Inspection obstacle avoidance method and system for low-altitude traffic checkpoint

Abstract

The invention provides an inspection obstacle avoidance method and system for a low-altitude traffic gate, and relates to the technical field of inspection obstacle avoidance, and the method comprises the steps: marking a low-altitude traffic gate in a target low-altitude airspace, and collecting the spatial point cloud data of the gate; comparing the point cloud data with pre-stored point cloud data, and if the comparison does not pass, adding an obstacle mark; after the inspection unmanned aerial vehicle enters a low-altitude flight channel associated with the low-altitude traffic gate, first navigation track information corresponding to the non-free flight obstacle object is collected according to the obstacle mark; and carrying out movement prediction in a preset time period through the first navigation track information, and carrying out obstacle avoidance adjustment under the limitation of a dynamic obstacle safety distance in combination with second navigation track information corresponding to the inspection unmanned aerial vehicle. According to the invention, the technical problem of poor obstacle avoidance efficiency caused by difficulty in accurately identifying and predicting the dynamic obstacle in a low-altitude environment in the prior art is solved, and the inspection obstacle avoidance efficiency is improved through point cloud data comparison and dynamic obstacle prediction.

Description

Technical Field

[0001] This application relates to the field of inspection and obstacle avoidance technology, and in particular to inspection and obstacle avoidance methods and systems for low-altitude traffic checkpoints. Background Technology

[0002] Low-altitude flight areas typically contain various types of obstacles, including static structural obstacles such as buildings, cranes, and bridges, as well as dynamic obstacles such as other aircraft, birds, and weather changes. The high density and variety of obstacles make traditional obstacle recognition methods ill-suited to accurately address the complexity of the low-altitude environment. In low-altitude flight, especially during inspection missions, existing obstacle avoidance methods often rely on real-time sensor input when dealing with dynamic obstacles, lacking the ability to predict the future trajectory of obstacles. This results in untimely reactions from UAVs when encountering fast-moving obstacles, increasing the risk of collisions and reducing the efficiency of inspection missions.

[0003] In summary, existing technologies suffer from poor obstacle avoidance efficiency due to the difficulty in accurately identifying and predicting dynamic obstacles in low-altitude environments. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for inspection and obstacle avoidance at low-altitude traffic checkpoints, in order to solve the technical problem in the prior art that the obstacle avoidance efficiency is poor because it is difficult to accurately identify and predict dynamic obstacles in low-altitude environments.

[0005] In view of the above problems, this application provides a method and system for inspection and obstacle avoidance at low-altitude traffic checkpoints.

[0006] Firstly, this application provides a method for inspecting and avoiding obstacles at low-altitude traffic checkpoints. This method is implemented through a low-altitude traffic checkpoint inspection and obstacle avoidance system. The method includes: marking low-altitude traffic checkpoints in the target low-altitude airspace and collecting spatial point cloud data of the checkpoints; comparing the collected spatial point cloud data with pre-stored point cloud data; if the comparison fails, adding obstacle markers; after the inspection drone enters the low-altitude flight channel associated with the low-altitude traffic checkpoint, collecting first flight trajectory information corresponding to non-free-flying obstacles for the obstacle markers; performing movement prediction for a preset time period using the first flight trajectory information corresponding to the non-free-flying obstacles, and adjusting obstacle avoidance based on the second flight trajectory information corresponding to the inspection drone within a dynamic obstacle safety distance limit.

[0007] Optionally, the upper limit of the flight speed and the base value of the obstacle safety distance of the low-altitude flight channel associated with the low-altitude traffic checkpoint are obtained; the upper limit of the flight speed is used to predict the conflict between the non-free-flying obstacle and the inspection drone; if the conflict prediction of either the non-free-flying obstacle or the inspection drone fails, the dynamic obstacle safety distance is determined according to the base value of the obstacle safety distance.

[0008] Optionally, if the conflict prediction of both the non-free-flying obstacle and the inspection drone is passed, the obstacle safety distance base value is directly written into the dynamic obstacle safety distance.

[0009] Optionally, a safety distance compensation coefficient is determined based on the payload weight of the inspection drone; the obstacle safety distance base value is multiplied by the safety distance compensation coefficient to obtain the dynamic obstacle safety distance.

[0010] Optionally, environmental parameters in the target low-altitude airspace are obtained, including wind speed data and wind direction data; based on the environmental parameters in the target low-altitude airspace and the payload weight of the inspection drone, the safe distance compensation coefficient is updated.

