Obstacle avoidance control method and system of aircraft

Abstract

The invention is suitable for the technical field of aircraft obstacle avoidance, and particularly relates to an aircraft obstacle avoidance control method and system, and the method comprises the steps: delimiting a flight region of an aircraft, collecting the stereoscopic optical remote sensing data of the flight region, drawing an obstacle recognition graph, marking the position of an obstacle, and configuring the color of the obstacle. Wherein one obstacle at least corresponds to one color, calculating the height of each obstacle, and marking the height in the obstacle identification graph; and segmenting the height into a plurality of layers, and creating branch nodes and leaf nodes. By constructing the obstacle avoidance tree, the color data of all obstacles can be visually displayed, and the color image can be quickly verified, so that the flight safety is greatly improved while the normal flight of the aircraft is ensured, the suspicious obstacles on the flight path can be verified by extracting the RGB values, the flight path planning is optimized, and the flight path planning efficiency is improved. And the flight safety is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of aircraft obstacle avoidance, and in particular to an obstacle avoidance control method and system for an aircraft. Background Art

[0002] Aircraft include airplanes, helicopters, drones, detectors, etc. The obstacle avoidance of aircraft mainly relies on the collaborative work of sensors, algorithms, and automatic control systems. Common sensors include radar, lidar, infrared detectors, cameras, and ultrasonic sensors. The sensors can sense the surrounding environment in real time and detect obstacles. The obstacle avoidance algorithm adjusts the planned path through data analysis and processing and calculates a safe flight route.

[0003] However, many small consumer aircraft do not have advanced obstacle avoidance sensors and only rely on GPS or altitude-holding modules for basic flight control, that is, flying according to a predetermined route. Such an obstacle avoidance method has poor adaptability to real-time environmental changes and cannot cope with sudden obstacles.

[0004] Compared with lidar, infrared detectors, ultrasonic sensors, etc., RGB sensors have a lower cost, can provide high-resolution and rich visual information, and are easy to integrate into aircraft; therefore, "how to use RGB sensors for obstacle avoidance" is the technical problem to be solved by the present invention. Summary of the Invention

[0005] The purpose of the present invention is to provide an obstacle avoidance control method and system for an aircraft to solve the problem of "how to use RGB sensors for obstacle avoidance" proposed in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] An obstacle avoidance control method for an aircraft, the method includes:

[0008] Define the flight area of the aircraft, collect the stereo optical remote sensing data of the flight area, draw an obstacle recognition map, mark the positions of the obstacles, configure the colors of the obstacles, where at least one color corresponds to one obstacle, calculate the height of each obstacle, and mark it on the obstacle recognition map;

[0009] Divide the height into several layers, create branch nodes and leaf nodes, and mount the leaf nodes into the branch nodes. Upload the obstacles in each layer to the branch nodes, upload the colors to the leaf nodes, and integrate the branch nodes and leaf nodes to generate an obstacle avoidance tree;

[0010] In the obstacle recognition map, traverse the detour points, establish the correspondence between the detour points and the obstacles to generate child nodes, and mount the child nodes into the leaf nodes via the correspondence;

[0011] Collect the flight altitude of the aircraft, configure the maximum activity altitude, find all leaf nodes at the maximum activity altitude to obtain target nodes, collect color images on the flight path via an RGB sensor pre-integrated in the aircraft, extract RGB values, and use the RGB values to traverse the target nodes in sequence to determine whether there are the same colors in the target nodes. If so, find the branch nodes corresponding to the same colors, define suspicious obstacles, obtain the real-time position of the aircraft, find the suspicious obstacles located on the flight path to obtain target obstacles, find the child nodes corresponding to the target obstacles to obtain waypoints for bypassing, and use the waypoints for bypassing to offset the preset flight path.

[0012] Further, the step of drawing an obstacle recognition map and marking the positions of obstacles includes:

[0013] Obtain the flight mission of the aircraft and set flight parameters;

[0014] With the position as the center and a preset distance as the radius, construct an electronic fence, embed standard parameters into the electronic fence, and correct the flight parameters.

[0015] Further, the step of marking the positions of obstacles and configuring the colors of obstacles includes:

[0016] Divide the flight mission into several stage missions;

[0017] Select an identification color from the colors, create a correspondence between the identification color and the stage mission, and start the stage mission when the color image is the same as the identification color.

