A UAV obstacle avoidance system based on radar control

Abstract

The present invention discloses a radar-controlled unmanned aerial vehicle obstacle avoidance system, comprising: a radar scanning composition module, an obstacle avoidance path judgment module, and an obstacle avoidance path recommendation module. The radar scanning composition module is used to scan the terrain around the unmanned aerial vehicle through the radar mounted on the unmanned aerial vehicle, and to construct a 3D model of the surrounding environment through the acquired scanning data and the unmanned aerial vehicle's own driving speed; the obstacle avoidance path judgment module is used to judge the surrounding environment, to determine the specific environmental conditions that require the unmanned aerial vehicle to avoid obstacles and the corresponding multiple obstacle avoidance schemes. The present invention can not only comprehensively consider multiple factors to determine whether the unmanned aerial vehicle needs to avoid obstacles on the forward route, but also screen the obstacle avoidance schemes according to the different daily behavior habits of the controller, and select the best obstacle avoidance scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to an unmanned aerial vehicle obstacle avoidance system based on radar control. Background Art

[0002] With the development of science and technology, drones have gradually come into the public eye, and are increasingly being used in people's lives, playing an important role in industry, agriculture, commerce and other areas.

[0003] At the same time, due to technological breakthroughs, the endurance of drones has been significantly improved, and they can work for a long time and reach longer distances. Combining radar with drones can enable drones to work over ultra-long distances. But at the same time, we still need to face a problem, that is, how to use radar to efficiently judge obstacles on the route ahead and effectively avoid them.

[0004] In view of the above situation, we need a radar-controlled drone obstacle avoidance system, which can not only comprehensively consider multiple factors to determine whether the drone needs to avoid obstacles on the forward route, but also screen the obstacle avoidance schemes according to the different daily behavior habits of the controller and select the best obstacle avoidance scheme. Summary of the invention

[0005] The purpose of the present invention is to provide a UAV obstacle avoidance system based on radar control to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a radar-controlled unmanned aerial vehicle obstacle avoidance system, comprising: a radar scanning composition module, an obstacle avoidance path judgment module, and an obstacle avoidance path recommendation module.

[0007] The radar scanning and mapping module is used to scan the terrain around the drone using the radar mounted on the drone, and to construct a 3D model of the surrounding environment using the acquired scanning data and the driving speed of the drone itself;

[0008] The obstacle avoidance path judgment module is used to judge the surrounding environment, determine the specific environmental conditions that require the drone to avoid obstacles and the corresponding multiple obstacle avoidance solutions;

[0009] The obstacle avoidance path recommendation module is used to receive the obstacle avoidance plan transmitted by the obstacle avoidance path judgment module, and select the obstacle avoidance plan according to the daily behavior habits and operation status of the drone control end operator, and provide the drone control end operator with the obstacle avoidance plan that is most suitable for execution in the current environment.

[0010] The present invention realizes the function of judging whether obstacle avoidance is needed and how to select the best obstacle avoidance plan through the collaborative cooperation of multiple modules. The radar scanning composition module mainly establishes a 3D model of the surrounding environment through the information collected by the radar. The obstacle avoidance path judgment module mainly realizes the function of judging whether the UAV needs to perform obstacle avoidance and provides eight obstacle avoidance plans in a targeted manner. The obstacle avoidance path judgment module mainly considers various factors comprehensively, screens the obstacle avoidance plans, and obtains the best obstacle avoidance plan.

[0011] Furthermore, the radar-controlled unmanned aerial vehicle obstacle avoidance system also includes a radar-controlled unmanned aerial vehicle obstacle avoidance method. The specific operation steps of the radar-controlled unmanned aerial vehicle obstacle avoidance method are as follows:

[0012] S1. In the radar scanning and composition module, the terrain around the drone is scanned by the radar mounted on the drone, and a 3D model of the surrounding environment is constructed based on the acquired scanning data and the driving speed of the drone itself;

[0013] S2, using the obstacle avoidance path judgment module to judge the surrounding environment, determine the specific environmental conditions where the drone needs to avoid obstacles and the corresponding multiple obstacle avoidance solutions;

[0014] S3. The obstacle avoidance path recommendation module is used to accept the obstacle avoidance plan transmitted by the obstacle avoidance path judgment module, and the obstacle avoidance plan is selected according to the daily behavior habits and operation status of the drone controller, so as to provide the drone controller with the obstacle avoidance plan that is most suitable for execution in the current environment.

[0015] Furthermore, the obstacle avoidance path judgment module includes: an obstacle avoidance behavior judgment module and an obstacle avoidance solution analysis module.

[0016] The obstacle avoidance behavior judgment module determines whether the UAV can pass normally according to the current driving state based on the 3D model of the surrounding environment constructed in the radar scanning composition module. If it cannot pass normally, obstacle avoidance is required;

[0017] The obstacle avoidance solution analysis module predicts the distance Q between the obstacle and each edge of the obstacle when the drone reaches the obstacle based on the current state of the drone. n , n is the direction of the obstacle. When the UAV is avoiding obstacles in the eight directions of above, below, left, right, upper left, lower left, upper right and lower right, the obstacle avoidance plan needs to be executed.

[0018] The obstacle avoidance path judgment module of the present invention includes an obstacle avoidance behavior judgment module and an obstacle avoidance solution analysis module. The obstacle avoidance behavior judgment module mainly functions to judge whether the UAV needs to perform obstacle avoidance behavior under the current situation. The obstacle avoidance solution analysis module mainly predicts the distance Q between the UAV and each edge of the obstacle when it reaches the obstacle based on the current state of the UAV. n Consider and obtain eight solutions for drone obstacle avoidance.

