Unmanned aerial vehicle obstacle avoidance control method, system and equipment based on radar

Abstract

The present application relates to the technical field of obstacle avoidance control for unmanned aerial vehicles, and specifically to a radar-based obstacle avoidance control method, system and equipment for unmanned aerial vehicles, the method comprising: using a laser radar to collect obstacle data near the unmanned aerial vehicle during flight; using the braking distance of the unmanned aerial vehicle and the distance of the obstacle closest to the unmanned aerial vehicle to preliminarily screen an interval; if there is no obstacle in the interval, the interval is recorded as the first optional interval; based on the distribution of obstacles in the interval, the avoidance priority of the interval is determined, and the second optional interval is further screened; using the proximity of the center directions of the first optional interval and the second optional interval to the destination direction of the unmanned aerial vehicle, the current movement direction of the unmanned aerial vehicle and the last movement direction of the unmanned aerial vehicle, respectively, the next movement direction of the unmanned aerial vehicle is determined. The present application aims to screen the optional intervals for unmanned aerial vehicle flight and improve the obstacle avoidance efficiency of unmanned aerial vehicles.

Description

Technical Field

[0001] The present application relates to the technical field of unmanned aerial vehicle obstacle avoidance control, and specifically to a radar-based unmanned aerial vehicle obstacle avoidance control method, system and equipment. Background Art

[0002] In order to improve the flight safety and operating efficiency of drones, drones need to have obstacle avoidance functions to help them avoid collisions during flight, ensure flight safety, adapt to various complex environments, and reduce maintenance costs. Existing drone obstacle avoidance methods include visual obstacle avoidance systems, infrared obstacle avoidance systems, ultrasonic systems, and lidar obstacle avoidance systems. Among them, lidar obstacle avoidance technology is widely used because of its advantages such as high-precision measurement, strong environmental adaptability, wide detection range, and strong anti-interference ability.

[0003] When using LiDAR to avoid obstacles, the Vector Field Histogram (VFH) algorithm is usually used to compare the credibility with the size of the artificially set threshold to select a feasible flight path. However, when the threshold of the VFH algorithm is set too small, some passable paths will be ignored; when the threshold is too large, the drone will not be able to detect obstacles in time, and will not have time to avoid them, resulting in collisions. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a radar-based drone obstacle avoidance control method, system and device. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a radar-based drone obstacle avoidance control method, the method comprising the following steps:

[0006] A1, using LiDAR to collect obstacle data near the drone during flight;

[0007] A2, with the laser radar as the center, divide the horizontal plane around the laser radar into a preset number of fan-shaped intervals; use the braking distance of the drone and the distance of the nearest obstacle to the drone to preliminarily screen the intervals;

[0008] A3, if there is no obstacle in the interval, the interval is recorded as the first optional interval; otherwise, the obstacles in the interval are expanded, and the rectangular coordinates of all boundary points in the expanded interval are clustered;

[0009] Based on the number of clusters after clustering, the average shortest distance between clusters, and the shortest distance between the clusters and the two straight line boundaries of the fan-shaped area where the interval is located, the confidence of the potential path of the interval is determined;

[0010] A4, based on the number of clusters in the interval and the confidence of the potential path, determine the avoidance priority of the interval, and further screen the second optional interval;

[0011] A5, determining the next movement direction of the drone by using the proximity of the center directions of the first optional interval and the second optional interval to the destination direction of the drone, the current movement direction of the drone, and the last movement direction of the drone.

[0012] Preferably, the braking distance is calculated by the current speed of the drone and the maximum acceleration that can be achieved during the driving process of the drone.

[0013] Preferably, the interval obtained by preliminary screening is an interval where the distance of the obstacle closest to the drone is greater than the braking distance of the drone.

[0014] Preferably, the expansion radius during the expansion process is the maximum distance from the laser radar to the outermost side of the UAV fuselage.

[0015] Preferably, the calculation method of the potential path confidence is further determined as follows: the potential path confidence of the i-th interval is recorded as ;

[0016]

[0017] In the formula, represents the potential path confidence of the i-th interval; m represents the number of clusters in the interval; Indicates the shortest distance between the cluster in the i-th interval and one of the straight line boundaries of the fan-shaped area where the interval is located; It represents the shortest distance between the cluster in the ith interval and another straight line boundary of the fan-shaped area where the interval is located; 2R is used to represent the fuselage width of the drone; Represents the average shortest distance between clusters in the i-th interval.

