adjust their flight paths, ensuring efficient search and rescue operations, reducing coverage blind spots, and improving search and rescue success rates and response speeds.

[0077] Furthermore, a wireless communication module is included between the multiple drones and the multiple manned aircraft, and the wireless communication module is used for communication connection between the multiple drones and the multiple manned aircraft.

[0078] Specifically, wireless communication modules refer to the communication equipment used for data transmission between drones and manned aircraft. These typically include Wi-Fi, 4G / 5G, dedicated radios, and other modules, enabling real-time transmission of data, commands, and status information. Communication connectivity involves sharing data between multiple aircraft via wireless signals, enabling drones and manned aircraft to communicate mission status, location, speed, and other information for coordinated action.

[0079] Wireless communication modules are deployed between multiple drones and manned aircraft to build a real-time, stable communication network. This typically includes multiple communication technologies, such as 5G, LoRa, or satellite communications, to ensure stable signal transmission in various search and rescue scenarios. For example, in urban environments, high-bandwidth 5G modules can be used for fast data transmission; in areas with weaker networks, such as mountainous areas or at sea, satellite communications or LoRa can be used to ensure long-distance communication.

[0080] Establishing communication links requires ensuring that each aircraft can share critical data, such as location, flight status, and mission progress, in real time. This real-time data exchange enables drones and manned aircraft to collaborate on missions. For example, when a drone detects a target during a forest search and rescue operation and transmits its location, other aircraft can adjust their routes accordingly and quickly approach the area. Communication protocols such as MAVLink or RTPS are often used to standardize data transmission between devices, enabling compatible communication between different types of aircraft.

[0081] The use of wireless communication modules significantly improves the efficiency of collaboration between drones and manned aircraft, enabling rapid response and flexible adjustments among multiple aircraft types in complex search and rescue missions. Through real-time communication, search and rescue teams can rapidly share and process on-site information, reducing redundant searches and shortening response times, effectively increasing search and rescue coverage and success rates. This technology is particularly well-suited for high-risk search and rescue environments, such as those in complex terrain or at sea, enabling efficient data exchange and real-time decision-making.

[0082] In summary, the method for joint search and rescue path planning for UAVs and manned aircraft under airspace stratification provided by the embodiments of the present application has the following technical effects:

[0083] 1. Through airspace stratification and joint search and rescue path planning, the collaborative efficiency of drones and manned aircraft in search and rescue missions is optimized. Through clear task allocation and planning, path conflicts can be reduced in complex environments, resource utilization can be improved, and comprehensive coverage of search and rescue missions can be ensured. Ultimately, the success rate and speed of search and rescue are improved, and the uncertainty caused by human intervention is reduced.

[0084] 2. By collecting airspace and regional parameters of the target search and rescue area, an airspace stratification model is established to effectively identify the characteristics and risks of different airspaces. This model lays a scientific foundation for subsequent path planning, enabling search and rescue operations to be optimized for specific environmental conditions, improving the relevance and efficiency of search and rescue missions.

[0085] 3. Joint search and rescue path planning for multiple drones and manned aircraft enables the rational allocation of mission areas and optimal resource allocation. This approach ensures coordinated operations among different aircraft, reduces conflicts during mission execution, improves the efficiency of multi-aircraft collaboration, and ultimately enhances the overall effectiveness of search and rescue operations.

[0086] 4. Through path conflict simulation and optimization, potential conflicts between drones and manned aircraft can be promptly identified and resolved. This measure ensures that aircraft can safely perform their missions during search and rescue operations, reduces resource waste and accident risks caused by conflicts, and significantly improves the safety and effectiveness of search and rescue operations.

[0087] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0088] Furthermore, the terms "first" or "second" as described above may not only represent an order relationship but may also represent a specific concept and / or refer to the selection of multiple elements individually or collectively. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, if such modifications and variations fall within the scope of this application and its equivalents, this application is intended to include such modifications and variations.