[0019] Furthermore, when determining whether the drone needs to perform obstacle avoidance behavior, the obstacle avoidance behavior judgment module needs to calculate the intersection point a of the straight line where the current drone's forward direction is located and the vertical plane where the obstacle is located, where the vertical plane where the obstacle is located is perpendicular to the drone's forward direction.

[0020] Get the size of the drone, calculate the main view of the drone from the nose to the tail, process the main view, get the smallest rectangle that can completely contain the main view, replace the intersection a with the center point of the rectangle, and if the rectangle overlaps with the obstacle in the vertical plane where the obstacle is located, then it is determined that the drone needs to avoid the obstacle.

[0021] At the same time, the obstacle avoidance behavior judgment module also obtains the straight-line flight records of the same operator at the control end during daily flight, and retrieves the straight-line flight records of the operator at a unit distance b. By establishing a spatial rectangular coordinate system, the flight trajectory of the drone controlled by the operator is fitted, and the average deviation distance c from the theoretical straight-line flight trajectory is calculated.

[0022] Divide the average deviation distance c by the unit distance b of the straight-line flight, and then we can get the tangent value of the deviation angle α when the operator controls the drone, that is, , and then get the deviation angle when the operator controls the drone ,

[0023] The value of the deviation angle α is related to the operator of the operating end. If the operator has good skills, the corresponding deviation angle α will be small.

[0024] Get the distance d between the current drone and the vertical plane where the obstacle is located, multiply d by the tangent value of the deviation angle α, and then get the deviation value e that the operator of the drone controls to drive the drone to the vertical plane where the obstacle is located, that is, ,

[0025] With the intersection a as the center of the circle and the distance deviation value e between the center point of the rectangle and the intersection a as the radius, calculate whether there is any overlap between the entire figure enclosed by the rectangle and the obstacle in the vertical plane where the obstacle is located. If there is an overlap, it is determined that the drone needs to avoid the obstacle.

[0026] The obstacle avoidance behavior judgment module of the present invention comprehensively considers factors such as the intersection point a of the straight line where the current UAV's forward direction is located and the vertical plane where the obstacle is located, the size of the UAV, the deviation angle α when the operator controls the UAV, and the distance d between the current UAV and the vertical plane where the obstacle is located. The deviation angle α when the operator controls the UAV is calculated to prevent driving deviations caused by the operator's personal habits. Considering this factor can make the obstacle avoidance behavior judgment module more accurate in determining whether the UAV needs to perform obstacle avoidance behavior in the current situation.

[0027] Furthermore, the obstacle avoidance solution analysis module predicts the distance Q from each edge of the obstacle when reaching the obstacle according to the current state. n , the distance d between the current UAV and the vertical plane where the obstacle is located and the deviation angle α when the operator controls the UAV,

[0028] Use the current state of the drone to predict the distance Q between the edges of the obstacle when it reaches the obstacle n Divide by the distance d between the current drone and the vertical plane where the obstacle is located, and get the tangent value of the angle β that the controller needs to avoid when avoiding the obstacle. , and then get the angle β that the controller needs to avoid when avoiding obstacles, that is, ,

[0029] The controller at the control end has a deviation angle α when controlling the drone.

[0030] When α and β are in the same direction, the actual adjustment angle is β+α.

[0031] When α and β are in opposite directions, the actual adjustment angle is β-α.

[0032] When the adjustment angle of the drone is less than β, the drone cannot avoid obstacles normally. To ensure that the actual adjustment angle of the drone is greater than or equal to β, the adjustment angle of the drone must be greater than or equal to β+α.

[0033] In the obstacle avoidance scheme corresponding to each direction, the action of the drone when the adjustment angle is β+α in that direction is counted, and the action is compared with the comparison database to obtain the difficulty coefficient f of the action. n and the time t required to complete the action n , where n is the direction of the obstacle, including eight directions: top, bottom, left, right, upper left, lower left, upper right, and lower right.

[0034] The obstacle avoidance scheme analysis module of the present invention comprehensively analyzes the deviation angle α when the operator controls the drone and the angle β that the operator at the control end needs to avoid when avoiding obstacles. One is considered from the personal habits of the operator, and the other is considered from the theoretical value of the calculation, so that these two factors affect the actual adjustment result. According to the two extreme value situations of the calculation, the actual adjustment angle range can be obtained as β-α~β+α. Therefore, in order to ensure that the actual adjustment angle of the drone is greater than or equal to β, when the operator at the control end controls the drone to fly with a deviation angle α, the adjustment angle set by the drone needs to be greater than or equal to β+α.

[0035] Furthermore, the obstacle avoidance path recommendation module includes: a driver behavior habit analysis module and an obstacle avoidance solution recommendation confirmation module.

[0036] The operator behavior analysis module is used to analyze the operator's behavior and operation status during daily operation of the drone.

[0037] The obstacle avoidance scheme recommendation and confirmation module screens the obstacle avoidance schemes according to the results obtained by the operator behavior habit analysis module, and then obtains the best obstacle avoidance scheme.