[0018] Further, the step of dividing the altitude into several layers includes:

[0019] Configure the altitude range of each layer, calculate the density of obstacles in the flight area, and dynamically adjust the altitude range based on the density;

[0020] Create a priority queue, select potential obstacles on the flight path, establish a link between the potential obstacles and the branch nodes, construct a priority query mechanism, and integrate the priority queue and the priority query mechanism into the obstacle avoidance tree.

[0021] Further, the step of traversing waypoints for bypassing in the obstacle recognition map and establishing a correspondence between the waypoints for bypassing and the obstacles includes:

[0022] Collect the influencing factors of the flight path, where the influencing factors at least include meteorology and wind zones, and set thresholds for the influencing factors;

[0023] When the real-time value of the influencing factor is greater than the threshold, send an adjustment request to the user, receive the backup points uploaded by the user, and overwrite the detour points.

[0024] Further, the steps of obtaining the real-time position of the aircraft, finding the suspicious obstacles on the flight path to obtain the target obstacles, finding the child nodes corresponding to the target obstacles to obtain the detour points, and using the detour points to offset the preset flight path include:

[0025] Locate the edge devices on the flight path, divide the flight path into several flight segments, and establish a communication link between the edge devices and the aircraft;

[0026] Create an obstacle avoidance tree corresponding to each flight segment and deploy the obstacle avoidance tree to the corresponding edge device.

[0027] Further, the method further includes:

[0028] Configure the parameters of each obstacle, where the parameters at least include: shape, size, and depth;

[0029] Use the sensing devices pre-integrated in the aircraft to collect the sensing data on the flight path and verify the parameters of the target obstacles.

[0030] Further, the system includes:

[0031] A marking module, used to delimit the flight area of the aircraft, collect the three-dimensional optical remote sensing data of the flight area, draw an obstacle recognition map, mark the positions of the obstacles, configure the colors of the obstacles, where at least one color corresponds to one obstacle, calculate the height of each obstacle, and mark it on the obstacle recognition map;

[0032] A generation module, used to divide the height into several layers, create branch nodes and leaf nodes, mount the leaf nodes to the branch nodes, upload the obstacles in each layer to the branch nodes, upload the colors to the leaf nodes, integrate the branch nodes and leaf nodes, and generate an obstacle avoidance tree;

[0033] A mounting module, used to traverse the detour points in the obstacle recognition map, establish the corresponding relationship between the detour points and the obstacles, generate child nodes, and mount the child nodes to the leaf nodes via the corresponding relationship;

[0034] An offset module is used to collect the flight altitude of the aircraft, configure the maximum activity altitude, find all the leaf nodes at the maximum activity altitude to obtain target nodes, collect color images on the flight path via an RGB sensor pre-integrated in the aircraft, extract the RGB values, and sequentially traverse the target nodes using the RGB values to determine whether there are the same colors in the target nodes. If so, find the branch nodes corresponding to the same colors, define suspicious obstacles, obtain the real-time position of the aircraft, find the suspicious obstacles located on the flight path to obtain target obstacles, find the child nodes corresponding to the target obstacles to obtain detour points, and offset the preset flight path using the detour points.

[0035] Furthermore, the annotation module includes:

[0036] A setting unit is used to obtain the flight mission of the aircraft and set flight parameters;

[0037] A correction unit is used to construct an electronic fence with the position as the center and a preset distance as the radius, embed standard parameters into the electronic fence, and correct the flight parameters;

[0038] A splitting unit is used to split the flight mission into several stage missions;

[0039] A starting unit is used to select a marking color from the colors, create a correspondence between the marking color and the stage mission, and start the stage mission when the color image is the same as the marking color.

[0040] Furthermore, the generation module includes:

[0041] An adjustment unit is used to configure the height range of each layer, calculate the density of obstacles in the flight area, and dynamically adjust the height range based on the density;

[0042] An integration unit is used to create a priority queue, select potential obstacles on the flight path, establish a link between the potential obstacles and the branch nodes, construct a priority query mechanism, and integrate the priority queue and the priority query mechanism into the obstacle avoidance tree.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] By defining the flight area, the flight path can be optimized and flight safety can be improved. By drawing an obstacle recognition map, a data basis for the aircraft to avoid obstacles can be provided, reducing the occurrence of flight accidents. By determining the color of the obstacles, the actual positions of the obstacles can be quickly verified, ensuring that the aircraft can respond promptly and avoid collisions with the obstacles. By constructing an obstacle avoidance tree, the color data of all obstacles can be visually displayed, and the obstacles can be quickly verified through color images, thus greatly improving flight safety while ensuring the normal flight of the aircraft. By extracting the RGB values, the suspicious obstacles on the flight path can be verified, optimizing the flight path planning and further improving flight safety. Description of the Drawings

