An unmanned aerial vehicle dynamic geographic fence self-adaptive generation method for urban dense building areas

Abstract

This invention discloses an adaptive generation method for dynamic geofences for UAVs in densely built-up urban areas, belonging to the field of UAV flight control technology. This method aims to solve the problem that traditional fixed geofences cannot simultaneously ensure high-speed flight safety and passage through narrow spaces in urban environments. Its core lies in establishing a two-way "velocity-space" constraint mechanism, including: real-time acquisition of the UAV's flight speed V and the distance D to the nearest obstacle. obs The minimum safe radius R is determined based on the speed V using a braking distance model or lookup table. safe It generates non-isotropic dynamic geofence boundaries; and connects the fence boundaries with D obs Comparison; in the event of a conflict, reverse calculations are performed based on environmental geometry to force the drone to travel at a speed not exceeding the maximum permissible speed V. max Fly until the conflict is resolved.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) flight control technology, specifically relating to a method for autonomous obstacle avoidance and activity range limitation of UAVs in complex urban environments, and in particular a method for dynamically and adaptively generating geofences based on flight speed and environmental geometric features. Background Technology

[0002] With the widespread application of drones in urban logistics, inspection, and emergency response, ensuring their flight safety in complex environments such as densely built-up areas has become crucial. Geo-fencing technology, as a virtual electronic boundary, is widely used to limit the operating range of drones, preventing them from entering no-fly zones or colliding with obstacles.

[0003] Existing drone geofencing technologies are mainly divided into two categories: one is fixed fences based on static databases, such as pre-delineating permanent no-fly zones on a map, such as airports and government buildings; the other is simple dynamic fences, such as setting a spherical or cylindrical safe area with a fixed radius centered on the drone's current location.

[0004] However, the aforementioned existing technologies have significant drawbacks in applications within densely built-up urban areas: 1. Lack of dynamic adaptability, highlighting the contradiction between safety and efficiency: Static or fixed-radius fences cannot be coupled with the real-time motion state (especially speed) of drones. When a drone flies at high speed, its physical braking distance may far exceed the fixed safety radius. Once an obstacle suddenly appears near the fence boundary, the drone will suffer an inertial impact due to insufficient braking distance. Conversely, if the radius of the fixed fence is increased indiscriminately to ensure high-speed safety, in the "urban canyon" of towering buildings, drones will logically be deemed "without a way to go," unable to plan paths through narrow streets or gaps between buildings, severely restricting the efficiency of tasks such as logistics and delivery.

[0005] 2. High computational requirements, making low-cost deployment difficult: Some advanced dynamic obstacle avoidance solutions rely on complex algorithms such as Simultaneous Localization and Mapping (SLAM) or Model Predictive Control (MPC). While these algorithms are flexible, they consume a lot of computational resources, making it difficult to implement real-time operation on low-cost, low-power embedded flight control systems (such as systems based on microcontrollers like STM32), thus limiting their application in large-scale commercial drone fleets.

[0006] Therefore, there is an urgent need for a method for generating dynamic geofences for UAVs that can ensure physical safety during high-speed flight, adapt to the needs of passage in narrow spaces, and is computationally lightweight and easy to implement in engineering. Summary of the Invention

[0007] 1. Technical problem to be solved: To address the problems existing in the prior art, the present invention aims to provide an adaptive generation method for dynamic geofences of unmanned aerial vehicles (UAVs) in densely built-up urban areas. The core idea of ​​this method is to establish a two-way "velocity-space" constraint mechanism, enabling the geofence to expand forward according to flight speed to ensure braking margin, and to contract backward according to environmental geometry to restrain flight speed to adapt to narrow passages, thereby achieving a balance between safety and accessibility.

[0008] 2. Technical Solution: To solve the above problems, the present invention adopts the following technical solution.