[0011] Optionally, based on the environmental parameters in the target low-altitude airspace, the impact on the flight state of the inspection drone is analyzed to determine the influencing factors; based on the second flight trajectory information corresponding to the inspection drone, the trajectory deviation is extracted through the changes in environmental parameters and the influencing factors; and the dynamic obstacle safety distance is corrected using the trajectory deviation.

[0012] Optionally, a first distance tracking coordinate is added based on the first predicted flight trajectory information corresponding to the non-free-flying obstacle within the preset time period; a second distance tracking coordinate is added based on the second predicted flight trajectory information corresponding to the inspection drone within the preset time period; and the distance between the first distance tracking coordinate and the second distance tracking coordinate is compared with the dynamic obstacle safety distance.

[0013] Secondly, this application also provides a low-altitude traffic checkpoint inspection and obstacle avoidance system for executing the low-altitude traffic checkpoint inspection and obstacle avoidance method as described in the first aspect. The low-altitude traffic checkpoint inspection and obstacle avoidance system includes: a point cloud data acquisition module for marking low-altitude traffic checkpoints in the target low-altitude airspace and acquiring checkpoint spatial point cloud data; a data comparison module for comparing the acquired checkpoint spatial point cloud data with pre-stored point cloud data; if the comparison fails, an obstacle marker is added; an obstacle marking module for collecting first flight trajectory information corresponding to non-free-flying obstacle objects after the inspection drone enters the low-altitude flight channel associated with the low-altitude traffic checkpoint; and an obstacle avoidance adjustment module for predicting movement over a preset time period using the first flight trajectory information corresponding to the non-free-flying obstacle object, and adjusting obstacle avoidance within a dynamic obstacle safety distance limit in conjunction with the second flight trajectory information corresponding to the inspection drone.

[0014] One or more technical solutions provided in this application have at least the following beneficial effects:

[0015] By marking low-altitude traffic checkpoints in the target low-altitude airspace and collecting spatial point cloud data of the checkpoints, the collected checkpoint spatial point cloud data is compared with pre-stored point cloud data. If the comparison fails, obstacle markers are added. After the inspection drone enters the low-altitude flight channel associated with the low-altitude traffic checkpoint, the first flight trajectory information corresponding to the non-free-flying obstacle is collected for the obstacle markers. Movement prediction for a preset time period is performed using the first flight trajectory information corresponding to the non-free-flying obstacle, and obstacle avoidance adjustments are made within the dynamic obstacle safety distance limit, combined with the second flight trajectory information corresponding to the inspection drone. In other words, by marking traffic checkpoints in the low-altitude airspace, collecting spatial point cloud data, comparing it with pre-stored point cloud data, marking obstacles, collecting obstacle flight trajectory information, performing movement prediction, and combining it with the drone's flight trajectory, obstacle avoidance adjustments are made while ensuring a safe distance. This accurately identifies and predicts dynamic obstacles, improves the drone's inspection and obstacle avoidance efficiency, and thus improves the drone's safety in complex low-altitude environments.

[0016] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the inspection and obstacle avoidance method for low-altitude traffic checkpoints in this application.

[0019] Figure 2 This is a schematic diagram of the inspection and obstacle avoidance system for low-altitude traffic checkpoints in this application.

[0020] Figure labeling: Point cloud data acquisition module 11, data comparison module 12, obstacle marking module 13, obstacle avoidance adjustment module 14. Detailed Implementation

[0021] This application provides a method and system for obstacle avoidance at low-altitude traffic checkpoints, solving the technical problem of poor obstacle avoidance efficiency in existing technologies due to the difficulty in accurately identifying and predicting dynamic obstacles in low-altitude environments. By marking traffic checkpoints in low-altitude airspace and collecting spatial point cloud data, comparing it with pre-stored point cloud data, marking obstacles, collecting obstacle flight path information, predicting movement, and combining this with the drone's flight path, obstacle avoidance adjustments are made while ensuring a safe distance. This accurately identifies and predicts dynamic obstacles, improving the drone's obstacle avoidance efficiency and thus enhancing its safety in complex low-altitude environments.

[0022] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0023] Example 1, please refer to the appendix. Figure 1 This application provides a method for inspection and obstacle avoidance at low-altitude traffic checkpoints. The method is executed by a low-altitude traffic checkpoint inspection and obstacle avoidance system, and specifically includes the following steps:

[0024] S100: Mark low-altitude traffic checkpoints in the target low-altitude airspace and collect spatial point cloud data of the checkpoints.