[0038] The obstacle avoidance path recommendation module of the present invention includes a controller behavior habit analysis module and an obstacle avoidance plan recommendation confirmation module. The controller behavior habit analysis module is mainly used to analyze and process the daily behavior data of the controller corresponding to the drone, and then the obstacle avoidance plan recommendation confirmation module screens the obstacle avoidance plan according to the data processing results.

[0039] Furthermore, the operator behavior analysis module counts the number of actions performed by the operator at the control end when operating the drone on a daily basis, and calculates the average heart rate w of the operator before performing each action.

[0040] Sort the actions in descending order of difficulty coefficient, starting from the one with the highest difficulty coefficient, and determine the number of times the action corresponding to the difficulty coefficient has been completed.

[0041] When the number of times the action corresponding to the difficulty coefficient is completed is greater than or equal to the first preset value, the operator's operation coefficient is determined to be the difficulty coefficient;

[0042] When the number of times the action corresponding to the difficulty coefficient is completed is less than the first preset value, the number of times of the next ranking action is compared with the first preset value in sequence until the operator's operation coefficient is obtained.

[0043] The operator behavior habit analysis module of the present invention analyzes the number of actions completed by the control end operator when operating the drone daily and the average heart rate w of the operator before performing each action. The number of actions completed corresponding to the difficulty coefficient is compared with the first preset value, and then the operator operation coefficient can be obtained. The operator operation coefficient can more directly reflect the operator's skills. The better the skills, the corresponding operator operation coefficient, so it can be used to screen obstacle avoidance solutions.

[0044] Furthermore, the obstacle avoidance scheme recommendation and confirmation module screens the eight obstacle avoidance schemes and obtains the difficulty coefficients f of the corresponding actions in the eight obstacle avoidance schemes respectively. n and the time t required to complete the action n ,

[0045] The operation coefficient corresponding to the controller and the difficulty coefficient f n For comparison,

[0046] When the operating coefficient is greater than or equal to f n When , it is determined that for the controller of the control end, the f n The corresponding obstacle avoidance scheme is feasible.

[0047] When the operating coefficient is less than f n When , it is determined that for the controller of the control end, the f n The corresponding obstacle avoidance scheme is not feasible, so the obstacle avoidance scheme is eliminated;

[0048] Count the feasible obstacle avoidance solutions, and further select the minimum time required to complete the corresponding action among all feasible obstacle avoidance solutions , get The difficulty coefficient f1 of the corresponding action in the obstacle avoidance solution,

[0049] When t n and The difference between t and t is greater than or equal to the second preset value, then it is determined that t n The corresponding obstacle avoidance solution has no execution value, so it is eliminated.

[0050] When t n and The difference between t and t is greater than or equal to the third preset value and less than the second preset value, and the t n The corresponding f n If it is greater than or equal to f1, then it is determined that t n The corresponding obstacle avoidance solution has no execution value, so it is eliminated.

[0051] On the contrary, the obstacle avoidance scheme has execution value;

[0052] Get the operator's heart rate v at this time and the operator's average heart rate w before performing the corresponding actions in each obstacle avoidance scheme with execution value in daily life n ,

[0053] When n When the difference between v and v is greater than or equal to the fourth preset value, the obstacle avoidance scheme that meets the condition is extracted.

[0054] When n When the differences with v are all less than the fourth preset value, all obstacle avoidance solutions with execution value are directly extracted;

[0055] The number of corresponding actions in the extracted obstacle avoidance schemes completed by the operator at the control end during daily control of the drone is compared, and the obstacle avoidance scheme corresponding to the action with the largest number of actions is taken as the obstacle avoidance scheme most suitable for the operator at the control end of the drone to execute in the current environment, that is, the optimal obstacle avoidance scheme.

[0056] The obstacle avoidance scheme recommendation confirmation module of the present invention first calculates the operation coefficient corresponding to the operator of the control end and the difficulty coefficient f n Compare the operator's ability to implement each obstacle avoidance plan, and determine the obstacle avoidance plan that exceeds the ability range as an unimplementable plan and eliminate it; then select the minimum time required to complete the corresponding action among all feasible obstacle avoidance plans. , get The difficulty coefficient f1 of the corresponding action in the obstacle avoidance scheme is determined by judging t n and The difference between the size of f1 and the size of t n and The difference between is greater than or equal to the second preset value, which means that t n Corresponding obstacle avoidance solution ratio The time difference of the corresponding obstacle avoidance solution is too large, which is a waste of time and not feasible. n and The difference between t and t is greater than or equal to the third preset value and less than the second preset value, and the t n The corresponding f n is greater than or equal to f1, then t n Corresponding obstacle avoidance solution ratio The corresponding obstacle avoidance solution not only takes more time, but also has a higher difficulty coefficient, so it has no execution value. Finally, the heart rate v of the operator at this time is compared with the average heart rate w of the operator before performing the corresponding actions in each obstacle avoidance solution with execution value in daily life. n For comparison, because heart rate can reflect a person's nervousness, the higher the heart rate, the more nervous the person is, and then choose w nThe solution corresponding to the difference with v being greater than or equal to the third preset value is to prevent the operator from being too nervous, resulting in mistakes when executing an obstacle avoidance solution with a high difficulty coefficient, thereby causing obstacle avoidance failure.

[0057] Furthermore, the obstacle avoidance path recommendation module will record each obstacle avoidance situation and the execution result of the operator at the drone control end respectively, and use it as research data for the corresponding operator. When the same situation occurs next time, it will be used as an alternative obstacle avoidance plan when recommending an obstacle avoidance path to the operator.

