74 results about "No-fly zone" patented technology

The present application belongs to the technical field of co-delivery center site selection. Disclosed are an urban-end co-distribution center site selection method and system based on air-ground coordination, which are used for solving the technical problems of an existing air-ground coordination co-delivery center site selection model being insufficiently integrated with an unmanned aerial vehicle delivery technique, failing to take into consideration constraint conditions on unmanned aerial vehicles, and the solving efficiency being low. The method comprises: establishing a planned layout set of an air-ground coordination co-delivery center; taking the minimization of the overall delivery time and the minimization of the total network operation cost as objectives to construct an air-ground coordination layout planning model; setting constraint conditions of the air-ground coordination layout planning model; by means of a grey wolf population algorithm, generating an initial site selection solution set for a pre-selected point set of the co-delivery center; filtering out invalid solutions in the initial site selection solution set on the basis of the constraint conditions; and performing an individual iterative update on the filtered initial site selection solution set, and performing a fitness assessment and no-fly zone crossing determination on a new solution set obtained after each iteration, so as to obtain a final site selection scheme.

The invention provides a multi-unmanned aerial vehicle adaptive migration method for a sudden no-fly zone, and relates to the technical field of unmanned aerial vehicles. And acquiring fusion environment data through cooperative sensing of multiple unmanned aerial vehicles, processing the data by adopting a semantic boundary extraction algorithm based on deep learning, and outputting a sudden no-fly zone map and unmanned aerial vehicle safety state codes. On the basis, the unmanned aerial vehicles are shunted according to the safety state codes and the sudden no-fly zone map, real-time obstacle avoidance tracks are generated for the non-intruding unmanned aerial vehicles, and cluster collaborative separation is executed for the intruding unmanned aerial vehicles. For unmanned aerial vehicles whose tasks are interrupted due to obstacle avoidance or separation, task decomposition is realized based on the sudden no-fly zone map and space-time isolation, and continuous execution of cluster tasks is realized through an energy consumption constraint auction mechanism, so that a plurality of unmanned aerial vehicles can better cope with a complex and changeable sudden no-fly zone environment.

The invention relates to the technical field of electric power, in particular to an unmanned aerial vehicle route automatic planning method and system for a complex space of a transformer substation. Analyzing at least one inspection task instruction input by a user to obtain a task type corresponding to each inspection task instruction and a data acquisition requirement corresponding to the task type; according to a preset three-dimensional semantic map of the transformer substation and the data acquisition requirement of each inspection task instruction, a flight route of at least one unmanned aerial vehicle is automatically generated by adopting a path planning algorithm, and the flight route comprises a waypoint sequence, a flight height, a shooting angle and a holder action instruction; the three-dimensional semantic map comprises no-fly zone information in the transformer substation, a plurality of devices, the position of each device, the device type, the electrification attribute and the safety rule label. And controlling the corresponding unmanned aerial vehicle to inspect target equipment in the transformer substation according to the flight route of each unmanned aerial vehicle. And large-scale and standardized inspection operation is realized.

The invention relates to the technical field of unmanned aerial vehicle cluster navigation, and discloses an unmanned aerial vehicle cluster navigation method based on man-machine interaction. The method comprises the steps that cluster task parameters and environment constraint conditions input by an operator are efficiently received through a multi-modal interaction interface, the cluster task parameters comprise a target point coordinate set and formation form identification, and the environment constraint conditions cover no-fly zone coordinates and meteorological interference levels. And the system calls a pre-stored topological relation template according to the formation form identifier to generate an initial track, completes space conflict detection in combination with the no-fly zone coordinates, and outputs a corrected track point sequence. And inputting the meteorological interference level into a dynamic anti-interference model to calculate a course correction amount, and superposing the course correction amount to a corrected track point sequence to generate an anti-interference navigation instruction set. And acquiring position data of the unmanned aerial vehicle in real time to calculate a trajectory deviation degree, triggering a manual intervention request if the trajectory deviation degree exceeds a dynamic threshold value, calling a correction instruction, fusing to generate a hybrid control strategy, and distributing the hybrid control strategy to each node of the cluster to realize reliable man-machine collaborative navigation.