[0025] Specifically, target low-altitude airspace refers to the flight area of ​​drones or other aircraft, typically at a low altitude (generally below 1000 meters), but the exact range depends on the flight mission and regional airspace restrictions. During low-altitude flight missions, target checkpoint locations within the low-altitude airspace are marked using a Geographic Information System (GIS) or flight mission planning system. Precise positioning and map data ensure accurate alignment between the marked checkpoints and the mission area. Low-altitude traffic checkpoints are checkpoints or monitoring points established during low-altitude flight to control and monitor low-altitude flight traffic, aircraft positions, and flight paths.

[0026] Once low-altitude traffic checkpoints are marked, sensors (such as LiDAR, depth cameras, or stereo vision systems) are used to collect spatial point cloud data of that location. These sensors are typically mounted on drones and, during flight, scan the ground and obstacles in the target area to acquire rich 3D point cloud data. During flight, the drone, equipped with a LiDAR or other depth sensor, flies over the target checkpoint and begins scanning the surrounding environment. The point cloud data provides the 3D coordinates (x, y, z) of each point, describing the distribution, size, and spatial layout of objects at that location. Through precise acquisition of point cloud data, detailed 3D information about the environment surrounding the low-altitude traffic checkpoint is obtained, improving the accuracy of the 3D environment model and further enhancing the ability to identify and predict obstacles.

[0027] S200: Compare the collected checkpoint spatial point cloud data with the pre-stored point cloud data. If the comparison fails, add obstacle markers.

[0028] Specifically, pre-stored point cloud data refers to reference point cloud data collected by high-precision sensors under similar environmental conditions, including the known locations and shapes of obstacles in the environment. This serves as a comparison benchmark to determine whether the collected real-time point cloud data contains anomalies or new obstacles. The collected checkpoint spatial point cloud data is compared with the pre-stored point cloud data. The comparison process includes two steps: first, the spatial error between the two sets of point clouds is calculated using an algorithm (such as the ICP algorithm, i.e., the Iterative Closest Point algorithm); second, the difference areas are analyzed to determine whether there are new obstacles or environmental changes. The ICP algorithm is used to find the optimal pairing between the two point cloud datasets. By progressively minimizing the spatial distance between the point clouds, their optimal alignment is determined. This method is suitable for processing point cloud data with a certain degree of noise.

[0029] If the comparison fails, it indicates the presence of a new obstacle in the environment. The system automatically identifies and marks this new obstacle. Obstacles are categorized into specific types, such as static obstacles (e.g., buildings, tower cranes) or dynamic obstacles (e.g., drones in flight, birds). For example, suppose a drone uses LiDAR to collect spatial data from a city's low-altitude traffic checkpoint during a power line inspection mission. The collected point cloud data includes surrounding fixed obstacles such as buildings and tower cranes. As the drone flies, by comparing the real-time point cloud data with previously stored point cloud data, a new obstacle is detected during the comparison process (e.g., another drone appearing in its flight path). The drone is identified and marked as a dynamic obstacle by identifying the area where the comparison fails. By comparing point cloud data in real time, changes in the low-altitude flight environment, especially newly appearing obstacles, are accurately identified, preventing potential collisions and improving the safety and efficiency of flight missions.

[0030] S300: After the inspection drone enters the low-altitude flight channel associated with the low-altitude traffic checkpoint, it collects the first flight trajectory information corresponding to the non-free-flying obstacle object based on the obstacle mark.

[0031] Specifically, inspection drones are drones used for inspection purposes. They typically possess high-precision sensors and long endurance, and are used for low-altitude flight missions to inspect low-altitude traffic checkpoints and detect obstacles. Inspection drones fly into predetermined low-altitude flight paths, which may contain various obstacles, including both static and dynamic ones. The flight altitude and speed of inspection drones are usually influenced by low-altitude flight regulations and mission requirements.

[0032] Once the inspection drone enters the low-altitude flight path, it detects and identifies any non-free-flying obstacles (such as tower cranes, buildings, or other aircraft). While tower cranes and construction equipment do not possess the ability to fly freely, they may experience some form of movement due to external factors. Other aircraft typically fly along fixed routes.

[0033] Inspection drones collect initial flight trajectory information for non-free-flying obstacles using onboard sensors (such as cameras and lidar). For example, assuming a drone enters a flight path and detects a moving obstacle (such as another drone) using lidar, it will collect data on the obstacle's position, speed, and direction. Specifically, the drone enters a low-altitude flight path; sensors detect and mark obstacles; data on the obstacle's position, speed, and direction are collected; and based on this data, the obstacle's trajectory is calculated.