[0045] Figure 1 Schematic diagram of an example of the obstacle avoidance tree provided by an embodiment of the present invention;

[0046] Figure 2 Flow chart of the obstacle avoidance control method for an aircraft provided by an embodiment of the present invention;

[0047] Figure 3 First sub - flow chart of the obstacle avoidance control method for an aircraft provided by an embodiment of the present invention;

[0048] Figure 4 Second sub - flow chart of the obstacle avoidance control method for an aircraft provided by an embodiment of the present invention;

[0049] Figure 5 Third sub - flow chart of the obstacle avoidance control method for an aircraft provided by an embodiment of the present invention;

[0050] Figure 6 Fourth sub - flow chart of the obstacle avoidance control method for an aircraft provided by an embodiment of the present invention;

[0051] Figure 7 Block diagram of the composition of the obstacle avoidance control system for an aircraft provided by an embodiment of the present invention;

[0052] Figure 8 Block diagram of the composition of the annotation module in the obstacle avoidance control system for an aircraft provided by an embodiment of the present invention;

[0053] Figure 9 Block diagram of the composition of the generation module in the obstacle avoidance control system for an aircraft provided by an embodiment of the present invention;

[0054] Figure 10 Block diagram of the composition of the mounting module in the obstacle avoidance control system for an aircraft provided by an embodiment of the present invention;

[0055] Figure 11 Block diagram of the composition of the offset module in the obstacle avoidance control system for an aircraft provided by an embodiment of the present invention. Detailed implementation manners

[0056] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0057] In Embodiment 1, Figure 1 and Figure 2 The implementation process of the obstacle avoidance control method for the aircraft provided by the embodiment of the present invention is shown. The following is a detailed description, as follows:

[0058] S100: Define the flight area of the aircraft, collect the three-dimensional optical remote sensing data of the flight area, generate an obstacle recognition map, mark the positions of the obstacles, configure the colors of the obstacles, where at least one color corresponds to one obstacle, calculate the height of each obstacle, and mark it on the obstacle recognition map.

[0059] According to the flight mission, define several boundaries, connect all the boundaries to determine the flight area, and use optical remote sensing technology to collect high-resolution three-dimensional data in the flight area, that is, three-dimensional optical remote sensing data; among them, the three-dimensional optical remote sensing data can also be obtained from public data, and the three-dimensional optical remote sensing data can provide detailed spatial information of the terrain, buildings, trees and other potential obstacles in the flight area; integrate the three-dimensional optical remote sensing data to generate an obstacle recognition map, and mark the positions, shapes and sizes of all identified obstacles in it.

[0060] Extract the color images of each obstacle from the three-dimensional optical remote sensing data to generate an RGB image, and extract the colors of the obstacles from the red, green and blue bands, where at least one color exists for each obstacle; for example, the leaves of trees generally appear green in the RGB band, while the tree trunks may show brown or gray; in other words, in this embodiment, the colors of the obstacles are green, brown and gray; it should be noted that the colors in this embodiment are represented by RGB values.

[0061] Determine the height of each obstacle through the multi-view data in the three-dimensional optical remote sensing data or from public data, and mark the height and shape data on the obstacle recognition map.

[0062] S200: Divide the height into several layers, create branch nodes and leaf nodes, mount the leaf nodes to the branch nodes, upload the obstacles in each layer to the branch nodes, upload the colors to the leaf nodes, and integrate the branch nodes and leaf nodes to generate an obstacle avoidance tree.

[0063] According to the maximum flight altitude of the aircraft, several layers are segmented; for example, if the maximum flight altitude of a certain aircraft is 500 meters, then five layers can be segmented: 0 - 30 meters, 30 - 50 meters, 50 - 100 meters, 100 - 200 meters, and 200 - 500 meters; the advantage of doing this is that it can optimize obstacle detection and accurately plan the flight path; in this application, if the height of an obstacle is 65 meters, then its corresponding layer is 50 - 100 meters.