The invention relates to the technical field of unmanned aerial vehicle navigation and control, in particular to an improved sparrow searching method for three-dimensional unmanned aerial vehicle path planning. According to the method, a three-dimensional mountain terrain model is constructed through a Gaussian function superposition method, obstacles and no-fly zones are defined by adopting a cylindrical geometric model, and obstacle avoidance constraints, take-off and landing safety constraints and flight height constraints are set; a comprehensive cost function with path length, threat penalty, flight safety height and path smoothness as sub-targets is constructed, an improved sparrow search algorithm is introduced, and a sine and cosine strategy is adopted in discoverer position updating to dynamically balance horizontal exploration and vertical obstacle avoidance. A Levy flight mechanism is introduced in follower position updating so as to enhance the capability of local development and jumping out of local optimum; the method comprises the steps of population initialization, role division, iterative optimization, collision detection and an elitist retention strategy. According to the method, a safe and smooth three-dimensional flight path with the optimal comprehensive cost is output, and the method is suitable for an actual unmanned aerial vehicle navigation task.

The invention discloses a multi-unmanned aerial vehicle area coverage path planning method with no-fly zone avoidance. The method comprises the following steps: converting an input operation area boundary and longitude and latitude coordinates of a no-fly zone into a UTM plane rectangular coordinate system, carrying out safety buffer area expansion on all no-fly zones, and calculating a difference set between the operation area and the safety buffer areas of all no-fly zones to obtain a final available operation area; segmenting the final available operation region to generate a plurality of sub-regions with balanced areas; for each sub-region, generating a parallel scanning route covering the region; and each sub-region line segment generates a parallel scanning route covering the region according to a preset interval. The method can ensure that the path avoids the no-fly zone, the zone distribution is balanced, and the calculation is efficient.

The invention discloses a method and a system for planning a route of an unmanned aerial vehicle over a lake by fusing meteorological data, and belongs to the technical field of unmanned aerial vehicle navigation. The method comprises the following steps: acquiring an initial route according to an unmanned aerial vehicle flight starting place and no-fly zone data; obtaining current meteorological data of an area corresponding to the initial route of the unmanned aerial vehicle, inputting the current meteorological data into the climate risk model, outputting a risk entropy value, and judging whether the unmanned aerial vehicle passes through a high-risk field; meanwhile, the flight safety of the unmanned aerial vehicle is judged according to the stability function; if the initial route passes through the high-risk field and the flight safety of the unmanned aerial vehicle is dangerous, taking a cost function to obtain a minimum value as a target, and optimizing the initial route to obtain a target route; and if the flight safety of the unmanned aerial vehicle is safe, taking the initial route as a target route. And safe flight and compliance control in a complex airspace above a lake are realized.

The invention discloses a highway inspection unmanned aerial vehicle task planning monitoring system based on a knowledge graph, particularly relates to the technical field of unmanned aerial vehicle intelligent inspection, and is used for solving the problem that an existing system cannot generate a feasible decision scheme when an airspace control rule conflicts with an emergency timeliness requirement. The method comprises the following steps: constructing a knowledge graph integrating geographic no-fly zone space coordinates, an aging constraint time threshold and a traffic flow thermodynamic map layer, triggering a rule conflict state when detecting that an overlapping region exists between an aging constraint target position and a no-fly zone, and further fusing a real-time traffic control instruction and unmanned aerial vehicle maneuvering characteristic parameters to generate an elastic space domain; and after verification of a high-flow area overlapping rate threshold value or approval of a forced passing instruction, creating a temporary channel entity which covers the overlapping area coordinates and is associated with a time threshold value, and finally generating an inspection route which penetrates through a conflict area and meets a passing time limit based on the entity, thereby realizing coordination of dynamic rule conflict resolution and safety emergency inspection.

