CN117434967B - Unmanned aerial vehicle anti-collision detection method, system, medium and equipment - Google Patents
Unmanned aerial vehicle anti-collision detection method, system, medium and equipment Download PDFInfo
- Publication number
- CN117434967B CN117434967B CN202311742611.9A CN202311742611A CN117434967B CN 117434967 B CN117434967 B CN 117434967B CN 202311742611 A CN202311742611 A CN 202311742611A CN 117434967 B CN117434967 B CN 117434967B
- Authority
- CN
- China
- Prior art keywords
- unmanned aerial
- target
- aerial vehicle
- influence
- flight
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 82
- 238000000034 method Methods 0.000 claims abstract description 43
- 230000001133 acceleration Effects 0.000 claims description 66
- 230000006870 function Effects 0.000 claims description 30
- 238000004422 calculation algorithm Methods 0.000 claims description 23
- 238000006073 displacement reaction Methods 0.000 claims description 16
- 238000012502 risk assessment Methods 0.000 claims description 13
- 238000004364 calculation method Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 abstract description 13
- 238000004891 communication Methods 0.000 description 8
- 238000012545 processing Methods 0.000 description 7
- 230000009471 action Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000004590 computer program Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000006978 adaptation Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 230000000386 athletic effect Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000007704 transition Effects 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 238000009877 rendering Methods 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Traffic Control Systems (AREA)
Abstract
An unmanned aerial vehicle anti-collision detection method, an unmanned aerial vehicle anti-collision detection system, an unmanned aerial vehicle anti-collision detection medium and unmanned aerial vehicle anti-collision detection equipment relate to the technical field of collision detection. The method comprises the following steps: acquiring inherent flight parameters and flight trajectories of the unmanned aerial vehicle, and determining a first influence area affecting the flight trajectories according to the inherent flight parameters; determining a target collision object in the first influence area, and acquiring motion information of the target collision object; determining a second influence area of the target collision object on the flight track in the first influence area based on the motion information; calculating a risk score value of the target collision object on the unmanned aerial vehicle flight based on the motion information and the second influence area; and adjusting the flight track based on the risk score value to obtain a target flight track, and controlling the unmanned aerial vehicle to fly according to the target flight track. By means of the technical scheme, collision risk in the flight process of the unmanned aerial vehicle can be reduced.
Description
Technical Field
The application relates to the technical field of collision detection, in particular to an unmanned aerial vehicle collision detection method, an unmanned aerial vehicle collision detection system, a unmanned aerial vehicle collision detection medium and unmanned aerial vehicle collision detection equipment.
Background
With the wide application of unmanned aerial vehicles in military, civil and commercial fields, the requirements on the flight safety of unmanned aerial vehicles are increasingly increased. Meanwhile, the unmanned aerial vehicle flies in cities, densely populated areas and other air areas to bring more collision risks, so that unmanned aerial vehicle anti-collision detection technology is generated.
Early unmanned aerial vehicle anticollision detection techniques mainly relied on GPS navigation system and remote control to operate to avoid collisions, but this method has limited effect in complex environments. With the progress of sensor technology and the development of artificial intelligence algorithms, various sensors including vision sensors, laser radars, infrared and ultrasonic sensors and the like are beginning to be applied to unmanned aerial vehicle collision avoidance detection, so that the autonomous obstacle avoidance capability of the unmanned aerial vehicle is improved.
Currently, unmanned aerial vehicle collision avoidance technology has made significant progress, and many commercial and consumer unmanned aerial vehicles are equipped with a degree of collision avoidance systems that can avoid collisions with obstacles to a certain extent, but still face challenges in complex and varied environments.
Disclosure of Invention
The application provides an unmanned aerial vehicle anticollision detection method, system, medium and equipment, can reduce unmanned aerial vehicle's in-process collision risk.