[0034] By predicting the dynamic trajectories of non-free-flying obstacles (such as tower cranes, construction equipment, and other aircraft), the risk of collisions caused by sudden obstacles is eliminated. At the same time, the flight path is optimized, and flight interruptions caused by untimely obstacle avoidance response are reduced, thereby improving the overall efficiency and safety of inspection tasks.

[0035] S400: Based on the first flight trajectory information corresponding to the non-free-flying obstacle, a movement prediction is performed for a preset time period. Combined with the second flight trajectory information corresponding to the inspection drone, obstacle avoidance adjustments are made under the dynamic obstacle safety distance limit.

[0036] Specifically, based on the collected initial trajectory information of the obstacle, the future trajectory of the obstacle is predicted within a preset time period. The movement prediction within the preset time period is based on the collected obstacle trajectory information and predicts movement within a certain time range. Historical data (such as speed, acceleration, and changes in direction) is used to estimate the obstacle's possible future location. For example, if a tower crane boom has moved at a certain speed in a certain direction over the past few seconds, its location in the next few seconds is predicted.

[0037] Simultaneously, the system acquires the second flight trajectory information of the inspection drone, namely the drone's current flight path and dynamic status, including speed, altitude, and heading, which accurately reflects the drone's current position and next flight direction. After obtaining the predicted trajectory of the non-free-flying obstacle and the current trajectory information of the inspection drone, a dynamic safety distance is calculated and adjusted in real time based on the obstacle's movement speed, the drone's flight speed, and the type of obstacle. For example, if the obstacle moves quickly, the safety distance is increased; if the obstacle moves slowly or is far away, the safety distance can be decreased.

[0038] If it is predicted that the distance between the inspection drone and an obstacle will become too close at some point in the future, exceeding the dynamic safe distance limit, obstacle avoidance adjustment will be automatically triggered. Based on the prediction results, the drone's flight path, heading, or altitude will be adjusted to avoid potential collision points, thereby ensuring the drone's safe passage. Through accurate obstacle trajectory prediction and dynamic safe distance calculation, drones can more accurately avoid potential collision risks, especially in complex low-altitude environments.

[0039] Furthermore, this application S400 includes:

[0040] Obtain the upper limit of the flight speed and the base value of the obstacle safety distance of the low-altitude flight channel associated with the low-altitude traffic checkpoint; use the upper limit of the flight speed to make a conflict prediction for the non-free-flying obstacle object and the inspection drone; if the conflict prediction for either the non-free-flying obstacle object or the inspection drone fails, determine the dynamic obstacle safety distance based on the base value of the obstacle safety distance.

[0041] Specifically, the upper speed limit for low-altitude flight corridors associated with low-altitude traffic checkpoints is typically provided by the flight management system or air traffic control center to ensure that aircraft do not exceed the speed limit, thereby avoiding flight risks caused by excessive speed. The upper speed limit is the prescribed flight speed limit within the low-altitude flight corridor. Within this corridor, both inspection drones and non-free-flying obstacles (such as tower cranes and buildings) must adhere to this speed limit. Simultaneously, the baseline value for obstacle safety distances associated with the flight corridor is obtained, set based on experience and safety standards, typically determined by the aircraft type, flight altitude, and flight environment. Non-free-flying obstacles are usually objects that do not fly autonomously or whose flight is restricted, such as buildings and tower cranes.

[0042] Based on the maximum flight speed, combined with the motion state of non-free-flying obstacles, the flight speed and direction of the inspection drone, conflict prediction is calculated. For example, if the inspection drone's flight speed approaches or exceeds the maximum speed limit of the passage, or if an obstacle is rapidly approaching the drone's path, potential collision risks can be identified in advance.

[0043] If a potential collision risk is detected between the inspection drone and a non-free-flying obstacle during the conflict prediction process, the dynamic obstacle safety distance is adjusted based on the obstacle safety distance baseline. In other words, if the conflict prediction for either the non-free-flying obstacle or the inspection drone fails, it indicates a potential collision risk, and the dynamic obstacle safety distance is adjusted according to the obstacle safety distance baseline. This adjustment is typically based on the obstacle's dynamic behavior (such as acceleration or deceleration) and the drone's current position and flight trajectory. The safety distance is automatically adjusted by analyzing the relative speed and direction of the aircraft and the obstacle. For example, if an obstacle is rapidly approaching the drone, the dynamic safety distance is increased to provide more obstacle avoidance space.