[0009] An adaptive method for generating dynamic geofences for UAVs in densely built-up urban areas includes the following steps: S1. Real-time acquisition of the UAV's current flight speed V and the distance D to the nearest obstacle detected by the onboard sensors. obs ; S2. Determine the corresponding minimum safe radius R based on the current flight speed V. safe Wherein, the minimum safety radius R safe It includes at least the minimum safe braking distance calculated based on the current flight speed V; S3, based on the minimum safety radius R safe The dynamic geofence boundary at the current moment is generated through geometric modeling or envelope generation algorithms. S4. The distance D between the dynamic geofence boundary and the nearest obstacle... obs Perform a comparison; S5. If the dynamic geofence boundary conflicts with an obstacle, then based on the environmental geometry at the time of the conflict, the system calculates in reverse and forces the drone to operate at a speed not exceeding the maximum permissible speed V. max The flight speed continues until the dynamic geofence boundary breaks free from the obstacle.

[0010] Preferably, in step S2, the minimum safety radius R safe The calculation model is as follows: R safe = R base + V * T react + (V 2 / 2a); Among them, R base For the basic protection radius of the drone fuselage, T react Let be the system reaction time, and 'a' be the maximum braking acceleration. This model directly links the fence safety boundary to the physical braking process, allowing the safety margin to change scientifically with speed.

[0011] To further reduce the complexity of real-time computation, in step S2, R can be determined using a pre-stored lookup table. safe The lookup table stores the mapping relationship between different speed ranges and discrete safety radius values. For example, low speed (<5m / s) corresponds to 2 meters, medium speed (5-10m / s) corresponds to 10 meters, and high speed (>15m / s) corresponds to 30 meters.

[0012] Preferably, in step S3, the shape of the dynamic geofence boundary is anisotropic, specifically, the extension length in the direction of the current flight speed V vector is greater than or equal to the extension length perpendicular to that vector direction, for example, forming an ellipsoidal or teardrop-shaped envelope. This shape provides more braking space in the velocity direction while remaining compact in the lateral direction, which is beneficial for pathfinding in narrow environments.

[0013] Preferably, in step S5, "based on the environmental geometric features during the conflict, the maximum permissible speed V is calculated in reverse". max Specifically, it includes: Based on the nearest obstacle distance D at the time of the conflict obs The actual usable space width determines a temporary safety radius R after contraction. temp ; According to the temporary safety radius R temp Using the minimum safety radius R safe The maximum permissible speed V is obtained by reversing the forward calculation relationship (such as the formula or lookup table above) with respect to speed V. max This step achieves a closed-loop feedback from "space constraint" to "speed constraint," and is the key to the two-way constraint mechanism.

[0014] The present invention also provides an electronic device and a computer-readable storage medium for implementing the above method.

[0015] 3. Beneficial effects: 1. Compared with the prior art, the technical solution provided by this invention has the following beneficial effects: It fundamentally solves the contradiction between safety and passage: through the two-way constraint of "speed-space", the fence is allowed to expand with speed in open areas to prevent high-speed impacts, while in narrow areas the fence is forced to contract and speed is limited simultaneously to achieve safe passage, effectively balancing flight safety and environmental adaptability.

[0016] 2. Conforms to the laws of physical motion, resulting in higher safety: The innovative "braking distance" model is embedded into the geofence generation logic, making the design of the safety boundary based on the dynamic characteristics of the drone. This is more scientific and reliable than simply relying on distance judgment, and can effectively prevent collisions caused by insufficient braking.

[0017] 3. Lightweight computation and strong engineering practicality: The core logic avoids complex SLAM mapping and trajectory optimization algorithms, mainly relying on geometric comparison, formula calculation or table lookup operations. The algorithm has low complexity and can be easily deployed on low-cost microcontrollers such as STM32, meeting the cost and reliability requirements of large-scale logistics drone fleets.

[0018] It should be noted that the structures not described in this invention are not related to the design points and improvement directions of this invention, and are the same as or can be implemented using existing technologies, so they will not be elaborated here. Attached Figure Description

[0019] Figure 1 The flowchart illustrates the overall process of the dynamic geofence adaptive generation and speed constraint method provided in this embodiment of the invention.

[0020] Figure 2 This is a schematic diagram of the hardware system architecture and data flow involved in the embodiments of the present invention.

[0021] Figure 3 This is a diagram illustrating the mapping relationship between speed and fence radius, and the speed clamping principle in an embodiment of the present invention.

[0022] Figure 4 This is a schematic diagram illustrating the changes in the dynamic geofence range at three different flight speeds, as shown in an embodiment of the present invention.