The invention discloses a multi-unmanned aerial vehicle cooperation-oriented low-altitude Internet of Things secure communication method and system, and belongs to the technical field of low-altitude Internet of Things. The method comprises the following steps: constructing a multi-unmanned aerial vehicle collaborative optimization model integrating a no-fly zone, a ground obstacle zone, communication security and energy consumption constraint; aiming at a high-dimensional non-convex characteristic of the model, a solution algorithm based on a multi-agent depth deterministic strategy gradient framework is adopted, and a local observation space containing a self state, environment information and a channel state and an action space containing speed adjustment and power adjustment are designed for each unmanned aerial vehicle agent through a centralized training decentralized execution mechanism; and a multi-target reward function is defined, so that the intelligent agent autonomously learns a cooperative strategy in a complex environment through training, and an optimized unmanned aerial vehicle track and transmitting power control instruction is jointly output. According to the invention, on the premise of strictly ensuring flight safety and physical layer safety, the sum of long-term safety communication rates of the low-altitude Internet of Things is effectively improved, and the method has good dynamic adaptability and expandability.

The invention discloses an unmanned aerial vehicle autonomous route planning method and system capable of adapting to environmental changes. The method comprises the steps of obtaining a dynamic three-dimensional situation map of a flight area of an unmanned aerial vehicle based on multi-source data of the flight area of the unmanned aerial vehicle; acquiring a current no-fly zone and weather forecast information of the flight area of the unmanned aerial vehicle, and analyzing and acquiring a flight risk distribution condition of the flight area of the unmanned aerial vehicle based on the dynamic three-dimensional situation map; considering the flight risk distribution condition of the unmanned aerial vehicle flight area, the unmanned aerial vehicle flight task and the unmanned aerial vehicle self-state information to generate an unmanned aerial vehicle reference route; and automatically adjusting the flight speed, the flight height and the flight course of the unmanned aerial vehicle according to a preset flight parameter adjustment model based on the acquired multi-source data and the state information of the unmanned aerial vehicle in the process of flying along the reference route. According to the invention, the reliability and safety of flight task execution of the unmanned aerial vehicle in a complex dynamic environment can be improved.

The embodiment of the invention provides an unmanned aerial vehicle route early warning method, system and device for a no-fly zone and a medium, and belongs to the field of unmanned aerial vehicles. The method comprises the following steps: establishing a no-fly zone spatial database, and storing electronic fence boundaries and security level attributes of each zone; the method comprises the following steps: acquiring detection data, tracking an unmanned aerial vehicle entering a no-fly zone, generating a real-time flight path, performing compliance verification on the real-time flight path by taking a reported planned route of the unmanned aerial vehicle as a reference, marking the unmanned aerial vehicle deviating from the planned route as a suspicious target, associating identity information of the unmanned aerial vehicle with pilot information, and triggering primary early warning at the same time; and for a suspicious target, carrying out spatial superposition calculation on the flight path of the suspicious target and the electronic fence of the no-fly zone, determining an intrusion risk level according to a security level attribute, and generating an intrusion event and advanced early warning information. Accurate monitoring and track traceability management and control of the unmanned aerial vehicles in key areas are realized, and the accuracy of safety prevention and control of the unmanned aerial vehicles in the areas is improved.

The invention discloses an unmanned aerial vehicle autonomous cruise and obstacle avoidance method and system for a dynamic no-fly zone. Relates to the technical field of unmanned aerial vehicle autonomous navigation and path planning. The method comprises the following steps: issuing and keeping a no-fly zone set static in a task initialization stage; fusing the multi-source sensing data through a layered SLAM mapping module to generate an occupied grid map; performing global path planning by adopting an improved BFS algorithm and a weighted greedy strategy based on a map, and generating a patrol sequence covering reachable points; compressing and smoothing the spliced path to generate a final cruise trajectory; and triggering local trajectory re-planning for sudden obstacles in a non-no-fly zone based on SLAM perception in flight. According to the invention, autonomous cruise and dynamic obstacle avoidance with high coverage rate and low redundancy are realized under the condition of strictly following the constraint of the static no-fly zone.