[0018] Preferably, the avoidance priority of the i-th interval is recorded as , .

[0019] Preferably, the screening process of the second optional interval is:

[0020] For satisfaction The avoidance priority of all intervals is calculated, and a first segmentation threshold is obtained by threshold segmentation, and intervals with avoidance priority greater than the first segmentation threshold are recorded as second optional intervals;

[0021] For satisfaction The avoidance priorities of all intervals are determined, and threshold segmentation is performed to obtain a second segmentation threshold. The intervals with avoidance priorities greater than the second segmentation threshold are recorded as second optional intervals.

[0022] Preferably, the method for determining the next movement direction of the drone is further determined as follows:

[0023] The cost function value of the i-th interval is recorded as ; ; In the formula, represents the angular deviation between the center direction of the i-th interval and the destination direction; is the angular deviation between the center direction of the i-th interval and the current motion direction; is the angular deviation between the center direction of the ith interval and the direction of the previous movement; , and are respectively the preset first, second and third weight coefficients;

[0024] The center direction of the interval with the largest cost function value between the first optional interval and the second optional interval is used as the next movement direction of the UAV.

[0025] In a second aspect, an embodiment of the present application provides a radar-based drone obstacle avoidance control system, the drone obstacle avoidance control system comprising:

[0026] The flight data collection module is used to collect obstacle data near the UAV during flight using LiDAR;

[0027] An optional interval calculation module is used to divide the horizontal plane around the laser radar into a preset number of sector intervals with the laser radar as the center; the intervals are initially screened using the braking distance of the drone and the distance of the nearest obstacle to the drone;

[0028] If there is no obstacle in the interval, the interval is recorded as the first optional interval; otherwise, the obstacles in the interval are expanded, and the rectangular coordinates of all boundary points in the expanded interval are clustered; based on the number of clusters after clustering, the average shortest distance between clusters, and the shortest distance between the clusters and the two straight line boundaries of the sector area where the interval is located, the confidence of the potential path of the interval is determined;

[0029] Based on the number of clusters in the interval and the confidence of the potential path, the avoidance priority of the interval is determined, and the second optional interval is further screened;

[0030] The flight decision control module is used to determine the next movement direction of the drone by using the proximity of the center directions of the first optional interval and the second optional interval to the destination direction of the drone, the current movement direction of the drone and the previous movement direction of the drone.

[0031] In a third aspect, an embodiment of the present application also provides a radar-based UAV obstacle avoidance control device, the system comprising a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of any one of the above-mentioned radar-based UAV obstacle avoidance control methods when executing the computer program.

[0032] It can be seen from the above embodiments that the radar-based drone obstacle avoidance control method, system and device provided in the embodiments of the present application have at least the following beneficial effects:

[0033] The present application preliminarily screens intervals based on the braking distance of the drone and the distance of the obstacle closest to the drone. The beneficial effect is that areas where there is no safe braking distance are removed to avoid the risk of drone obstacle avoidance. Then, a potential path confidence is constructed based on the distribution of obstacles in the interval to determine whether the interval containing obstacles contains a potential path. Based on the number of clusters in the interval and the potential path confidence, the avoidance priority of the interval is determined to characterize the degree to which a potential path in an interval is suitable for drone obstacle avoidance. Finally, the center directions of the first optional interval and the second optional interval are used to determine the proximity to the destination direction of the drone, the current movement direction of the drone, and the last movement direction of the drone, respectively, and the differences in multiple directions are combined to select a movement direction that best suits the drone, thereby improving the drone's obstacle avoidance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0035] Figure 1 A flowchart of a radar-based drone obstacle avoidance control method according to an embodiment of the present application;

[0036] Figure 2 A schematic diagram of the flight direction of a drone during flight provided by one embodiment of the present application;

[0037] Figure 3 A schematic diagram of the structure of a radar-based drone obstacle avoidance control system provided for one embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to further explain the technical means and effects adopted by this application to achieve the predetermined invention purpose, the radar-based unmanned aerial vehicle obstacle avoidance control method, system and equipment proposed in this application, its specific implementation method, structure, features and effects are described in detail below in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0039] Unless otherwise specified and limited, terms such as "comprises", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such articles or devices. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the article or device including the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items. All technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of this application.