[0058] The alternative obstacle avoidance scheme in the present invention is only a reference scheme and does not participate in the screening of the normal obstacle avoidance scheme.

[0059] Compared with the prior art, the beneficial effect achieved by the present invention is that the present invention can not only comprehensively consider multiple factors to determine whether the drone needs to avoid obstacles on the forward route, but also screen the obstacle avoidance schemes according to the different daily behavior habits of the controller, and select the best obstacle avoidance scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0061] Figure 1 It is a schematic diagram of the composition of a radar-controlled unmanned aerial vehicle obstacle avoidance system of the present invention;

[0062] Figure 2 It is a structural schematic diagram of a deviation angle α when a controller of a UAV is controlling a UAV in an obstacle avoidance system based on radar control according to the present invention;

[0063] Figure 3 It is a schematic diagram of a flow chart of an obstacle avoidance behavior judgment module in a radar-controlled unmanned aerial vehicle obstacle avoidance system of the present invention judging whether the unmanned aerial vehicle needs to perform obstacle avoidance behavior;

[0064] Figure 4 The present invention is a flowchart of an obstacle avoidance scheme recommendation and confirmation module in an obstacle avoidance path recommendation module of an unmanned aerial vehicle obstacle avoidance system based on radar control for screening the best obstacle avoidance scheme. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] See also Figure 1-Figure 4 The present invention provides a technical solution: a radar-controlled unmanned aerial vehicle obstacle avoidance system, comprising: a radar scanning composition module, an obstacle avoidance path judgment module, and an obstacle avoidance path recommendation module.

[0067] The radar scanning and mapping module is used to scan the terrain around the drone using the radar mounted on the drone, and to construct a 3D model of the surrounding environment using the acquired scanning data and the driving speed of the drone itself;

[0068] The obstacle avoidance path judgment module is used to judge the surrounding environment, determine the specific environmental conditions that require the drone to avoid obstacles and the corresponding multiple obstacle avoidance solutions;

[0069] The obstacle avoidance path recommendation module is used to receive the obstacle avoidance plan transmitted by the obstacle avoidance path judgment module, and select the obstacle avoidance plan according to the daily behavior habits and operation status of the drone control end operator, and provide the drone control end operator with the obstacle avoidance plan that is most suitable for execution in the current environment.

[0070] The present invention realizes the function of judging whether obstacle avoidance is needed and how to select the best obstacle avoidance plan through the collaborative cooperation of multiple modules. The radar scanning composition module mainly establishes a 3D model of the surrounding environment through the information collected by the radar. The obstacle avoidance path judgment module mainly realizes the function of judging whether the UAV needs to perform obstacle avoidance and provides eight obstacle avoidance plans in a targeted manner. The obstacle avoidance path judgment module mainly considers various factors comprehensively, screens the obstacle avoidance plans, and obtains the best obstacle avoidance plan.

[0071] The radar-controlled unmanned aerial vehicle obstacle avoidance system also includes a radar-controlled unmanned aerial vehicle obstacle avoidance method. The specific operation steps of the radar-controlled unmanned aerial vehicle obstacle avoidance method are as follows:

[0072] S1. In the radar scanning and composition module, the terrain around the drone is scanned by the radar mounted on the drone, and a 3D model of the surrounding environment is constructed based on the acquired scanning data and the driving speed of the drone itself;

[0073] S2, using the obstacle avoidance path judgment module to judge the surrounding environment, determine the specific environmental conditions where the drone needs to avoid obstacles and the corresponding multiple obstacle avoidance solutions;

[0074] S3. The obstacle avoidance path recommendation module is used to accept the obstacle avoidance plan transmitted by the obstacle avoidance path judgment module, and the obstacle avoidance plan is selected according to the daily behavior habits and operation status of the drone controller, so as to provide the drone controller with the obstacle avoidance plan that is most suitable for execution in the current environment.

[0075] The obstacle avoidance path judgment module includes: an obstacle avoidance behavior judgment module and an obstacle avoidance solution analysis module.

[0076] The obstacle avoidance behavior judgment module determines whether the UAV can pass normally according to the current driving state based on the 3D model of the surrounding environment constructed in the radar scanning composition module. If it cannot pass normally, obstacle avoidance is required;

[0077] The obstacle avoidance solution analysis module predicts the distance Q between the obstacle and each edge of the obstacle when the drone reaches the obstacle based on the current state of the drone. n , n is the direction of the obstacle. When the UAV is avoiding obstacles in the eight directions of above, below, left, right, upper left, lower left, upper right and lower right, the obstacle avoidance plan needs to be executed.

[0078] The obstacle avoidance path judgment module of the present invention includes an obstacle avoidance behavior judgment module and an obstacle avoidance solution analysis module. The obstacle avoidance behavior judgment module mainly functions to judge whether the UAV needs to perform obstacle avoidance behavior under the current situation. The obstacle avoidance solution analysis module mainly predicts the distance Q between the UAV and each edge of the obstacle when it reaches the obstacle based on the current state of the UAV. n Consider and obtain eight solutions for drone obstacle avoidance.

[0079] When determining whether the drone needs to perform obstacle avoidance, the obstacle avoidance behavior judgment module needs to calculate the intersection point a between the straight line where the current drone's forward direction is located and the vertical plane where the obstacle is located, where the vertical plane where the obstacle is located is perpendicular to the drone's forward direction.