The invention discloses a low-altitude unmanned aerial vehicle path planning method and system based on deep learning. The method comprises the following steps: acquiring flight state information and environment data of an unmanned aerial vehicle in real time; task priority calculation: calculating the task priority according to the unmanned aerial vehicle state information in combination with the task emergency degree and the task type; based on collaborative decision-making of deep reinforcement learning, decision-making is carried out on task allocation and flight paths through a deep learning algorithm in combination with unmanned aerial vehicle state and environment data and task priority information; planning an optimal flight path for the unmanned aerial vehicle according to the intelligent decision comprehensive environment data; the flight path is a discrete point set path planning target which minimizes the flight time on the premise of avoiding a no-fly zone and an obstacle; and in the process of executing the path planning by the unmanned aerial vehicle, when abnormality is detected, making a decision again, and performing path planning again until the unmanned aerial vehicle finally reaches the target position point.

This application discloses a low-altitude airspace planning method and system based on micro-terminal areas, relating to the field of low-altitude airspace planning. The method includes: generating corresponding take-off and landing areas for low-altitude take-off and landing points or fields, and generating take-off and landing hub areas for adjacent take-off and landing points or fields to construct micro-terminal areas of the low-altitude airspace; constructing risk areas, which, together with control areas and no-fly zones, constitute the restricted areas of the low-altitude flight path network; generating alternative intermediate points connecting each take-off and landing point or field based on the restricted areas; constructing an initial low-altitude flight path network connecting all points based on the data of the alternative intermediate points and the data of the micro-terminal areas; calculating the corresponding weights of each edge line in the initial low-altitude flight path network, and optimizing the initial low-altitude flight path network based on the weighted network; and removing redundant intermediate points based on the optimized network, constructing the flight path network and the entry and exit points of each micro-terminal area to generate the final low-altitude flight path network. This application provides a more reliable technical basis for low-altitude flight path network planning.

The present invention is a system and method for a UAV flight highway and management thereof, comprising: a ground control station, a server (for example a cloud server), a geographic locator communication device, a communication transmitter, and one or more UAVs. The present invention is operable to identify ground level topography and air space objects (e.g., buildings) within a region, as well as other restrictions to UAV flights (e.g., restricted flight zones), and generates within such region a UAV flight highway, that may be multi-lane and multi-layer, based upon specific latitudinal and longitudinal points. The present invention is operable to control the flight of one or more UAVs along such flight highway, along multiple-lanes thereof, wherein the UAVs may travel at different speeds in different lanes and different layers along the UAV flight highway.

The application discloses a hierarchical encryption storage method and system for a UAV. The method comprises the following steps: obtaining a dynamic security policy coded as a smart contract from a block chain network, and calling a static security policy from a local; the dynamic security policy and the static security policy both comprise a no-fly area and an execution action; obtaining a three-dimensional position of the UAV in real time, and judging whether the three-dimensional position of the UAV is in or near the no-fly area of the dynamic security policy or the static security policy; if yes, calling a pre-configured flight task type from the local, and determining a security level according to the flight task type; executing the corresponding dynamic security policy or static security policy according to the security level, and performing hierarchical data encryption storage. When the UAV enters a sensitive area, not only corresponding flight control is implemented, but also encryption of a corresponding level is triggered automatically and in real time, so that linkage protection of flight safety and data safety is realized, and data protection and flight state are ensured to be performed synchronously.