[0040] The specific scheme of the radar-based drone obstacle avoidance control method, system and equipment provided by the present application is described in detail below with reference to the accompanying drawings.

[0041] See also Figure 1 , which shows a flowchart of a radar-based drone obstacle avoidance control method provided by an embodiment of the present application, the method comprising the following steps:

[0042] A1, using LiDAR to collect obstacle data near the drone during flight.

[0043] The laser radar is installed at the center of the drone. The location of the laser radar is taken as the pole. A ray is drawn from the pole in the flight plane of the drone, which is called the polar axis. The polar axis is perpendicular to the flight direction, and a polar coordinate system is obtained.

[0044] Use laser radar to collect laser scanning data during the flight of the drone to obtain the polar coordinate data of obstacles , where n represents the number of obstacles obtained at the current moment; is the polar coordinate of the nth obstacle; and Respectively represent the distance and angle between the nth obstacle and the drone.

[0045] Since the laser emitted by the lidar cannot penetrate the interior of the obstacle, the collected obstacle polar coordinate data is only the surface coordinates of the obstacle within the field of view of the drone, and the obstacle data behind the obstacle cannot be obtained.

[0046] From the built-in flight control system of the drone, obtain the maximum distance R from the pole to the outermost side of the drone body, the real-time speed v of the drone, the maximum acceleration a of the drone, and the direction angle β of the destination in polar coordinates at the current moment.

[0047] At this point, through the above steps, the lidar can be used to collect obstacle data near the drone during flight.

[0048] A2, with the laser radar as the center, divide the horizontal plane around the laser radar into a preset number of fan-shaped intervals; use the braking distance of the drone and the distance of the nearest obstacle to the drone to preliminarily screen the intervals.

[0049] First, based on the obstacle polar coordinate data collected by the LiDAR in real time, the obstacle polar coordinates are projected onto a two-dimensional plane to obtain a real-time obstacle map. Since the UAV has a certain width, a certain safety distance needs to be left between the LiDAR and the obstacle when avoiding obstacles to prevent the UAV body from colliding with the obstacle.

[0050] Therefore, the maximum distance R from the laser radar to the outermost side of the fuselage is used as the expansion radius r, and the boundary of the obstacle is expanded to obtain an expansion map. The expansion algorithm is a well-known technology, and the specific process will not be repeated.

[0051] In the expansion map, the laser radar is taken as the center and the 360 ​​degrees around the horizontal plane where the laser radar is located are divided into a sector-shaped interval every 10 degrees, resulting in 36 sector-shaped intervals. In other embodiments, the number of intervals and the division method can be set by the implementer.

[0052] When using the vector field histogram algorithm (VFH) to plan the optimal movement direction of the drone, the distance between the polar coordinate data of the obstacle and the manually set threshold is used. The optional interval is obtained by using the relationship between , and finally the optimal interval is selected from the optional interval. The vector field histogram algorithm is a well-known technology and will not be described in detail.

[0053] In this application, the obstacle position closest to the drone is used to preliminarily screen the interval.

[0054] It should be understood that when the distance to the nearest obstacle of the drone is smaller, it means that the distance between the obstacle and the drone in the interval is closer. When , the i-th interval is used as the interval after preliminary screening. The interval where the obstacle closest to the drone is located is eliminated and no subsequent analysis is performed on it.

[0055] When comparing the distance of the obstacle closest to the drone with the threshold, the value of the threshold will directly affect the accuracy of the VFH algorithm in determining whether a section is passable. If the threshold is too small, some passable paths will be ignored; if the threshold is too large, the drone will not be able to detect obstacles in time, and will not have time to avoid them, resulting in collisions.

[0056] If the current speed of the drone is v, then when the drone is moving at a uniform deceleration with a maximum acceleration a, the braking distance when it decelerates to 0 is To prevent the drone from colliding with obstacles, the drone can only travel in the direction of the i-th interval when the distance between the drone and the obstacle in the i-th interval is greater than s. When the closest distance between the drone and the obstacle in the i-th interval is s, the threshold is set. When .