[0064] Create branch nodes and leaf nodes. Here, the node is the basic unit that makes up the obstacle avoidance tree, which can store a small amount of data and has no data processing ability; specifically, the branch node is mainly used to represent the obstacle, and the leaf node is used to represent the color corresponding to the obstacle (as shown in the attached instructions Figure 1 ). Upload the obstacles in each layer to the branch nodes, upload the color corresponding to each obstacle to the leaf nodes, mount the leaf nodes to the branch nodes, and integrate them to generate the obstacle avoidance tree; the obstacle avoidance tree is similar to a "tree" in nature. The branch nodes are mounted in the obstacle avoidance tree in ascending order. By setting up the obstacle avoidance tree, it can intuitively display data such as the distribution of obstacles in the flight area and the color of each obstacle.

[0065] S300: In the obstacle recognition map, traverse to find the detour points, establish the corresponding relationship between the detour points and the obstacles, generate child nodes, and via the corresponding relationship, mount the child nodes to the leaf nodes.

[0066] Set a detour point at each obstacle, establish the corresponding relationship between the detour point and the obstacle, create child nodes that correspond one-to-one with the detour points. The child nodes are mainly used to represent the detour points. According to the corresponding relationship, mount the child nodes to the leaf nodes.

[0067] S400: Collect the flight altitude of the aircraft, configure the maximum activity altitude, find all the leaf nodes at the maximum activity altitude to obtain the target nodes. Via the RGB sensor pre-integrated in the aircraft, collect the color image on the flight path and extract the RGB values. Use the RGB values to traverse the target nodes in sequence to determine whether there are the same colors in the target nodes. If so, find the branch nodes corresponding to the same colors and define the suspicious obstacles. Obtain the real-time position of the aircraft, find the suspicious obstacles located on the flight path to obtain the target obstacles, find the child nodes corresponding to the target obstacles to obtain the detour points, and use the detour points to offset the preset flight path.

[0068] Define the leaf nodes below the maximum active height as target nodes. During the flight mission execution, the aircraft may only collide with the obstacles corresponding to the target nodes. The RGB sensor will collect color images in real time on the flight path and capture the values of the red, green, and blue channels, i.e., RGB values. Use these RGB values to traverse the target nodes and determine whether there are the same RGB values in the target nodes. If so, define the branch nodes with the same RGB values as suspicious nodes, and the obstacles corresponding to the suspicious nodes are suspicious obstacles. Continue to determine whether the suspicious obstacles are on the flight path. If so, and when the aircraft is about to reach the suspicious obstacles, define this suspicious obstacle as a target obstacle. Use the target obstacle to traverse the obstacle avoidance tree, find the corresponding child nodes, and use the bypass points in the child nodes to adjust the flight path.

[0069] It should be noted that if there are no identical RGB values in the target nodes, it indicates that there may be unknown obstacles. At this time, use the Euclidean distance formula to find the obstacle with the highest similarity in the obstacle avoidance tree, and adjust the flight path according to the relative position relationship between this obstacle and its bypass point.

[0070] In nature, taller facilities and objects include high-rise buildings, trees, and utility poles, etc. The colors of these objects do not change drastically. For example, high-rise buildings are generally gray and white, trees are green and yellow, and utility poles are gray or silver. Generally speaking, the color of each object is unique, and this characteristic can be used to identify the object and thus locate the obstacle to achieve obstacle avoidance. However, there may also be facilities with the same color, which requires identifying multiple sets of colors from the object or its appendages to further determine the type of the object. Specifically in this application, by using the RGB sensor to collect real-time color images on the flight path and determine whether they are the same as the pre-stored colors, the positioning and identification of obstacles are realized.

[0071] For example: In a certain flight mission, the aircraft needs to execute the mission at an altitude of 25 - 30 meters. At this time, find the corresponding layer for 0 - 30 meters. By traversing the obstacle avoidance tree shown in Appendix 1 of the specification, branch node 1 and branch node 2 are obtained. That is to say, during the mission execution, the aircraft may hit branch node 1 and branch node 2. Assume that branch node 1 is a utility pole, branch node 2 is a wind turbine, leaf node 1 is silver-gray, leaf node 2 is gray + black, leaf node 3 is white, and leaf node 4 is gray + white. When the aircraft is in flight and uses the RGB sensor to detect the RGB value corresponding to black, the obstacle is determined to be a utility pole. At this time, use child node 1 as the bypass point and adjust the flight path.

[0072] In Embodiment 2, Figure 3The implementation process of the obstacle avoidance control method for the aircraft provided by the embodiments of the present invention is shown. The following details the steps of generating an obstacle recognition map and marking the positions of obstacles, as follows:

[0073] S101: Obtain the flight mission of the aircraft and set flight parameters.