The invention discloses an intrusion detection method and device for an electronic fence, a medium and equipment, and relates to the technical field of low-altitude electronic fences. According to the method, the candidate electronic fences where the target unmanned aerial vehicle possibly falls are quickly retrieved through the plane circumscribed rectangles of all the electronic fences, then the specific relation between the target unmanned aerial vehicle and the candidate electronic fences is quickly judged through the ray method, finally height judgment is conducted, and through layer-by-layer progressive judgment, the height of the target unmanned aerial vehicle is judged. According to the method, only possibly invaded fences can be accurately judged, the detection efficiency is improved, meanwhile, space calculation is carried out through a layer-by-layer progressive 3D calculation means, intrusion detection of variable three-dimensional electronic fences can be rapidly coped with, therefore, the electronic fences in any three-dimensional form can be self-defined in a complex airspace, a plane no-fly zone is defined, and the detection efficiency is improved. And the height layer of the airspace is accurately managed, three-dimensional airspace management is realized, and the flexibility of airspace management is improved.

The application belongs to the technical field of unmanned aerial vehicle communication sensing, and specifically discloses a method for jointly scheduling communication and sensing resources of an unmanned aerial vehicle in a covert communication scenario, which comprises the following steps: establishing a communication performance model and a radar sensing performance model; combining covert communication constraints and no-fly zone avoidance constraints to build a joint optimization problem of communication and sensing resources; solving the closed-form optimal solution of sensing power and communication power, and reducing variables of the joint optimization problem; introducing a segmented hovering flight structure to discretize the continuous flight trajectory of the unmanned aerial vehicle, and combining the closed-form optimal solution to convert the joint optimization problem into a discrete optimization problem; using a block coordinate descent algorithm to alternately and iteratively solve the discrete optimization problem, and outputting an optimal user association strategy, an unmanned aerial vehicle hovering time and a continuous flight trajectory of the unmanned aerial vehicle. The application can improve the covert communication capability and comprehensive sensing accuracy of the unmanned aerial vehicle under the premise of meeting the covert communication demand and physical no-fly zone constraints.

The application provides a kind of unmanned aerial vehicle route smoothing method and system for grid path, it is related to the field of path optimization.The method obtains the original path point set of unmanned aerial vehicle, and based on the position of no-fly area, eliminates the redundant path point in the original path point set, to obtain a plurality of key path points, and then based on a plurality of key path points, obtain the smoothed route.As there is a security risk in the no-fly area flight, the method provided in the application considers the position of the no-fly area when eliminating the redundant path point, so that the route after eliminating the redundant path point will not pass through the no-fly area, i.e.the smoothed route avoids the area that may cause security risk, so the safety of the smoothed route can be effectively improved.

The invention belongs to the technical field of coast engineering measurement, and particularly relates to an automatic measuring vehicle for measuring an artificial shoreline. The measuring vehicle comprises a vehicle body, a high-precision GPS receiver, a laser sensor, a controller and a safety protection system. The controller controls the vehicle body to automatically run along the shoreline through a closed-loop algorithm, synchronously collects position and distance data, and solves the coordinates of the shoreline; meanwhile, multiple levels of safety protection mechanisms, such as an early warning air bag, graded deceleration and an emergency separation structure, are integrated, the system adapts to a complex levee top environment, efficient, automatic and high-precision shoreline surveying and mapping operation can be achieved under the conditions of a no-fly zone and the like, and the measurement safety and the data reliability are remarkably improved.

This disclosure presents a method, device, and medium for heterogeneous sensor collaborative localization and map building. The method includes: generating a weighted fusion global descriptor for each platform; filtering cross-platform loop closure candidates through similarity calculation and maximizing algebraic connectivity; performing local point cloud registration on selected loops and employing a minimum vertex coverage algorithm to reduce communication overhead; dynamically electing and optimizing nodes; optimizing the decentralized pose graph using an asymptotic nonconvexity algorithm and fusing prior pose prediction with GNSS adaptive constraints; and unifying the global reference system through dynamic anchor point selection and iterative alignment. This disclosure achieves complementary advantages between backpack and UAV systems, significantly reducing communication and computational burdens while ensuring compliant operation in no-fly zones, and improving the accuracy and global consistency of collaborative localization and mapping in complex environments.