[0044] In a real-time dynamic environment, the relative positions of obstacles and drones are constantly changing. Calculating the dynamic obstacle safety distance requires predicting the flight status over the next few seconds. If the calculated distance between the drone's current path and the obstacle is less than the dynamic safety distance, an obstacle avoidance signal is issued, and the drone's flight path is adjusted to avoid a collision. By limiting flight speed and adjusting the dynamic safety distance, collisions between the aircraft and obstacles are effectively avoided, especially in complex low-altitude environments, ensuring flight safety.

[0045] Furthermore, this application also includes the following steps:

[0046] If the conflict predictions for both the non-free-flying obstacle and the inspection drone are successful, the obstacle safety distance base value is directly written into the dynamic obstacle safety distance.

[0047] Specifically, if the conflict predictions for both non-free-flying obstacles and inspection drones pass, indicating that the paths of the drone and non-free-flying obstacles will not conflict, the baseline obstacle safety distance is directly used as the dynamic obstacle safety distance. In other words, even if the obstacle and the drone are in relatively close positions, since the prediction results indicate no collision, no further adjustment to the safety distance is needed; the baseline safety distance can be maintained. For example, suppose the baseline obstacle safety distance for a low-altitude flight channel is set at 10 meters. If the dynamic information of the inspection drone and obstacles such as tower cranes indicates that their relative positions and speeds do not conflict, 10 meters is directly used as the dynamic obstacle safety distance without additional adjustment. When the conflict prediction passes, directly using the baseline obstacle safety distance as the dynamic obstacle safety distance avoids complex dynamic adjustment calculations and simplifies the computational burden on the flight control system.

[0048] Furthermore, this application also includes the following steps:

[0049] Based on the payload weight of the inspection drone, a safety distance compensation coefficient is determined; the obstacle safety distance base value is multiplied by the safety distance compensation coefficient to obtain the dynamic obstacle safety distance.

[0050] The environmental parameters in the target low-altitude airspace are obtained, including wind speed data and wind direction data; based on the environmental parameters in the target low-altitude airspace and the payload weight of the inspection drone, the safe distance compensation coefficient is updated.

[0051] Based on the environmental parameters in the target low-altitude airspace, the impact on the flight state of the inspection UAV is analyzed, and the influencing factors are determined. Based on the second flight trajectory information corresponding to the inspection UAV, the trajectory deviation is extracted by the changes in environmental parameters and the influencing factors. The dynamic obstacle safety distance is corrected using the trajectory deviation.

[0052] Specifically, the payload weight of an inspection drone refers to the total weight of its effective payload (such as cameras, sensors, and lidar), which affects its flight performance, particularly flight stability and speed. The safety distance compensation coefficient, determined based on the drone's payload weight, is a dynamically adjusted value. A heavier payload may result in poorer stability and slower response, thus requiring a larger safety distance. For example, if the inspection drone's payload weight is 5 kg and the compensation coefficient is 1.2, the corresponding safety distance will increase by 20%. If the payload is 10 kg, the compensation coefficient may increase to 1.5, increasing the safety distance by 50%.

[0053] Acquire environmental data such as wind speed and direction, and analyze how this data affects the drone's flight to obtain influencing factors. Influencing factors refer to factors that cause deviations in the drone's flight trajectory under specific environmental conditions, including wind speed, wind direction, airflow, and temperature changes. Any change can affect the flight trajectory. Excessive wind speed can cause flight trajectory deviations, and changes in wind direction can affect the aircraft's directional stability. For example, assuming a wind speed of 15 m / s and a wind direction at a 30-degree angle to the aircraft's heading, the drone may experience lateral drift during flight, requiring dynamic correction to account for this effect.

[0054] Based on the second flight trajectory information corresponding to the inspection drone, the trajectory deviation is extracted through changes in environmental parameters and influencing factors. This deviation reflects the impact of these changes on the drone, causing a discrepancy between the drone's current position and the preset trajectory. The trajectory deviation is the difference between the actual flight trajectory and the preset trajectory. Trajectory deviation can be caused by various factors, such as changes in wind speed and payload center of gravity, and is used to measure the degree of deviation from the flight path.

[0055] The dynamic obstacle safety distance is adjusted based on the trajectory deviation and payload weight. For example, if the aircraft deviates due to wind speed and the deviation exceeds the predetermined safety range, the safety distance needs to be adjusted according to the compensation coefficient, increasing the distance between the drone and the obstacle; that is, the safety distance compensation coefficient is updated. For example, assuming the trajectory deviation is 3 meters, the base safety distance is 10 meters, and the compensation coefficient is 1.5, the dynamic obstacle safety distance needs to be corrected to 15 meters.