[0074] Obtain the flight mission of the aircraft from the flight crew of the aircraft. The flight mission usually includes the target position, route, mission objective, execution time, etc.; according to the flight mission, set flight parameters, and the flight parameters include: flight altitude, airspeed, heading angle, maximum flight range, etc.

[0075] S102: With the position as the center and a preset distance as the radius, construct an electronic fence, embed standard parameters into the electronic fence, and correct the flight parameters.

[0076] Embed standard parameters into the electronic fence. The standard parameters include but are not limited to flight altitude, attitude angle, steering angle, speed, etc. When the aircraft enters the electronic fence, adjust the flight parameters to the standard parameters; the advantage of doing this is that it can timely adjust the flight parameters and further improve the safety of the aircraft.

[0077] In Embodiment 3, Figure 3 The implementation process of the obstacle avoidance control method for the aircraft provided by the embodiments of the present invention is shown. The following details the steps of marking the positions of obstacles and configuring the colors of obstacles, as follows:

[0078] S103: Split the flight mission into several stage missions.

[0079] According to the flight mission, determine the estimated route of the aircraft, and according to the length of the estimated route, divide the estimated route at a preset step length. The flight mission in each segment is the stage mission.

[0080] S104: Select an identification color from the colors, create a correspondence between the identification color and the stage mission, and start the stage mission when the color image is the same as the identification color.

[0081] In the estimated route corresponding to the stage mission, find the identification color. When the aircraft detects the identification color through the RGB sensor, start the corresponding stage mission.

[0082] For example, the flight mission of a certain aircraft is to collect remote sensing data at three locations A, B, and C. At location B, a certain red-roofed building is selected, and the identification color is red. Based on the real-time position of the aircraft, it is determined that after the aircraft arrives at B, it is judged whether there is an identifier. If so, remote sensing data collection is carried out. The advantage of doing this is that it can trigger the flight mission and verify the position of the aircraft.

[0083] In Embodiment 4, Figure 4 The implementation process of the obstacle avoidance control method for the aircraft provided by the embodiment of the present invention is shown. The following details the step of dividing the height into several layers as follows:

[0084] S201: Configure the height range of each layer, calculate the density of obstacles in the flight area, and dynamically adjust the height range based on the density.

[0085] Divide the flight area into multiple blocks, calculate the density of obstacles in each block, and adjust the height range according to this density. Specifically, for areas with a large density of obstacles, the height range should be refined.

[0086] S202: Create a priority queue, select potential obstacles on the flight path, establish a link between the potential obstacles and the branch nodes, construct a priority query mechanism, and integrate the priority queue and the priority query mechanism into the obstacle avoidance tree.

[0087] Based on the obstacle recognition map and the estimated route, determine potential obstacles, create a priority queue, transfer the branch nodes corresponding to the obstacles that may be passed through to the priority queue, and start the priority query mechanism. The priority queue is a set composed of obstacles. In the priority queue, the branch nodes are sorted according to the order in which the aircraft passes through. The priority query mechanism is: when the UAV collects a color image, it preferentially traverses the priority queue. The advantage of doing this is that it can further improve the verification efficiency of obstacles.

[0088] In Embodiment 5, Figure 5 The implementation process of the obstacle avoidance control method for the aircraft provided by the embodiment of the present invention is shown. The following details the step of traversing the bypass points in the obstacle recognition map and establishing the corresponding relationship between the bypass points and the obstacles as follows:

[0089] S301: Collect the influencing factors of the flight path, where the influencing factors at least include: meteorology and wind areas, and set the thresholds of the influencing factors.

[0090] Collect the factors that may affect the flight safety at the bypass points, that is, the influencing factors. The influencing factors include: meteorology, wind areas, etc., and set corresponding thresholds for each influencing factor.

[0091] S302: When the real-time value of the influencing factor is greater than the threshold, send an adjustment request to the user, receive the backup point uploaded by the user, and overwrite the detour point.

[0092] For example, the wind speed threshold corresponding to a certain detour point is 10 m / s. If the wind speed is gradually increasing during the flight of the aircraft and reaches 10 m / s before reaching the detour point, at this time, send an adjustment request to the user to remind the user that the current detour point cannot meet the flight conditions and request the user to provide a backup point. After receiving the request, the user uploads a new backup point and uses this backup point to overwrite the original detour point to update the flight path.