[0023] Figure 5 This is a schematic diagram illustrating how environmental geometric features exert inverse constraints on the fence range and flight speed, as shown in an embodiment of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0025] A typical application scenario for this invention is drone logistics delivery in an urban CBD (Central Business District) environment. The drone needs to flexibly switch between high-speed cruising between buildings and navigating narrow gaps between buildings.

[0026] like Figure 1 and Figure 2 As shown, the implementation of this invention relies on the flight control system, onboard sensors (such as LiDAR or depth cameras), and microprocessor of the UAV platform. The sensors perceive the foreground and surrounding environment in real time, obtaining the distance D to the nearest obstacle. obsThe flight control system provides status information such as the current flight speed V. The microprocessor executes the method described in this invention, outputting dynamic fence boundary information and speed control commands.

[0027] Example 1: Open Airspace Cruise Phase Reference Figure 4 Assume the drone is cruising at a speed of V of 15 m / s over a wide road.

[0028] The system executes step S1, acquiring a velocity V = 15 m / s. The sensor detects that the area ahead is open, D obs The value is very large (e.g., greater than 50 meters).

[0029] In step S2, the system determines the minimum safety radius R required based on the speed V by looking up a table (e.g., "high speed > 15m / s corresponds to 30 meters") or by calculation formula. safe The distance is 30 meters. This 30 meters includes the fuselage radius, the displacement during the system's reaction time, and the braking distance required to decelerate from 15 m / s to a stop.

[0030] Perform step S3 to generate a dynamic geofence boundary centered on the drone's current location. This boundary extends approximately 30 meters in the velocity vector direction (i.e., the direction of travel) and a smaller base value (e.g., 5 meters) in the vertical direction (lateral direction), forming a virtual envelope in the shape of an elongated ellipsoid (e.g., ...). Figure 4 (As shown).

[0031] Executing step S4, the comparison revealed that the generated 30-meter fence was completely in open space (D obs > 30 meters), no conflict.

[0032] Therefore, without triggering step S5, the drone is allowed to maintain a high-speed cruise of 15m / s, making full use of open space to improve delivery efficiency.

[0033] Example 2: Confined Space Traversal Stage Reference Figure 5 The drone approached the narrow passage between the two buildings, and the sensors detected that the actual physical gap was only 8 meters wide.

[0034] The system continues to run. In the comparison in step S4, the system finds that the 30-meter fence currently generated based on a speed of 15 m / s (especially its lateral projection) conflicts with the virtual obstacle surfaces of the buildings on both sides (i.e., the fence boundary exceeds the actual passage by 8 meters).

[0035] At this point, the reverse constraint mechanism in step S5 is triggered: a) Geometric Compression: Based on the actual passage width of 8 meters, the system reserves a safety margin of 1 meter on each side, and forcibly shrinks the lateral radius of the dynamic fence to a temporary safety radius R.temp = 3 meters.

[0036] b) Speed ​​clamping: The system adjusts speed according to R temp = 3 meters, calculate the maximum permissible speed in reverse. For example, using formula R safe = R base + V * T react + (V 2 The inverse operation of / 2a) or a reverse lookup table (e.g., the speed range corresponding to a safety radius of 3 meters is "low speed < 5 m / s") is used to calculate the maximum permissible speed V under the current environment. max It is approximately 4 m / s.

[0037] c) Action execution: The flight control system receives V max The command immediately initiates active braking, rapidly reducing the drone's speed from 15 m / s to below 4 m / s. As the speed decreases, R is recalculated based on the positive relationship (step S2). safe It also decreases synchronously. When the speed drops to 4 m / s, the corresponding fence radius is approximately 2-3 meters, which can be accommodated within an 8-meter passage (e.g., Figure 5 As shown), the conflict is resolved.

[0038] Through this process, drones can safely and controllably traverse narrow spaces that would otherwise be impassable at high speeds.

[0039] Example 3: Resumption of Cruise Phase After the drone successfully passed through the gap between buildings, the sensors detected D. obs The value increases rapidly.

[0040] During the continuous comparison in step S4, the system found that the conflict between the dynamic fence boundary and environmental obstacles had been resolved.