[0056] The dynamic obstacle safety distance is obtained by multiplying the baseline obstacle safety distance by the updated safety distance compensation coefficient. The safety distance compensation coefficient typically considers factors such as the aircraft's load, environmental factors (e.g., wind speed, wind direction), and flight trajectory deviations. By multiplying the baseline safety distance by the compensation coefficient, a dynamic obstacle safety distance that is more adapted to the current environmental conditions and flight status can be obtained. For example, suppose that in a certain inspection mission, the basic safety distance (baseline obstacle safety distance) is set to 10 meters. Based on the aircraft's load weight (6 kg), environmental data (e.g., wind speed 12 m / s, wind direction deviation, etc.), and a safety distance compensation coefficient of 1.3, then the dynamic obstacle safety distance is 13 meters.

[0057] By dynamically adjusting the safety distance, drones can maintain a sufficient safe distance in changing flight environments, effectively avoiding the risk of collision. The safety distance compensation coefficient is adjusted according to actual environmental conditions (such as wind speed and wind direction) to ensure that drones maintain safe flight in ever-changing environments, especially improving obstacle avoidance capabilities in complex low-altitude environments.

[0058] Furthermore, this application also includes the following steps:

[0059] A first distance tracking coordinate is added based on the first predicted flight trajectory information corresponding to the non-free-flying obstacle within the preset time period; a second distance tracking coordinate is added based on the second predicted flight trajectory information corresponding to the inspection drone within the preset time period; and the distance between the first distance tracking coordinate and the second distance tracking coordinate is compared with the dynamic obstacle safety distance.

[0060] Specifically, within a preset time period, the flight trajectories of the obstacle and the drone are predicted based on the motion patterns of the non-free-flying obstacle and the flight state of the aircraft. Based on the aforementioned predicted flight trajectory information, the expected positions (coordinates) of the obstacle and the drone within the preset time period are calculated, resulting in first and second distance tracking coordinates. The first distance tracking coordinate is the expected position coordinate of the obstacle at a certain moment, calculated based on the first predicted flight trajectory information (trajectory prediction of the non-free-flying obstacle). The second distance tracking coordinate is the expected position coordinate of the drone at a certain moment, calculated based on the second predicted flight trajectory information (trajectory prediction of the inspection drone). For example, within the preset time period, if the trajectory of the non-free-flying obstacle is predicted to reach a certain position (x1, y1, z1) in 10 seconds, and the trajectory of the inspection drone is predicted to reach a position (x2, y2, z2) at the same time, then these two position coordinates are the first and second distance tracking coordinates.

[0061] The distance between the obstacle and the drone within a preset time period is calculated, and it is determined whether this distance is less than the dynamic obstacle safety distance. If it is less, it indicates a potential collision risk, requiring adjustments to the flight path for obstacle avoidance. The Euclidean distance formula (or other distance measurement methods) is typically used to calculate the spatial distance between the two at a specific moment. The dynamic obstacle safety distance is a dynamically adjusted safety distance based on information such as the aircraft's load, environmental factors, and flight trajectory, ensuring sufficient obstacle avoidance space between the aircraft and the obstacle. By predicting the future positions of the obstacle and the inspection drone, potential collision risks can be identified in advance, allowing for obstacle avoidance adjustments. This enables the aircraft to react before a collision occurs, reducing real-time response time and improving obstacle avoidance efficiency.

[0062] In summary, the obstacle avoidance method for low-altitude traffic checkpoints provided in this application has the following beneficial effects:

[0063] By marking low-altitude traffic checkpoints in the target low-altitude airspace and collecting spatial point cloud data of the checkpoints, the collected checkpoint spatial point cloud data is compared with pre-stored point cloud data. If the comparison fails, obstacle markers are added. After the inspection drone enters the low-altitude flight channel associated with the low-altitude traffic checkpoint, the first flight trajectory information corresponding to the non-free-flying obstacle is collected for the obstacle markers. Movement prediction for a preset time period is performed using the first flight trajectory information corresponding to the non-free-flying obstacle, and obstacle avoidance adjustments are made within the dynamic obstacle safety distance limit, combined with the second flight trajectory information corresponding to the inspection drone. In other words, by marking traffic checkpoints in the low-altitude airspace, collecting spatial point cloud data, comparing it with pre-stored point cloud data, marking obstacles, collecting obstacle flight trajectory information, performing movement prediction, and combining it with the drone's flight trajectory, obstacle avoidance adjustments are made while ensuring a safe distance. This accurately identifies and predicts dynamic obstacles, improves the drone's inspection and obstacle avoidance efficiency, and thus improves the drone's safety in complex low-altitude environments.