Aiming at the problem of missile terminal guidance with no-fly zone and impact angle constraints, a model predictive static programming guidance law based on control barrier function is proposed. The proposed algorithm inherits the high efficiency of the traditional model predictive static programming while ensuring the no-fly zone constraints. The main reasons are as follows: first, the projection operator under the linear inequality constraints has an explicit expression, avoiding additional optimization solving; second, due to the forward invariance of the control barrier function, the no-fly zone constraints do not need to be imposed at all discrete nodes, but only at one or a few discrete nodes, reducing the scale of solving. The simulation results show that the proposed algorithm can meet the no-fly zone and impact angle constraints while ensuring high computational efficiency. Subsequent research will focus on trajectory optimization and guidance problems with multiple no-fly zone constraints, even dynamic no-fly zone constraints.

This invention discloses a UAV no-fly zone data storage system and method based on TPM and TEE. It employs a TPM hardware integrity verification module on the UAV to perform integrity verification of no-fly zone data before startup, and establishes a TEE trusted execution environment to ensure the integrity of no-fly zone data by comparing and calculating with waypoint data during flight. This invention significantly defends against attacks that tamper with UAV-borne no-fly zone data throughout the entire UAV startup and flight phase, eliminating the adverse effects of malicious attacks.

The invention relates to the technical field of communication networks, and provides an urban low-altitude airspace communication network performance test method and device, equipment and a medium. The method comprises the following steps: acquiring test constraint data, wherein the test constraint data comprises a test area boundary, building data, no-fly zone information, unmanned aerial vehicle performance parameters and battery energy constraint; according to the test constraint data, performing flight route planning processing of the unmanned aerial vehicle to generate a test route; controlling the unmanned aerial vehicle carrying the test terminal to fly according to the test route, controlling the test terminal to perform a communication test with the test server by using the to-be-tested communication network in the flight process of the unmanned aerial vehicle, and synchronously collecting space-time position information of the unmanned aerial vehicle and communication performance data obtained in the communication test; and associating and processing the space-time position information and the communication performance data, and calculating a performance index of the communication network to be tested. The invention provides a feasible method for testing the performance of the low-altitude communication network, and the safety and efficiency of the test are remarkably improved.

The application discloses a hypersonic vehicle trajectory planning guidance method, system and equipment, relates to the field of missile trajectory planning, and comprises the following steps: constructing an irregular no-fly zone model according to a dynamics model and constraints and terminal constraints in a flight process; determining the influence range of a segmented polygon no-fly zone by using an irregular no-fly zone segmentation and recombination algorithm according to the irregular no-fly zone model; determining a heading angle corridor and a lateral roll angle flip logic according to the influence range of the segmented polygon no-fly zone; determining a lateral guidance adjustment factor according to the heading angle corridor and the lateral roll angle flip logic; iteratively profiling a roll angle in longitudinal guidance according to the lateral guidance adjustment factor; and realizing hypersonic vehicle trajectory planning guidance according to the lateral guidance adjustment factor and the roll angle profile. The application enables the hypersonic vehicle to evade irregular no-fly zone threats while reaching an expected terminal position, thereby realizing hypersonic vehicle trajectory planning guidance.

This invention relates to the field of UAV collaborative patrol and path planning technology, and discloses a method and system for static trajectory planning and dynamic rescheduling of multiple UAVs in high-altitude areas. The method receives patrol mission instructions, acquires a set of patrol points, digital elevation model data, and environmental data such as air pressure and temperature, determines a comprehensive environmental correction factor, and forms corrected actual performance parameters. Based on these parameters, it performs terrain-aware static trajectory planning on candidate flight paths, forming a static trajectory scheme through comparison of steep ascent, descent, and turn modes. During patrol execution, it detects real-time environmental changes, dynamic no-fly zones, UAV malfunctions, and changes in communication coverage, making corresponding adjustments to affected trajectories, remaining tasks, and relay networks. A post-hoc check determines whether to continue patrolling or continue dynamic relay network adjustments. This invention is applicable to multi-UAV collaborative patrol missions in complex high-altitude terrain scenarios.