[0080] Get the size of the drone, calculate the main view of the drone from the nose to the tail, process the main view, get the smallest rectangle that can completely contain the main view, replace the intersection a with the center point of the rectangle, and if the rectangle overlaps with the obstacle in the vertical plane where the obstacle is located, then it is determined that the drone needs to avoid the obstacle.

[0081] At the same time, the obstacle avoidance behavior judgment module also obtains the straight-line flight records of the same operator at the control end during daily flight, and retrieves the straight-line flight records of the operator at a unit distance b. Figure 2 The distance b from the starting point to the actual arrival point is fitted by establishing a spatial rectangular coordinate system, and the average deviation distance c from the theoretical straight line flight trajectory of the drone is calculated when the drone is flying in a straight line, as shown in Figure 2 The distance c between the actual arrival point and the theoretical arrival point;

[0082] Divide the average deviation distance c by the unit distance b of the straight-line flight, and then we can get the tangent value of the deviation angle α when the operator controls the drone, that is, , and then get the deviation angle when the operator controls the drone ,

[0083] The value of the deviation angle α is related to the operator of the operating end. If the operator has good skills, the corresponding deviation angle α will be small.

[0084] Get the distance d between the current drone and the vertical plane where the obstacle is located, multiply d by the tangent value of the deviation angle α, and then get the deviation value e that the operator of the drone controls to drive the drone to the vertical plane where the obstacle is located, that is, ,

[0085] In this embodiment, if a certain operator operates a certain drone to fly a straight line with a unit distance of 2000 meters, and the average deviation distance from the normal straight line flight trajectory is 3 meters, the current distance between the drone and the vertical plane where the obstacle is located is rice,

[0086] The tangent value of the deviation angle α when the operator controls the drone is ,

[0087] The control end controls the operator of the drone to control the deviation value of the vertical plane where the obstacle is located.

[0088] rice.

[0089] With the intersection a as the center of the circle and the distance deviation value e between the center point of the rectangle and the intersection a as the radius, calculate whether there is any overlap between the entire figure enclosed by the rectangle and the obstacle in the vertical plane where the obstacle is located. If there is an overlap, it is determined that the drone needs to avoid the obstacle.

[0090] The obstacle avoidance behavior judgment module of the present invention comprehensively considers factors such as the intersection point a of the straight line where the current UAV's forward direction is located and the vertical plane where the obstacle is located, the size of the UAV, the deviation angle α when the operator controls the UAV, and the distance d between the current UAV and the vertical plane where the obstacle is located. The deviation angle α when the operator controls the UAV is calculated to prevent driving deviations caused by the operator's personal habits. Considering this factor can make the obstacle avoidance behavior judgment module more accurate in determining whether the UAV needs to perform obstacle avoidance behavior in the current situation.

[0091] The obstacle avoidance solution analysis module predicts the distance Q from each edge of the obstacle when reaching the obstacle based on the current state. n , the distance d between the current UAV and the vertical plane where the obstacle is located and the deviation angle α when the operator controls the UAV,

[0092] Use the current state of the drone to predict the distance Q between the edges of the obstacle when it reaches the obstacle nDivide by the distance d between the current drone and the vertical plane where the obstacle is located, and get the tangent value of the angle β that the controller needs to avoid when avoiding the obstacle. , and then get the angle β that the controller needs to avoid when avoiding obstacles, that is, ,

[0093] The controller at the control end has a deviation angle α when controlling the drone.

[0094] When α and β are in the same direction, the actual adjustment angle is β+α.

[0095] When α and β are in opposite directions, the actual adjustment angle is β-α.

[0096] When the adjustment angle of the drone is less than β, the drone cannot avoid obstacles normally. To ensure that the actual adjustment angle of the drone is greater than or equal to β, the adjustment angle of the drone must be greater than or equal to β+α.

[0097] In this embodiment, if α is 0.08 degrees and β is 30 degrees,

[0098] When α and β are in the same direction, the actual adjustment angle is β+α=30+0.08=30.08 degrees.

[0099] When α and β are in opposite directions, the actual adjustment angle is β-α=30-0.08=29.92 degrees.

[0100] The actual adjustment angle of the drone ranges from 29.92 to 30.08 degrees. Therefore, when performing obstacle avoidance in this situation, to ensure that the actual adjustment angle of the drone is greater than or equal to 30 degrees, the adjustment angle set by the drone must be greater than or equal to 30.08 degrees.

[0101] In the obstacle avoidance scheme corresponding to each direction, the action of the drone when the adjustment angle is β+α in that direction is counted, and the action is compared with the comparison database to obtain the difficulty coefficient f of the action. n and the time t required to complete the action n , where n is the direction of the obstacle, including eight directions: top, bottom, left, right, upper left, lower left, upper right, and lower right.

[0102] The obstacle avoidance scheme analysis module of the present invention comprehensively analyzes the deviation angle α when the operator controls the drone and the angle β that the operator at the control end needs to avoid when avoiding obstacles. One is considered from the personal habits of the operator, and the other is considered from the theoretical value of the calculation, so that these two factors affect the actual adjustment result. According to the two extreme value situations of the calculation, the actual adjustment angle range can be obtained as β-α~β+α. Therefore, in order to ensure that the actual adjustment angle of the drone is greater than or equal to β, when the operator at the control end controls the drone to fly with a deviation angle α, the adjustment angle set by the drone needs to be greater than or equal to β+α.

[0103] The obstacle avoidance path recommendation module includes: a driver behavior habit analysis module and an obstacle avoidance solution recommendation confirmation module.