[0093] In Embodiment 6, Figure 6 The implementation process of the obstacle avoidance control method for the aircraft provided by the embodiment of the present invention is shown. The following details the steps of obtaining the real-time position of the UAV, finding the suspicious obstacles on the flight path, obtaining the target obstacles, finding the sub-nodes corresponding to the target obstacles, obtaining the detour points, and offsetting the preset flight path using the detour points, as follows:

[0094] S401: Locate the edge devices on the flight path, divide the flight path into several flight segments, and establish a communication link between the edge devices and the aircraft.

[0095] Locate the edge devices on the flight path. The edge devices include ground base stations, communication towers, sensor nodes, and other facilities that support the aircraft mission, etc. Divide the flight path into several flight segments, and establish a communication link between the aircraft and the edge devices according to the distribution of the edge devices in each flight segment.

[0096] S402: Create an obstacle avoidance tree corresponding to each flight segment and deploy the obstacle avoidance tree to the corresponding edge device.

[0097] Create an obstacle avoidance tree for each flight segment. Each obstacle avoidance tree stores information such as the height, color, and position of the obstacles in the corresponding flight segment. Deploy the obstacle avoidance tree to the corresponding edge device to further improve the comparison efficiency of the color images.

[0098] In Embodiment 7, different from Embodiment 1, in the embodiment of the present invention, the method further includes:

[0099] Configure the parameters of each obstacle, where the parameters at least include: shape, size, and depth;

[0100] Use the sensing devices pre-integrated in the aircraft to collect the sensing data on the flight path and verify the parameters of the target obstacles.

[0101] If a sensing device is integrated in the aircraft, such as lidar, infrared sensor, visual camera, etc., the sensing device is used to collect sensing data on the flight path, where the sensing data includes the distance between the aircraft and the obstacle, the shape characteristics of the obstacle. A binocular vision camera can also be used to collect the depth map of the obstacle and generate a three-dimensional stereoscopic image. The comparison result of the RGB sensor is verified through the sensing data or the three-dimensional stereoscopic image, further improving the recognition efficiency of the obstacle.

[0102] Figure 7 The block diagram of the composition structure of the obstacle avoidance control system of the aircraft provided by the embodiment of the present invention is shown. The obstacle avoidance control system 1 of the aircraft includes:

[0103] The marking module 11 is used to delimit the flight area of the aircraft, collect the three-dimensional optical remote sensing data of the flight area, generate an obstacle recognition map, mark the positions of the obstacles, configure the colors of the obstacles, where at least one color corresponds to one obstacle, calculate the height of each obstacle, and mark it on the obstacle recognition map;

[0104] The generation module 12 is used to divide the height into several layers, create branch nodes and leaf nodes, and mount the leaf nodes into the branch nodes, upload the obstacles in each layer to the branch nodes, upload the colors to the leaf nodes, integrate the branch nodes and child nodes, and generate an obstacle avoidance tree;

[0105] The mounting module 13 is used to traverse the bypass points in the obstacle recognition map, establish the corresponding relationship between the bypass points and the obstacles, generate child nodes, and mount the child nodes to the leaf nodes via the corresponding relationship;

[0106] The offset module 14 is used to collect the flight height of the aircraft, configure the highest activity height, find all the leaf nodes at the highest activity height to obtain target nodes, collect the color image on the flight path via the RGB sensor pre-integrated in the aircraft, extract the RGB values, traverse the target nodes in turn using the RGB values, judge whether there are the same colors in the target nodes, if so, find the branch nodes corresponding to the same colors, define suspicious obstacles, obtain the real-time position of the UAV, find the suspicious obstacles located on the flight path to obtain target obstacles, find the child nodes corresponding to the target obstacles to obtain bypass points, and offset the preset flight path using the bypass points.

[0107] Figure 8 The block diagram of the composition structure of the obstacle avoidance control system of the aircraft provided by the embodiment of the present invention is shown. The marking module 11 includes:

[0108] The setting unit 111 is used to obtain the flight task of the aircraft and set flight parameters;

[0109] A correction unit 112, configured to construct an electronic fence with the position as the center and a preset distance as the radius, embed standard parameters into the electronic fence, and correct the flight parameters;

[0110] A splitting unit 113, configured to split the flight mission into several stage missions;

[0111] A starting unit 114, configured to select an identification color from the colors, create a correspondence between the identification color and the stage mission, and start the stage mission when the color image is the same as the identification color.