[0057] like Figure 2 The schematic diagram of the flight direction of the drone during flight is shown in the figure. r is the expansion radius, which means the maximum distance R from the laser radar to the outermost side of the fuselage. j1 and j2 are the two directions in which the drone can currently travel, and j3 is a direction in which the drone can travel after reaching a certain position. There is an obstacle in the j1 direction of the drone, but there is no obstacle in the j2 direction. Then the credibility of the interval in the j1 direction is higher than the credibility of the interval in the j2 direction. When the threshold is set to a small value, the VFH algorithm will not regard the interval in the j1 direction as an optional interval, and the drone is likely to travel in the j2 direction. However, if the threshold is set to a large value, both the j1 and j2 intervals will be regarded as optional intervals. If the drone travels in the j1 direction, it will find a new optional direction j3 after traveling a certain distance, and the drone will travel in the j3 direction. In comparison, the entire driving path in the j1 and j3 directions is closer than the entire driving path in the j2 direction. Therefore, the threshold When it is small, some better feasible paths may be ignored.

[0058] In order to enable the VFH algorithm to achieve accurate and rapid obstacle avoidance, it is necessary to select a suitable threshold to make the selected optional interval more accurate.

[0059] Preferably, in this embodiment, the interval obtained by preliminary screening is an interval in which the distance of the obstacle closest to the drone is greater than the braking distance of the drone.

[0060] As an implementation method, when determining which intervals around the drone can be used as optional intervals, a small threshold is first set. Remove the impossible-to-pass intervals. , if the distance of the nearest obstacle to the drone in the i-th interval is greater than or equal to , indicating that the obstacle distance in the ith interval is greater than or equal to the braking distance of the drone, the ith interval is retained; if the distance of the nearest obstacle to the drone in the ith interval is less than , then remove the i-th interval.

[0061] A3, if there is no obstacle in the interval, record the interval as the first optional interval; otherwise, expand the obstacles in the interval, and cluster the rectangular coordinates of all boundary points in the expanded interval; determine the potential path confidence of the interval based on the number of clusters after clustering, the average shortest distance between clusters, and the shortest distance between the clusters and the two straight line boundaries of the fan-shaped area where the interval is located.

[0062] Among the retained intervals, some intervals have a large distance from the nearest obstacle to the drone, but may not allow the drone to pass smoothly. There are also some intervals that have a relatively small distance from the nearest obstacle to the drone, but these intervals have potential paths for the drone to pass. Therefore, further screening is needed to remove the impassable intervals and retain the intervals with potential paths that are conducive to drone obstacle avoidance.

[0063] (1) When there is no obstacle in an interval, it means that the drone can pass through the interval; all intervals without obstacles are recorded as the first optional intervals.

[0064] (2) When there is an obstacle in an interval, further, in the expansion map, the polar coordinates of each expansion obstacle boundary point are converted into rectangular coordinates. The conversion method is a well-known technology and the specific process is not repeated. The rectangular coordinates of all boundary points in the interval are used as the input of the fuzzy subtractive clustering (FSC) algorithm, the initial number of cluster centers is set to a, and the clustering algorithm is used to divide all boundary points in the interval into m clusters, where a is 10 in this embodiment, and the FCS algorithm is a well-known technology and the specific process is not repeated. In other embodiments of the present application, other suitable clustering algorithms can also be selected to cluster the rectangular coordinates of all boundary points in the interval.

[0065] When the number of clusters in an interval is 1, it means that the interval contains only one expansion obstacle. When the distance between the expansion obstacle and the two straight line boundaries of the fan-shaped area where the interval is located is greater than 2R, it means that the interval contains a potential path, and the larger the distance, the more conducive it is for drones to pass.

[0066] When the number of clusters in an interval is greater than 1, it means that the interval contains two or more expansion obstacles. When the Euclidean distance between these expansion obstacles is large, it means that the interval is more likely to contain potential paths.

[0067] Accordingly, in this application, the confidence of the potential path of the interval is determined based on the number of clusters after clustering, the average shortest distance between clusters, and the shortest distance between the clusters and the two straight line boundaries of the sector area where the interval is located, which is used to characterize the possibility of the existence of a potential path in the interval.