[0104] The operator behavior analysis module is used to analyze the operator's behavior and operation status during daily operation of the drone.

[0105] The obstacle avoidance scheme recommendation and confirmation module screens the obstacle avoidance schemes according to the results obtained by the operator behavior habit analysis module, and then obtains the best obstacle avoidance scheme.

[0106] The obstacle avoidance path recommendation module of the present invention includes a controller behavior habit analysis module and an obstacle avoidance plan recommendation confirmation module. The controller behavior habit analysis module is mainly used to analyze and process the daily behavior data of the controller corresponding to the drone, and then the obstacle avoidance plan recommendation confirmation module screens the obstacle avoidance plan according to the data processing results.

[0107] The operator behavior analysis module counts the number of actions performed by the operator at the control end when operating the drone on a daily basis, and calculates the average heart rate w of the operator before performing each action.

[0108] Sort the actions in descending order of difficulty coefficient, starting from the one with the highest difficulty coefficient, and determine the number of times the action corresponding to the difficulty coefficient has been completed.

[0109] When the number of times the action corresponding to the difficulty coefficient is completed is greater than or equal to the first preset value, the operator's operation coefficient is determined to be the difficulty coefficient;

[0110] When the number of times the action corresponding to the difficulty coefficient is completed is less than the first preset value, the number of times of the next ranking action is compared with the first preset value in sequence until the operator's operation coefficient is obtained.

[0111] In this embodiment, if the operator A completes action x1 ranked first in difficulty 3 times, and completes action x2 ranked second in difficulty 80 times, the first preset value is 50,

[0112] Because 3<50, 80>50, the controller's operation coefficient is the difficulty coefficient value corresponding to action x2.

[0113] The operator behavior habit analysis module of the present invention analyzes the number of actions completed by the control end operator when operating the drone daily and the average heart rate w of the operator before performing each action. The number of actions completed corresponding to the difficulty coefficient is compared with the first preset value, and then the operator operation coefficient can be obtained. The operator operation coefficient can more directly reflect the operator's skills. The better the skills, the corresponding operator operation coefficient, so it can be used to screen obstacle avoidance solutions.

[0114] The obstacle avoidance scheme recommendation and confirmation module screens eight obstacle avoidance schemes and obtains the difficulty coefficients f of the corresponding actions in the eight obstacle avoidance schemes respectively. n and the time t required to complete the action n ,

[0115] The operation coefficient corresponding to the controller and the difficulty coefficient f n For comparison,

[0116] When the operating coefficient is greater than or equal to f n When , it is determined that for the controller of the control end, the f n The corresponding obstacle avoidance scheme is feasible.

[0117] When the operating coefficient is less than f n When , it is determined that for the controller of the control end, the f n The corresponding obstacle avoidance scheme is not feasible, so the obstacle avoidance scheme is eliminated;

[0118] Count the feasible obstacle avoidance solutions, and further select the minimum time required to complete the corresponding action among all feasible obstacle avoidance solutions , get The difficulty coefficient f1 of the corresponding action in the obstacle avoidance solution,

[0119] When t n and The difference between t and t is greater than or equal to the second preset value, then it is determined that t n The corresponding obstacle avoidance solution has no execution value, so it is eliminated.

[0120] When t n and The difference between t and t is greater than or equal to the third preset value and less than the second preset value, and the t n The corresponding f n If it is greater than or equal to f1, then it is determined that t nThe corresponding obstacle avoidance solution has no execution value, so it is eliminated.

[0121] On the contrary, the obstacle avoidance scheme has execution value;

[0122] In this embodiment, if an implementable obstacle avoidance solution corresponds to t n for 30 seconds, The value is 22 seconds, the second preset value is 5 seconds,

[0123] Because 30-22=8 seconds, and 8>5, it is determined that the feasible obstacle avoidance solution has no execution value.

[0124] Get the operator's heart rate v at this time and the operator's average heart rate w before performing the corresponding actions in each obstacle avoidance scheme with execution value in daily life n ,

[0125] When n When the difference between v and v is greater than or equal to the fourth preset value, the obstacle avoidance scheme that meets the condition is extracted.

[0126] When n When the differences with v are all less than the fourth preset value, all obstacle avoidance solutions with execution value are directly extracted;

[0127] The number of corresponding actions in the extracted obstacle avoidance schemes completed by the operator at the control end during daily control of the drone is compared, and the obstacle avoidance scheme corresponding to the action with the largest number of actions is taken as the obstacle avoidance scheme most suitable for the operator at the control end of the drone to execute in the current environment, that is, the optimal obstacle avoidance scheme.

[0128] The obstacle avoidance scheme recommendation confirmation module of the present invention first calculates the operation coefficient corresponding to the operator of the control end and the difficulty coefficient f n Compare the operator's ability to implement each obstacle avoidance plan, and determine the obstacle avoidance plan that exceeds the ability range as an unimplementable plan and eliminate it; then select the minimum time required to complete the corresponding action among all feasible obstacle avoidance plans. , get The difficulty coefficient f1 of the corresponding action in the obstacle avoidance scheme is determined by judging t n and The difference between the size of f1 and the size of f1, if t n and The difference between is greater than or equal to the second preset value, which means that t n Corresponding obstacle avoidance solution ratio The time difference of the corresponding obstacle avoidance solution is too large, which is a waste of time and not feasible. n and The difference between t and t is greater than or equal to the third preset value and less than the second preset value, and the t nThe corresponding f n is greater than or equal to f1, then t n Corresponding obstacle avoidance solution ratio The corresponding obstacle avoidance solution not only takes more time, but also has a higher difficulty coefficient, so it has no execution value. Finally, the heart rate v of the operator at this time is compared with the average heart rate w of the operator before performing the corresponding actions in each obstacle avoidance solution with execution value in daily life. n For comparison, because heart rate can reflect a person's nervousness, the higher the heart rate, the more nervous the person is, and then choose w n The solution corresponding to the difference with v being greater than or equal to the third preset value is to prevent the operator from being too nervous, resulting in mistakes when executing an obstacle avoidance solution with a high difficulty coefficient, thereby causing obstacle avoidance failure.