[0112] Figure 9 The composition structure block diagram of the obstacle avoidance control system of the aircraft provided by the embodiment of the present invention is shown. The generation module 12 includes:

[0113] An adjustment unit 121, configured to configure the height range of each layer, calculate the density of obstacles in the flight area, and dynamically adjust the height range based on the density;

[0114] An integration unit 122, configured to create a priority queue, select potential obstacles on the flight path, establish a link between the potential obstacles and the branch nodes, construct a priority query mechanism, and integrate the priority queue and the priority query mechanism into the obstacle avoidance tree.

[0115] Figure 10 The composition structure block diagram of the obstacle avoidance control system of the aircraft provided by the embodiment of the present invention is shown. The mounting module 13 includes:

[0116] An acquisition unit 131, configured to acquire the influencing factors of the flight path, where the influencing factors at least include meteorology and wind areas, and set thresholds for the influencing factors;

[0117] A covering unit 132, configured to send an adjustment request to the user when the real-time value of the influencing factor is greater than the threshold, receive the backup points uploaded by the user, and cover the detour points.

[0118] Figure 11 The composition structure block diagram of the obstacle avoidance control system of the aircraft provided by the embodiment of the present invention is shown. The offset module 14 includes:

[0119] A positioning unit 141, configured to locate the edge devices on the flight path, split the flight path into several flight segments, and establish a communication link between the edge devices and the aircraft;

[0120] A deployment unit 142, configured to create an obstacle avoidance tree corresponding to each flight segment, and deploy the obstacle avoidance tree to the corresponding edge devices.

[0121] Among them, the annotation module 11 is mainly used to complete step S100, the generation module 12 is mainly used to complete step S200, the mounting module 13 is mainly used to complete step S300, and the offset module 14 is mainly used to complete step S400;

[0122] The setting unit 111 is mainly used to complete step S101, the correction unit 112 is mainly used to complete step S102, the segmentation unit 113 is mainly used to complete step S103, and the startup unit 114 is mainly used to complete step S104;

[0123] The adjustment unit 121 is mainly used to complete step S201, and the integration unit 122 is mainly used to complete step S202;

[0124] The acquisition unit 131 is mainly used to complete step S301, and the covering unit 132 is mainly used to complete step S302;

[0125] The positioning unit 141 is mainly used to complete step S401, and the deployment unit 142 is mainly used to complete step S402.

[0126] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0127] The above-described embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

[0128] The above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An obstacle avoidance control method for an aircraft, characterized in that: The method comprises: Delineating the flight area of ​​the aircraft, collecting stereoscopic optical remote sensing data of the flight area, drawing an obstacle identification map, marking the positions of obstacles, configuring the colors of obstacles, wherein one obstacle corresponds to at least one color, calculating the height of each obstacle, and marking it in the obstacle identification map; Divide the height into several layers, create branch nodes and leaf nodes, mount the leaf nodes to the branch nodes, upload obstacles in each layer to the branch nodes, upload the colors to the leaf nodes, integrate the branch nodes and leaf nodes, and generate an obstacle avoidance tree; In the obstacle identification graph, traverse the detour points, establish the corresponding relationship between the detour points and the obstacles, generate child nodes, and mount the child nodes to the leaf nodes through the corresponding relationship; The flight altitude of the aircraft is collected, the maximum activity altitude is configured, all leaf nodes under the maximum activity altitude are found, and the target node is obtained. The color image on the flight path is collected through the RGB sensor pre-integrated in the aircraft, and the RGB value is extracted. The target nodes are traversed in sequence using the RGB value to determine whether the same color exists in the target node. If so, the branch nodes corresponding to the same color are found, and suspicious obstacles are defined. The real-time position of the aircraft is obtained, and the suspicious obstacles on the flight path are found to obtain the target obstacle. The child node corresponding to the target obstacle is found to obtain the detour point, and the detour point is used to offset the preset flight path.

2. The obstacle avoidance control method for an aircraft according to claim 1, characterized in that: The step of drawing an obstacle identification map and marking the positions of obstacles comprises: Get the flight mission of the aircraft and set the flight parameters; An electronic fence is constructed with the position as the center and the preset distance as the radius, standard parameters are embedded in the electronic fence, and the flight parameters are corrected.

3. The obstacle avoidance control method for an aircraft according to claim 2, characterized in that: The steps of marking the position of the obstacle and configuring the color of the obstacle include: Dividing the flight mission into a number of stage tasks; An identification color is selected from the colors, and a corresponding relationship between the identification color and a stage task is created. When the color image is the same as the identification color, the stage task is started.