[0064] Example 2: Based on the same inventive concept as the low-altitude traffic checkpoint inspection and obstacle avoidance method in Example 1, this application also provides a low-altitude traffic checkpoint inspection and obstacle avoidance system. Please refer to the appendix. Figure 2 The low-altitude traffic checkpoint inspection and obstacle avoidance system includes:

[0065] The point cloud data acquisition module 11 is used to mark low-altitude traffic checkpoints in the target low-altitude airspace and collect spatial point cloud data of the checkpoints; the data comparison module 12 is used to compare the collected spatial point cloud data of the checkpoints with the pre-stored point cloud data. If the comparison fails, an obstacle mark is added; the obstacle mark module 13 is used to collect the first flight trajectory information corresponding to the non-free-flying obstacle object after the inspection drone enters the low-altitude flight channel associated with the low-altitude traffic checkpoint, based on the obstacle mark; the obstacle avoidance adjustment module 14 is used to predict the movement of the non-free-flying obstacle object within a preset time period based on the first flight trajectory information corresponding to the non-free-flying obstacle object, and to adjust the obstacle avoidance under the dynamic obstacle safety distance limit in combination with the second flight trajectory information corresponding to the inspection drone.

[0066] Furthermore, the obstacle avoidance adjustment module 14 in the low-altitude traffic checkpoint inspection and obstacle avoidance system is also used for:

[0067] Obtain the upper limit of the flight speed and the base value of the obstacle safety distance of the low-altitude flight channel associated with the low-altitude traffic checkpoint; use the upper limit of the flight speed to make a conflict prediction for the non-free-flying obstacle object and the inspection drone; if the conflict prediction for either the non-free-flying obstacle object or the inspection drone fails, determine the dynamic obstacle safety distance based on the base value of the obstacle safety distance.

[0068] Furthermore, the obstacle avoidance adjustment module 14 in the low-altitude traffic checkpoint inspection and obstacle avoidance system is also used for:

[0069] If the conflict predictions for both the non-free-flying obstacle and the inspection drone are successful, the obstacle safety distance base value is directly written into the dynamic obstacle safety distance.

[0070] Furthermore, the obstacle avoidance adjustment module 14 in the low-altitude traffic checkpoint inspection and obstacle avoidance system is also used for:

[0071] Based on the payload weight of the inspection drone, a safety distance compensation coefficient is determined; the obstacle safety distance base value is multiplied by the safety distance compensation coefficient to obtain the dynamic obstacle safety distance.

[0072] Furthermore, the obstacle avoidance adjustment module 14 in the low-altitude traffic checkpoint inspection and obstacle avoidance system is also used for:

[0073] The environmental parameters in the target low-altitude airspace are obtained, including wind speed data and wind direction data; based on the environmental parameters in the target low-altitude airspace and the payload weight of the inspection drone, the safe distance compensation coefficient is updated.

[0074] Furthermore, the obstacle avoidance adjustment module 14 in the low-altitude traffic checkpoint inspection and obstacle avoidance system is also used for:

[0075] Based on the environmental parameters in the target low-altitude airspace, the impact on the flight state of the inspection UAV is analyzed, and the influencing factors are determined. Based on the second flight trajectory information corresponding to the inspection UAV, the trajectory deviation is extracted by the changes in environmental parameters and the influencing factors. The dynamic obstacle safety distance is corrected using the trajectory deviation.

[0076] Furthermore, the obstacle avoidance adjustment module 14 in the low-altitude traffic checkpoint inspection and obstacle avoidance system is also used for:

[0077] A first distance tracking coordinate is added based on the first predicted flight trajectory information corresponding to the non-free-flying obstacle within the preset time period; a second distance tracking coordinate is added based on the second predicted flight trajectory information corresponding to the inspection drone within the preset time period; and the distance between the first distance tracking coordinate and the second distance tracking coordinate is compared with the dynamic obstacle safety distance.

[0078] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1 The inspection and obstacle avoidance method and specific examples of the low-altitude traffic checkpoint in Embodiment 1 are also applicable to the inspection and obstacle avoidance system of the low-altitude traffic checkpoint in this embodiment. Through the foregoing detailed description of the inspection and obstacle avoidance method of the low-altitude traffic checkpoint, those skilled in the art can clearly understand the inspection and obstacle avoidance system of the low-altitude traffic checkpoint in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0079] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0080] Obviously, those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for inspecting and avoiding obstacles at low-altitude traffic checkpoints, characterized in that, include: Mark low-altitude traffic checkpoints in the target low-altitude airspace and collect spatial point cloud data of the checkpoints; The collected checkpoint spatial point cloud data is compared with the pre-stored point cloud data. If the comparison fails, obstacle markers are added. After the inspection drone enters the low-altitude flight channel associated with the low-altitude traffic checkpoint, it collects the first flight trajectory information corresponding to the non-free-flying obstacle object based on the obstacle mark. The movement of the non-free-flying obstacle is predicted within a preset time period by using the first flight trajectory information corresponding to the obstacle, and obstacle avoidance adjustment is made under the dynamic obstacle safety distance limit by combining the second flight trajectory information corresponding to the inspection drone.