[0068] In this embodiment, the potential path confidence of the i-th interval is For example:

[0069]

[0070] In the formula, represents the potential path confidence of the i-th interval; m represents the number of clusters in the interval; Indicates the shortest distance between the cluster in the i-th interval and one of the straight line boundaries of the fan-shaped area where the interval is located; It represents the shortest distance between the cluster in the ith interval and another straight line boundary of the fan-shaped area where the interval is located; 2R is used to represent the fuselage width of the drone; Represents the average shortest distance between clusters in the i-th interval.

[0071] in, ; In the formula, It represents the average inter-cluster distance of the ith interval, which is used to characterize the average distance between the two nearest boundary points between clusters in the ith interval; Indicates the number of ways to combine m clusters in pairs; and Represents the two closest boundary points on the kth cluster and the k+lth cluster; is a distance function used to calculate the distance between two points. In one implementation of this embodiment, the Euclidean distance is used. The calculation process of the Euclidean distance is a well-known technology and will not be described in detail. It should be understood that The larger the value of , the larger the overall distance between clusters in the ith interval, which means that the possibility of a potential path existing in the ith interval is greater.

[0072] It should be understood that when the number of clusters in the i-th interval is 1, if the shortest distance from the expansion obstacle in the interval to the interval boundary is greater than the fuselage width, it means that the interval contains a potential path, that is, the drone can pass through; if the shortest distance from the expansion obstacle in the interval to the boundaries on both sides of the interval is less than or equal to the fuselage width, it means that the interval does not contain a potential path and the drone cannot pass through. When the number of clusters in the i-th interval is greater than 1, if the average inter-cluster distance in the interval is less than or equal to the fuselage width, then there is no potential path in the interval; if the average shortest distance between clusters in the interval is greater than the fuselage width, it means that there is a potential path in the interval.

[0073] Among them, when When the value of is 0, it means that there is no potential path in the i-th interval; The value of is 1, indicating that there is a potential path in the i-th interval.

[0074] A4, based on the number of clusters of the interval and the confidence of the potential path, determine the avoidance priority of the interval, and further screen the second optional interval.

[0075] When m=1 and When m>1, it means that there is a potential path in the interval. When the shortest distance from the expansion obstacle to the boundaries of both sides of the interval is larger, it means that the interval is more conducive to the drone to avoid obstacles. When m>1 and When , it means that there is a potential path in this interval. The larger the average shortest distance between clusters in this interval, the more conducive it is for the drone to avoid obstacles. However, as the number of expansion obstacles increases, the position distribution between these expansion obstacles will become complicated, which is not conducive to the drone's obstacle avoidance. , it means that there is no potential path in this interval, that is, this interval is not suitable for drone obstacle avoidance.

[0076] Accordingly, in the present application, the avoidance priority of an interval is determined based on the number of clusters and the confidence of potential paths, which is used to characterize the degree to which potential paths within the interval are suitable for drone obstacle avoidance.

[0077] Preferably, in this embodiment, the avoidance priority of the i-th interval is For example:

[0078]

[0079] In the formula, represents the avoidance priority of the i-th interval. It should be understood that The larger the value of is, the more beneficial the potential path in the i-th interval is for the drone to avoid obstacles.

[0080] In this embodiment, the screening process of the second optional interval is:

[0081] For satisfaction The avoidance priority of all intervals is calculated, and the first segmentation threshold is obtained by threshold segmentation. The intervals with avoidance priority greater than the first segmentation threshold are recorded as the second optional intervals. The avoidance priorities of all intervals are determined, and threshold segmentation is performed to obtain a second segmentation threshold. The intervals with avoidance priorities greater than the second segmentation threshold are recorded as second optional intervals.

[0082] As an implementation method, when the distance of the nearest obstacle to the drone is greater than Among all the intervals of the preliminary screening, the avoidance priority of all intervals with a cluster number of 1 and potential paths (i.e., excluding The interval where the cluster is located), perform threshold segmentation to obtain the first segmentation threshold e, and use the intervals with avoidance priority greater than e as the second optional intervals; the avoidance priority of all intervals with a cluster number greater than 1 and a potential path (i.e., excluding The interval where the avoidance priority is greater than u is used as the second optional interval.

[0083] In this embodiment, the Otsu threshold segmentation algorithm is used for threshold segmentation. The Otsu threshold segmentation algorithm is a well-known technology, and the specific process will not be repeated here.

[0084] So far, all optional intervals of 360 degrees around the horizontal plane where the drone is located are obtained, and the optional intervals include a first optional interval and a second optional interval.