Claims

1. A joint search and rescue path planning method for UAVs and manned aircraft under airspace stratification is characterized by: The method comprises: Conduct airspace stratification according to the target search and rescue area and establish an airspace stratification model; Based on the airspace hierarchical model, joint search and rescue path planning is performed for multiple UAVs and multiple manned aircraft to obtain a joint search and rescue path planning result; Performing path conflict simulation optimization on the multiple UAVs and the multiple manned aircraft based on the joint search and rescue path planning result to obtain an optimized search and rescue path planning result; Encrypting the optimized search and rescue path planning result and sending it to the multiple unmanned aerial vehicles and the multiple manned aircraft, so that the multiple unmanned aerial vehicles and the multiple manned aircraft perform search and rescue in the target search and rescue area according to the optimized search and rescue path planning result; The step of performing path conflict simulation optimization on the multiple UAVs and the multiple manned aircraft based on the joint search and rescue path planning result to obtain an optimized search and rescue path planning result includes: Perform modeling based on the multiple UAVs and the multiple manned aircraft to construct a human-machine integration model; Based on the joint search and rescue path planning result, performing a search and rescue simulation according to the airspace layered model and the human-machine integration model to obtain a search and rescue simulation result; Performing path conflict identification based on the search and rescue simulation result to obtain a search and rescue path conflict identification result; The joint search and rescue path planning result is optimized according to the search and rescue path conflict identification result to generate the optimized search and rescue path planning result.

2. The method according to claim 1, wherein Airspace stratification is performed according to the target search and rescue area, and an airspace stratification model is established, including: Collecting airspace parameters and geographical parameters of the target search and rescue area to obtain a regional basic data set; Modeling is performed based on the regional basic data set to obtain a target search and rescue area model; The target search and rescue area model is airspace divided according to a predetermined airspace division rule to generate the airspace hierarchical model.

3. The method according to claim 2, wherein Collect the airspace parameters and regional parameters of the target search and rescue area to obtain a regional basic data set, including: Collecting airspace parameters of the target search and rescue area to obtain airspace parameter collection results; Collecting geographical parameters of the target search and rescue area and generating geographical parameter collection results; Data cleaning is performed based on the spatial parameter collection results and the regional parameter collection results to generate the regional basic data set.

4. The method according to claim 1, wherein Based on the airspace hierarchical model, joint search and rescue path planning is performed for multiple UAVs and multiple manned aircraft to obtain joint search and rescue path planning results, including: Based on the airspace hierarchical model, the search and rescue mission areas are allocated to the multiple UAVs and the multiple manned aircraft to obtain UAV search and rescue mission areas and manned aircraft search and rescue mission areas; Performing search and rescue path planning for the multiple drones based on the drone search and rescue mission area, and generating a drone search and rescue path planning result; Performing search and rescue path planning for the multiple manned aircraft based on the manned aircraft search and rescue mission area, and generating a manned aircraft search and rescue path planning result; The unmanned aerial vehicle search and rescue path planning result and the manned aircraft search and rescue path planning result are integrated to generate the joint search and rescue path planning result.

5. The method according to claim 4, wherein Performing search and rescue path planning for the multiple drones based on the drone search and rescue mission area, and generating drone search and rescue path planning results, including: Collecting basic characteristic parameters of the plurality of drones to obtain a drone characteristic data set; Conducting deep learning based on the drone search and rescue path planning set of the multiple drones to build a drone search and rescue path planning model; Based on the UAV search and rescue mission area and the UAV characteristic data set, search and rescue paths are planned for the multiple UAVs according to the UAV search and rescue path planning model to obtain the UAV search and rescue path planning results.

6. The method according to claim 1, wherein The multiple unmanned aerial vehicles and the multiple manned aircraft perform search and rescue in the target search and rescue area according to the optimized search and rescue path planning result, including: Based on the optimized search and rescue path planning results, establishing search and rescue path planning grids for each human and machine; Performing real-time monitoring on the multiple unmanned aerial vehicles and the multiple manned aircraft to obtain real-time flight monitoring data of each manned aircraft; Path deviation detection is performed on the real-time flight monitoring data of each man-machine based on the man-machine search and rescue path planning grid, and a man-machine path deviation warning signal is generated.

7. The method according to claim 1, wherein A wireless communication module is included between the multiple unmanned aerial vehicles and the multiple manned aircraft, and the wireless communication module is used for communication connection between the multiple unmanned aerial vehicles and the multiple manned aircraft.

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