[0041] The geometric compression constraints imposed by the environment on the fence are then released. The system resumes the forward flow where speed dominates fence generation. With open space ahead, the fence radius can expand again with increasing speed, the system removes the hard speed limit, and the drone can safely accelerate to continue to the next destination.

[0042] In summary, this invention achieves real-time, adaptive linkage between geofencing and flight speed and environmental geometry through a simple and efficient "perception-computation-comparison-feedback" loop. It is not only more scientifically sound in its algorithmic principles but also possesses significant cost and reliability advantages in engineering implementation, making it highly suitable for large-scale urban drone operations with stringent safety and efficiency requirements.

[0043] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for adaptive generation of dynamic geofences for UAVs in densely built-up urban areas, characterized in that, Includes the following steps: S1. Real-time acquisition of the UAV's current flight speed V and the distance D to the nearest obstacle detected by the onboard sensors. obs ; S2. Determine the corresponding minimum safe radius R based on the current flight speed V. safe Wherein, the minimum safety radius R safe It includes at least the minimum safe braking distance calculated based on the current flight speed V; S3, based on the minimum safety radius R safe The dynamic geofence boundary at the current moment is generated through geometric modeling or envelope generation algorithms. S4. The distance D between the dynamic geofence boundary and the nearest obstacle... obs Perform a comparison; S5. If the dynamic geofence boundary conflicts with an obstacle, then based on the environmental geometry at the time of the conflict, the system calculates in reverse and forces the drone to operate at a speed not exceeding the maximum permissible speed V. max The flight speed continues until the dynamic geofence boundary breaks free from the obstacle.

2. The method for adaptive generation of dynamic geofences for UAVs in densely built-up urban areas according to claim 1, characterized in that, In step S2, the minimum safety radius R safe The calculation model is as follows: R safe = R base + V * T react + (V 2 / 2a); Among them, R base For the basic protection radius of the drone fuselage, T react Let be the system reaction time, and 'a' be the maximum braking acceleration.

3. The method for adaptive generation of dynamic geofences for UAVs in densely built-up urban areas according to claim 1, characterized in that, In step S2, the minimum safe radius R corresponding to the current flight speed V is determined by using a speed-safe radius mapping lookup table pre-stored in the onboard memory. safe The lookup table stores the mapping relationship between different speed ranges and discretized safety radius values.

4. The method for adaptive generation of dynamic geofences for UAVs in densely built-up urban areas according to claim 1, characterized in that, In step S3, the shape of the dynamic geofence boundary is anisotropic, and its extension length in the direction of the current flight speed V vector is greater than or equal to its extension length perpendicular to the vector direction.

5. The method for adaptive generation of dynamic geofences for UAVs in densely built-up urban areas according to claim 4, characterized in that, The shape of the dynamic geofence boundary is an ellipsoidal or teardrop-shaped envelope, wherein the teardrop-shaped envelope expands in an arc along the velocity direction at the leading edge and gradually narrows at the trailing edge.

6. The method for adaptive generation of dynamic geofences for UAVs in densely built-up urban areas according to claim 1, characterized in that, In step S5, "based on the environmental geometry characteristics during the conflict, the maximum permissible speed V is calculated in reverse." max Specifically, it includes: Based on the nearest obstacle distance D at the time of the conflict obs The actual usable space width determines a temporary safety radius R after contraction. temp ; According to the temporary safety radius R temp Using the minimum safety radius R safe The maximum permissible speed V is obtained by reversing the forward calculation relationship between the speed and the velocity V. max .

7. The method for adaptive generation of dynamic geofences for UAVs in densely built-up urban areas according to claim 1, characterized in that, In step S1, the airborne sensor is a lidar or a depth camera; the nearest obstacle distance D obs This refers to the minimum obstacle distance within each sector after dividing the environment in front into sectors.

8. The method for adaptive generation of dynamic geofences for UAVs in densely built-up urban areas according to claim 1, characterized in that, The method further includes an environmental congestion quantification step performed after step S4: Based on the nearest obstacle distance D obs Calculate the environmental congestion factor K, where K is related to D. obs Negative correlation; The environmental congestion factor K is used to assist in determining whether to enter the reverse constraint process of step S5.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1 to 8.

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