[0085] A5, determining the next movement direction of the drone by using the proximity of the center directions of the first optional interval and the second optional interval to the destination direction of the drone, the current movement direction of the drone, and the last movement direction of the drone.

[0086] Furthermore, the cost function of each optional interval is calculated by comprehensively considering the proximity of the center direction of each interval to the destination direction of the UAV, the current movement direction of the UAV, and the last movement direction of the UAV.

[0087] In this embodiment, the cost function is calculated as follows: .

[0088] In the formula, represents the cost function value of the i-th interval, represents the angular deviation between the center direction of the i-th interval and the destination direction; is the angular deviation between the center direction of the i-th interval and the current motion direction; is the angular deviation between the center direction of the ith interval and the direction of the previous movement; , and are the first, second and third weight coefficients, respectively corresponding to the weighted weights of the destination direction of the drone, the current movement direction of the drone and the angle deviation of the last movement direction of the drone from the center direction of the interval. In this embodiment, they are 0.5, 0.3 and 0.2 respectively. In other embodiments of this application, the implementer can set the value by himself, but it is necessary to ensure . The angular deviation is the angle between the two directions.

[0089] In other embodiments of the present application, the angle between the center direction of each interval and the destination direction of the drone, the current movement direction of the drone, and the last movement direction of the drone can also be used to determine the next movement direction of the drone.

[0090] Calculate the cost function values ​​of all optional intervals, take the optional interval with the largest cost function value as the optimal interval, and take the center direction of the optimal interval as the next movement direction of the drone.

[0091] See also Figure 3 , Figure 3 is a schematic diagram of the structure of the radar-based unmanned aerial vehicle obstacle avoidance control system provided in the embodiment of the present application. In this embodiment, each unit included in the terminal is used to execute each step in the embodiment corresponding to the radar-based unmanned aerial vehicle obstacle avoidance control method. Figure 3 , the unmanned aerial vehicle obstacle avoidance control system 30 includes:

[0092] The flight data collection module 31 is used to collect obstacle data near the UAV during flight using a laser radar;

[0093] The optional interval calculation module 32 is used to divide the horizontal plane around the laser radar into a preset number of sector intervals with the laser radar as the center; the intervals are preliminarily selected by using the braking distance of the drone and the distance of the obstacle closest to the drone;

[0094] If there is no obstacle in the interval, the interval is recorded as the first optional interval; otherwise, the obstacles in the interval are expanded, and the rectangular coordinates of all boundary points in the expanded interval are clustered; based on the number of clusters after clustering, the average shortest distance between clusters, and the shortest distance between the clusters and the two straight line boundaries of the sector area where the interval is located, the confidence of the potential path of the interval is determined;

[0095] Based on the number of clusters in the interval and the confidence of the potential path, the avoidance priority of the interval is determined, and the second optional interval is further screened;

[0096] The flight decision control module 33 is used to determine the next movement direction of the drone by using the proximity of the center directions of the first optional interval and the second optional interval to the destination direction of the drone, the current movement direction of the drone and the previous movement direction of the drone.

[0097] Based on the same inventive concept as the above method, an embodiment of the present application also provides a radar-based UAV obstacle avoidance control device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned radar-based UAV obstacle avoidance control methods are implemented.

[0098] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0099] It should be noted that, unless otherwise specified and limited, terms such as "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, an element defined by the sentence "including one..." does not exclude the existence of other identical elements in the article or device including the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items.

[0100] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not invented by the present application.

[0101] It should be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A radar-based obstacle avoidance control method for unmanned aerial vehicles, characterized in that: The method comprises the following steps: A1, using LiDAR to collect obstacle data near the drone during flight; A2, with the laser radar as the center, divide the horizontal plane around the laser radar into a preset number of fan-shaped intervals; use the braking distance of the drone and the distance of the nearest obstacle to the drone to preliminarily screen the intervals; A3, if there is no obstacle in the interval, the interval is recorded as the first optional interval; otherwise, the obstacles in the interval are expanded, and the rectangular coordinates of all boundary points in the expanded interval are clustered; Based on the number of clusters after clustering, the average shortest distance between clusters, and the shortest distance between the clusters and the two straight line boundaries of the fan-shaped area where the interval is located, the confidence of the potential path of the interval is determined; A4, based on the number of clusters in the interval and the confidence of the potential path, determine the avoidance priority of the interval, and further screen the second optional interval; A5, determining the next movement direction of the drone by using the proximity of the center directions of the first optional interval and the second optional interval to the destination direction of the drone, the current movement direction of the drone, and the last movement direction of the drone; The calculation formula of the avoidance priority is: The avoidance priority of the i-th interval is recorded as , ; In the formula, represents the potential path confidence of the i-th interval; m represents the number of clusters in the interval; Indicates the shortest distance between the cluster in the i-th interval and one of the straight line boundaries of the fan-shaped area where the interval is located; Indicates the shortest distance between the cluster in the i-th interval and another straight line boundary of the fan-shaped area where the interval is located; Represents the average shortest distance between clusters in the i-th interval.