[0129] The obstacle avoidance path recommendation module will record each obstacle avoidance situation and the execution results of the drone control end operator respectively, and use it as research data for the corresponding operator. When the same situation occurs next time, it will be used as an alternative obstacle avoidance plan when recommending an obstacle avoidance path to the operator.

[0130] The alternative obstacle avoidance scheme in the present invention is only a reference scheme and does not participate in the screening of the normal obstacle avoidance scheme.

[0131] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, 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 process, method, article or device.

[0132] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A radar-controlled drone obstacle avoidance system, characterized in that: include: Radar scanning composition module, obstacle avoidance path judgment module, obstacle avoidance path recommendation module, The radar scanning and mapping module is used to scan the terrain around the drone using the radar mounted on the drone, and to construct a 3D model of the surrounding environment using the acquired scanning data and the driving speed of the drone itself; The obstacle avoidance path judgment module is used to judge the surrounding environment, determine the specific environmental conditions that require the drone to avoid obstacles and the corresponding multiple obstacle avoidance solutions; The obstacle avoidance path judgment module includes: an obstacle avoidance behavior judgment module and an obstacle avoidance solution analysis module. The obstacle avoidance behavior judgment module determines whether the UAV can pass normally according to the current driving state based on the 3D model of the surrounding environment constructed in the radar scanning composition module. If it cannot pass normally, obstacle avoidance is required; The obstacle avoidance solution analysis module predicts the distance Q between the obstacle and each edge of the obstacle when the drone reaches the obstacle based on the current state of the drone. n , n is the direction of the obstacle, and the obstacle avoidance scheme that the drone needs to execute when avoiding obstacles in the eight directions of above, below, left, right, upper left, lower left, upper right, and lower right respectively; When determining whether the drone needs to perform obstacle avoidance, the obstacle avoidance behavior judgment module needs to calculate the intersection point a between the straight line where the current drone's forward direction is located and the vertical plane where the obstacle is located, where the vertical plane where the obstacle is located is perpendicular to the drone's forward direction. Get the size of the drone, calculate the main view of the drone from the nose to the tail, process the main view, get the smallest rectangle that can completely contain the main view, replace the intersection a with the center point of the rectangle, and if the rectangle overlaps with the obstacle in the vertical plane where the obstacle is located, then it is determined that the drone needs to avoid the obstacle. At the same time, the obstacle avoidance behavior judgment module also obtains the straight-line flight records of the same operator at the control end during daily flight, and retrieves the straight-line flight records of the operator at a unit distance b. By establishing a spatial rectangular coordinate system, the flight trajectory of the drone controlled by the operator is fitted, and the average deviation distance c from the theoretical straight-line flight trajectory is calculated. Divide the average deviation distance c by the unit distance b of the straight-line flight, and then we can get the tangent value of the deviation angle α when the operator controls the drone, that is, , and then get the deviation angle when the operator controls the drone , The value of the deviation angle α is related to the operator of the operating end. If the operator has good skills, the corresponding deviation angle α will be small. Get the distance d between the current drone and the vertical plane where the obstacle is located, multiply d by the tangent value of the deviation angle α, and then get the deviation value e that the operator of the drone controls to drive the drone to the vertical plane where the obstacle is located, that is, , With the intersection point a as the center of the circle and the distance deviation value e between the center point of the rectangle and the intersection point a as the radius, calculate whether the whole figure enclosed by the rectangle overlaps with the obstacle in the vertical plane where the obstacle is located. If there is an overlap, it is determined that the drone needs to avoid the obstacle; The obstacle avoidance path recommendation module is used to receive the obstacle avoidance plan transmitted by the obstacle avoidance path judgment module, and select the obstacle avoidance plan according to the daily behavior habits and operation status of the drone control end operator, and provide the drone control end operator with the obstacle avoidance plan that is most suitable for execution in the current environment.

2. The radar-controlled unmanned aerial vehicle obstacle avoidance system according to claim 1, characterized in that: The radar-controlled unmanned aerial vehicle obstacle avoidance system also includes a radar-controlled unmanned aerial vehicle obstacle avoidance method. The specific operation steps of the radar-controlled unmanned aerial vehicle obstacle avoidance method are as follows: S1. In the radar scanning and composition module, the terrain around the drone is scanned by the radar mounted on the drone, and a 3D model of the surrounding environment is constructed based on the acquired scanning data and the driving speed of the drone itself; S2, using the obstacle avoidance path judgment module to judge the surrounding environment, determine the specific environmental conditions where the drone needs to avoid obstacles and the corresponding multiple obstacle avoidance solutions; S3. The obstacle avoidance path recommendation module is used to accept the obstacle avoidance plan transmitted by the obstacle avoidance path judgment module, and the obstacle avoidance plan is selected according to the daily behavior habits and operation status of the drone controller, so as to provide the drone controller with the obstacle avoidance plan that is most suitable for execution in the current environment.