The invention discloses an unmanned aerial vehicle electronic fence method based on an artificial potential field method, and the method comprises the following steps: S1, delimiting an electronic fence boundary based on a current flight region of an unmanned aerial vehicle, the interior of the boundary being a flight region, and the exterior of the boundary being a limited / no-fly region; s2, dividing the flight area into a plurality of concentric annular areas according to the distance; s3, based on the current position of the unmanned aerial vehicle, determining the current area of the unmanned aerial vehicle, and obtaining the expected acceleration corresponding to the area; s4, calculating an expected speed based on the expected acceleration; and S5, adjusting the flight speed of the unmanned aerial vehicle according to the calculated expected speed, and returning to the step S1 until the unmanned aerial vehicle lands. According to the method, the motion range of the unmanned aerial vehicle is effectively limited, the risk that the unmanned aerial vehicle rushes out of the fence is reduced, the problem of control failure caused by satellite signal interference and environmental factor influence in a traditional method is avoided, and the method has wide applicability and expandability.

The application discloses a near space hypersonic target maneuvering discrimination method based on pneumatic parameter identification, finds certain certainty law or common sense knowledge of the near space hypersonic target, makes more reasonable and accurate research and judgment on the target trajectory and maneuvering mode on the basis, and provides certain theoretical basis for rapid prediction of target attack range, reduction of detection area and early establishment of interception and attack scheme. The method comprises the following steps: firstly, the radar target tracking technology is used to realize the near space hypersonic target motion state estimation and identification of pneumatic parameters and guidance control parameters; secondly, the target motion is analyzed in the longitudinal and lateral directions, and the longitudinal gliding capacity and lateral turning capacity are analyzed according to the typical maneuvering mode types of the target; finally, the target maneuvering style is comprehensively discriminated by comprehensively combining the prior information such as enemy and our troop deployment, no-fly zone, and the maneuvering capacity of the typical near space hypersonic target.

The present application relates to the technical field of unmanned aerial vehicles, and discloses a method for generating an electronic fence for an unmanned aerial vehicle. The key points of the technical solution of the present application are: to provide a method for generating an electronic fence for an unmanned aerial vehicle on the basis of a geographic information object and a flight mission, wherein the method allows for efficient and intelligent generation of an accurate fence, is safe and convenient, and does not impose excessive restrictions, the electronic fence generated by the method has uniqueness and practical significance imparted by the geographic information object, and demonstrates a high degree of mission matching, for example, setting no-fly zones for densely populated areas such as schools, hospitals, and stations, and setting regular inspection zones for key facilities such as bridges, base stations, and factories; and to provide a method for generating an obstacle electronic fence online on the basis of a sensing system, wherein the method comprises rapidly and effectively using an onboard sensing system to present an obstacle risk in a flight mission in the form of an electronic fence, to provide a timely alert for an operator to adjust the flight route, thereby further enhancing the flight safety of regular operational flight routes.

The invention discloses a multi-unmanned aerial vehicle cooperative reconnaissance path planning method based on multi-step look-ahead search, and belongs to the technical field of unmanned aerial vehicle coverage path planning. The method comprises the following steps: firstly, discretizing a reconnaissance area into a three-dimensional grid, and constructing a hemispherical no-fly zone model with a safe buffer distance; in each simulation step, all feasible paths of a plurality of steps in the future are enumerated for each unmanned aerial vehicle through breadth-first search in sequence, and five indexes including new coverage gain, total coverage amount, distance award guided to an uncovered area, boundary avoidance punishment and turning punishment are comprehensively evaluated in a weighted sum form. And then a dynamic weight updating mechanism is designed, so that the unmanned aerial vehicle adapts to reconnaissance tasks under various conditions. The method can effectively improve the coverage rate and the convergence speed, guarantees the flight safety, and is suitable for large-range cooperative reconnaissance scenes of emergency rescue, disaster monitoring and the like.