2. The inspection and obstacle avoidance method for low-altitude traffic checkpoints as described in claim 1, characterized in that, Based on the second flight trajectory information corresponding to the inspection drone, obstacle avoidance adjustments are made within the dynamic obstacle safety distance limit, including: Obtain the upper limit of flight speed and the base value of obstacle safety distance for the low-altitude flight channel associated with the low-altitude traffic checkpoint; Based on the upper limit of the flight speed, conflict prediction is made for the non-free-flying obstacle objects and the inspection drone; If the conflict prediction for either the non-free-flying obstacle or the inspection drone fails, the dynamic obstacle safety distance is determined based on the obstacle safety distance baseline.

3. The inspection and obstacle avoidance method for low-altitude traffic checkpoints as described in claim 2, characterized in that, If the conflict predictions for both the non-free-flying obstacle and the inspection drone are successful, the obstacle safety distance base value is directly written into the dynamic obstacle safety distance.

4. The inspection and obstacle avoidance method for low-altitude traffic checkpoints as described in claim 3, characterized in that, Determining the dynamic obstacle safety distance based on the obstacle safety distance baseline includes: Based on the payload weight of the inspection drone, a safety distance compensation coefficient is determined; The dynamic obstacle safety distance is obtained by multiplying the obstacle safety distance base value by the safety distance compensation coefficient.

5. The inspection and obstacle avoidance method for low-altitude traffic checkpoints as described in claim 4, characterized in that, Based on the payload weight of the inspection drone, the safe distance compensation coefficient is determined, which also includes: Acquire environmental parameters in the target low-altitude airspace, including wind speed data and wind direction data; The safe distance compensation coefficient is updated based on the environmental parameters in the target low-altitude airspace and the payload weight of the inspection drone.

6. The inspection and obstacle avoidance method for low-altitude traffic checkpoints as described in claim 5, characterized in that, Based on the environmental parameters in the target low-altitude airspace and the payload weight of the inspection drone, the safety distance compensation coefficient is updated, including: Based on the environmental parameters in the target low-altitude airspace, analyze the impact on the flight status of the inspection UAV and determine the influencing factors; Based on the second flight trajectory information corresponding to the inspection drone, the trajectory deviation is extracted by the changes in environmental parameters and the influencing factors. The dynamic obstacle safety distance is corrected based on the trajectory deviation.

7. The inspection and obstacle avoidance method for low-altitude traffic checkpoints as described in claim 1, characterized in that, Obstacle avoidance adjustments are made within the dynamic obstacle safety distance limit, including: Add first distance tracking coordinates to the first predicted flight trajectory information corresponding to the non-free-flying obstacle object within the preset time period; A second distance tracking coordinate is added based on the second predicted flight trajectory information corresponding to the inspection drone within the preset time period; The distance between the first distance tracking coordinate and the second distance tracking coordinate is compared with the dynamic obstacle safety distance.

8. A patrol and obstacle avoidance system for low-altitude traffic checkpoints, characterized in that, The steps for implementing the inspection and obstacle avoidance method for low-altitude traffic checkpoints according to any one of claims 1 to 7, wherein the low-altitude traffic checkpoint inspection and obstacle avoidance system comprises: The point cloud data acquisition module is used to mark low-altitude traffic checkpoints in the target low-altitude airspace and collect spatial point cloud data of the checkpoints; The data comparison module is used to compare the collected checkpoint spatial point cloud data with the pre-stored point cloud data. If the comparison fails, obstacle markers are added. The obstacle marking module is used to collect first flight trajectory information corresponding to non-free-flying obstacle objects after the inspection drone enters the low-altitude flight channel associated with the low-altitude traffic checkpoint. The obstacle avoidance adjustment module is used to predict the movement of the non-free-flying obstacle object within a preset time period by using the first flight trajectory information corresponding to the obstacle, and to adjust the obstacle avoidance by combining the second flight trajectory information corresponding to the inspection drone under the dynamic obstacle safety distance limit.

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