3. The radar-controlled unmanned aerial vehicle obstacle avoidance system according to claim 2, characterized in that: The obstacle avoidance solution analysis module predicts the distance Q from each edge of the obstacle when reaching the obstacle based on the current state. n , the distance d between the current UAV and the vertical plane where the obstacle is located and the deviation angle α when the operator controls the UAV, Use the current state of the drone to predict the distance Q between the edges of the obstacle when it reaches the obstacle n Divide by the distance d between the current drone and the vertical plane where the obstacle is located, and get the tangent value of the angle β that the controller needs to avoid when avoiding the obstacle. , and then get the angle β that the controller needs to avoid when avoiding obstacles, that is, , The controller at the control end has a deviation angle α when controlling the drone. When α and β are in the same direction, the actual adjustment angle is β+α. When α and β are in opposite directions, the actual adjustment angle is β-α. When the adjustment angle of the drone is less than β, the drone cannot avoid obstacles normally. To ensure that the actual adjustment angle of the drone is greater than or equal to β, the adjustment angle of the drone must be greater than or equal to β+α. In the obstacle avoidance scheme corresponding to each direction, the action of the drone when the adjustment angle is β+α in the direction is counted, and the action is compared with the comparison database to obtain the difficulty coefficient f of the action. n and the time t required to complete the action n , where n is the direction of the obstacle, including eight directions: top, bottom, left, right, upper left, lower left, upper right, and lower right.

4. The radar-controlled unmanned aerial vehicle obstacle avoidance system according to claim 3, characterized in that: The obstacle avoidance path recommendation module includes: a driver behavior habit analysis module and an obstacle avoidance solution recommendation confirmation module. The operator behavior analysis module is used to analyze the operator's behavior and operation status during daily operation of the drone. The obstacle avoidance scheme recommendation and confirmation module screens the obstacle avoidance schemes according to the results obtained by the operator behavior habit analysis module, and then obtains the best obstacle avoidance scheme.

5. The radar-controlled unmanned aerial vehicle obstacle avoidance system according to claim 4, characterized in that: The operator behavior analysis module counts the number of actions performed by the operator at the control end when operating the drone on a daily basis, and calculates the average heart rate w of the operator before performing each action. Sort the actions in descending order of difficulty coefficient, starting from the one with the highest difficulty coefficient, and determine the number of times the action corresponding to the difficulty coefficient has been completed. When the number of times the action corresponding to the difficulty coefficient is completed is greater than or equal to the first preset value, the operator's operation coefficient is determined to be the difficulty coefficient; When the number of times the action corresponding to the difficulty coefficient is completed is less than the first preset value, the number of times of the next ranking action is compared with the first preset value in sequence until the operator's operation coefficient is obtained.

6. The radar-controlled unmanned aerial vehicle obstacle avoidance system according to claim 5, characterized in that: The obstacle avoidance scheme recommendation and confirmation module screens eight obstacle avoidance schemes and obtains the difficulty coefficients f of the corresponding actions in the eight obstacle avoidance schemes respectively. n and the time t required to complete the action n , The operation coefficient corresponding to the controller and the difficulty coefficient f n For comparison, When the operating coefficient is greater than or equal to f n When , it is determined that for the controller of the control end, the f n The corresponding obstacle avoidance scheme is feasible. When the operating coefficient is less than f n When , it is determined that for the controller of the control end, the f n The corresponding obstacle avoidance scheme is not feasible, so the obstacle avoidance scheme is eliminated; Count the feasible obstacle avoidance solutions, and further select the minimum time required to complete the corresponding action among all feasible obstacle avoidance solutions , get The difficulty coefficient f1 of the corresponding action in the obstacle avoidance solution, When t n and The difference between t and t is greater than or equal to the second preset value, then it is determined that t n The corresponding obstacle avoidance solution has no execution value, so it is eliminated. When t n and The difference between t and t is greater than or equal to the third preset value and less than the second preset value, and the t n The corresponding f n If it is greater than or equal to f1, then it is determined that t n The corresponding obstacle avoidance solution has no execution value, so it is eliminated. On the contrary, the obstacle avoidance scheme has execution value; Get the operator's heart rate v at this time and the operator's average heart rate w before performing the corresponding actions in each obstacle avoidance scheme with execution value in daily life n , When n When the difference between v and v is greater than or equal to the fourth preset value, the obstacle avoidance scheme that meets the condition is extracted. When n When the differences with v are all less than the fourth preset value, all obstacle avoidance solutions with execution value are directly extracted; The number of corresponding actions in the extracted obstacle avoidance schemes completed by the operator at the control end during daily control of the drone is compared, and the obstacle avoidance scheme corresponding to the action with the largest number of actions is taken as the obstacle avoidance scheme most suitable for the operator at the control end of the drone to execute in the current environment, that is, the optimal obstacle avoidance scheme.

7. The radar-controlled unmanned aerial vehicle obstacle avoidance system according to claim 6, characterized in that: The obstacle avoidance path recommendation module will record each obstacle avoidance situation and the execution results of the drone control end operator respectively, and use it as research data for the corresponding operator. When the same situation occurs next time, it will be used as an alternative obstacle avoidance plan when recommending an obstacle avoidance path to the operator.

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