2. The radar-based obstacle avoidance control method for unmanned aerial vehicles according to claim 1, characterized in that: The braking distance is calculated based on the current speed of the drone and the maximum acceleration that the drone can reach during driving.

3. The radar-based obstacle avoidance control method for unmanned aerial vehicles according to claim 2, characterized in that: The interval obtained by preliminary screening is the interval where the distance of the obstacle closest to the drone is greater than the braking distance of the drone.

4. The radar-based obstacle avoidance control method for unmanned aerial vehicles according to claim 1, characterized in that: The expansion radius during the expansion process is the maximum distance from the laser radar to the outermost side of the UAV fuselage.

5. The radar-based obstacle avoidance control method for unmanned aerial vehicles according to claim 1, characterized in that: The calculation method of the potential path confidence is further determined as follows: the potential path confidence of the ith interval is recorded as ; ; In the formula, 2R is used to represent the fuselage width of the UAV.

6. The radar-based obstacle avoidance control method for unmanned aerial vehicles according to claim 5, characterized in that: The screening process of the second optional interval is: For satisfaction The avoidance priority of all intervals is calculated, and a first segmentation threshold is obtained by threshold segmentation, and intervals with avoidance priority greater than the first segmentation threshold are recorded as second optional intervals; For satisfaction The avoidance priorities of all intervals are determined, and threshold segmentation is performed to obtain a second segmentation threshold. The intervals with avoidance priorities greater than the second segmentation threshold are recorded as second optional intervals.

7. The radar-based obstacle avoidance control method for unmanned aerial vehicles according to claim 1, characterized in that: The method for determining the next movement direction of the drone is further determined as follows: The cost function value of the i-th interval is recorded as ; ; In the formula, represents the angular deviation between the center direction of the i-th interval and the destination direction; is the angular deviation between the center direction of the i-th interval and the current motion direction; is the angular deviation between the center direction of the ith interval and the direction of the previous movement; , and are respectively the preset first, second and third weight coefficients; The center direction of the interval with the largest cost function value between the first optional interval and the second optional interval is used as the next movement direction of the UAV.

8. A radar-based unmanned aerial vehicle obstacle avoidance control system, implementing the radar-based unmanned aerial vehicle obstacle avoidance control method as claimed in claim 1, characterized in that: The unmanned aerial vehicle obstacle avoidance control system comprises: The flight data collection module is used to collect obstacle data near the UAV during flight using LiDAR; An optional interval calculation module is used to divide the horizontal plane around the laser radar into a preset number of sector intervals with the laser radar as the center; the intervals are initially screened using the braking distance of the drone and the distance of the nearest obstacle to the drone; If there is no obstacle in the interval, the interval is recorded as the first optional interval; otherwise, the obstacles in the interval are expanded, and the rectangular coordinates of all boundary points in the expanded interval are clustered; based on the number of clusters after clustering, the average shortest distance between clusters, and the shortest distance between the clusters and the two straight line boundaries of the sector area where the interval is located, the confidence of the potential path of the interval is determined; Based on the number of clusters in the interval and the confidence of the potential path, the avoidance priority of the interval is determined, and the second optional interval is further screened; The flight decision control module is used to determine the next movement direction of the drone by using the proximity of the center directions of the first optional interval and the second optional interval to the destination direction of the drone, the current movement direction of the drone and the previous movement direction of the drone.

9. A radar-based obstacle avoidance control device for unmanned aerial vehicles, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the radar-based drone obstacle avoidance control method as described in any one of claims 1-7 are implemented.

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