4. The obstacle avoidance control method for an aircraft according to claim 1, characterized in that: The step of dividing the height into a plurality of layers comprises: Configure the altitude range of each layer, calculate the density of obstacles in the flight area, and dynamically adjust the altitude range based on the density; A priority queue is created, potential obstacles on the flight path are selected, links between potential obstacles and branch nodes are established, a priority query mechanism is constructed, and the priority queue and the priority query mechanism are integrated into the obstacle avoidance tree.

5. The obstacle avoidance control method for an aircraft according to claim 4, characterized in that: The step of traversing the fly-around points in the obstacle identification map and establishing a corresponding relationship between the fly-around points and obstacles comprises: collecting influencing factors of the flight path, wherein the influencing factors at least include: weather and wind zone, and setting thresholds of the influencing factors; When the real-time value of the influencing factor is greater than a threshold, an adjustment request is sent to the user, a backup point uploaded by the user is received, and the detour point is covered.

6. The obstacle avoidance control method for an aircraft according to claim 5, characterized in that: The steps of obtaining the real-time position of the aircraft, finding out suspicious obstacles on the flight path, obtaining target obstacles, finding out the subnodes corresponding to the target obstacles, obtaining detour points, and using the detour points to offset the preset flight path include: Locating edge devices on the flight path, dividing the flight path into a number of segments, and establishing a communication link between the edge devices and the aircraft; An obstacle avoidance tree corresponding to each flight segment is created, and the obstacle avoidance tree is deployed to the corresponding edge device.

7. The obstacle avoidance control method for an aircraft according to claim 1, characterized in that: The method further comprises: Configuring parameters of each obstacle, wherein the parameters include at least: shape, size and depth; The sensing data on the flight path are collected by using the sensing equipment pre-integrated in the aircraft, and the parameters of the target obstacle are verified.

8. An obstacle avoidance control system for an aircraft, characterized in that: The system comprises: A marking module is used to delineate the flight area of ​​the aircraft, collect stereoscopic optical remote sensing data of the flight area, draw an obstacle identification map, mark the positions of obstacles, configure the colors of obstacles, where one obstacle corresponds to at least one color, calculate the height of each obstacle, and mark it in the obstacle identification map; A generation module, used to divide the height into several layers, create branch nodes and leaf nodes, mount the leaf nodes to the branch nodes, upload obstacles in each layer to the branch nodes, upload the colors to the leaf nodes, integrate the branch nodes and the leaf nodes, and generate an obstacle avoidance tree; A mounting module, used for traversing the detour points in the obstacle identification map, establishing a correspondence between the detour points and obstacles, generating child nodes, and mounting the child nodes to leaf nodes via the correspondence; The offset module is used to collect the flight altitude of the aircraft, configure the maximum activity altitude, find out all leaf nodes under the maximum activity altitude, obtain the target node, collect the color image on the flight path through the RGB sensor pre-integrated in the aircraft, and extract the RGB value, use the RGB value to traverse the target node in sequence, determine whether the same color exists in the target node, if so, find out the branch node corresponding to the same color, and define suspicious obstacles, obtain the real-time position of the aircraft, find out the suspicious obstacles on the flight path, obtain the target obstacle, find out the child node corresponding to the target obstacle, obtain the detour point, and use the detour point to offset the preset flight path.

9. The obstacle avoidance control system for an aircraft according to claim 8, characterized in that: The marking module comprises: A setting unit, used to obtain the flight mission of the aircraft and set the flight parameters; A correction unit, used to construct an electronic fence with the position as the center and the preset distance as the radius, embed standard parameters into the electronic fence, and correct the flight parameters; A dividing unit, used for dividing the flight mission into a plurality of stage tasks; The starting unit is used to select an identification color from the colors, create a corresponding relationship between the identification color and the stage task, and start the stage task when the color image is the same as the identification color.

10. The obstacle avoidance control system for an aircraft according to claim 8, characterized in that: The generation module comprises: An adjustment unit, configured to configure a height range for each layer, calculate the density of obstacles in the flight area, and dynamically adjust the height range based on the density; The integration unit is used to create a priority queue, select potential obstacles on the flight path, establish links between potential obstacles and branch nodes, build a priority query mechanism, and integrate the priority queue and the priority query mechanism into the obstacle avoidance tree.

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