In a first aspect, the present application provides a method for collision avoidance detection of a unmanned aerial vehicle, the method comprising:
acquiring inherent flight parameters and flight trajectories of the unmanned aerial vehicle, and determining a first influence area affecting the flight trajectories according to the inherent flight parameters;
determining a target collision object in the first influence area, and acquiring motion information of the target collision object;
determining a second influence area of the target collision object on the flight track in the first influence area based on the motion information;
calculating a risk score value of the target collision object on the unmanned aerial vehicle flight based on the motion information and the second influence area;
and adjusting the flight track based on the risk score value to obtain a target flight track, and controlling the unmanned aerial vehicle to fly according to the target flight track.
Through adopting above-mentioned technical scheme, confirm its first influence region through unmanned aerial vehicle's inherent flight parameter, rationally predict unmanned aerial vehicle's maneuvering range under the current state, consider unmanned aerial vehicle's self flight characteristic in collision detection, confirm the second influence region of target collision thing to the flight orbit in first influence region, can confirm the potential influence region of target collision thing to unmanned aerial vehicle flight orbit, calculate the risk score value of this target collision thing to unmanned aerial vehicle flight based on the motion information of target collision thing and second influence region, through fusing the risk of collision threat and unmanned aerial vehicle region of target self, can realize more comprehensive and accurate risk assessment, actively plan unmanned aerial vehicle's flight orbit according to the collision risk score, can avoid the collision risk in advance pertinently, reduce unmanned aerial vehicle's in-process collision risk.
Optionally, the intrinsic flight parameters include a maximum sideslip angle, a maximum attack angle and a maximum overload, and the determining, according to the intrinsic flight parameters, a first influence area affecting the flight trajectory includes: calculating the maximum maneuvering acceleration of the unmanned aerial vehicle according to the maximum overload, and calculating the maximum maneuvering angular speed of the unmanned aerial vehicle according to the maximum sideslip angle and the maximum attack angle; the maximum maneuvering acceleration and the maximum maneuvering angular speed are brought into a preset dynamics equation corresponding to the unmanned aerial vehicle, and the maximum displacement of the unmanned aerial vehicle in a preset risk assessment time is calculated; and taking the area with the current position of the unmanned aerial vehicle as the center and the maximum displacement as the radius as a first influence area for determining to influence the flight track.
By adopting the technical scheme, the maneuvering parameters of the unmanned aerial vehicle such as the maximum sideslip angle, the maximum attack angle, the maximum overload and the like are fully considered, the maximum maneuvering acceleration and the maximum maneuvering angular velocity of the unmanned aerial vehicle are calculated according to the inherent flight parameters, the maneuvering limit of the unmanned aerial vehicle in the current state is determined by the two parameters, the maximum displacement which can be achieved from the current position is calculated, and then the first influence area affecting the flight track is determined, wherein the first influence area is the space area which can be reached by the inherent maneuvering ability of the unmanned aerial vehicle, and the space area which needs to be concerned in advance is limited, so that collision targets can be detected and avoided in time.
Optionally, the determining the target collision object in the first influence area and acquiring the motion information of the target collision object include: obtaining a detection result of network detection equipment in a first preset time period in the first influence area, wherein the network detection equipment at least comprises one or more of a radar detector, an infrared detector and a laser detector; determining whether an identifiable object exists in the detection result according to a target detection algorithm, and taking the identifiable object as a target collision object; and analyzing the detection result according to a motion detection algorithm to obtain the motion information of the target collision object.
By adopting the technical scheme, various hidden or high-speed moving collision targets in the first influence area can be comprehensively found through the technical means of active detection and algorithm analysis of the network detection equipment, and the motion parameters of the collision targets are accurately acquired, so that the transition from passive collision detection to active target detection is realized, and the detection efficiency and accuracy of the collision targets can be greatly improved.
Optionally, the motion information includes acceleration, velocity and motion angle of the target collision object, and the determining, based on the motion information, a second influence area of the target collision object on the flight trajectory in the first influence area includes: predicting a motion track of the target collision object in the first influence area after a second preset time period according to the acceleration, the speed and the motion angle of the target collision object; extracting all boundary points of the motion trail, and expanding each boundary point outwards by a preset distance to obtain a second influence area.
By adopting the technical scheme, the motion trend of the target collision object is further predicted based on the motion information of the target collision object, the potential influence area of the target collision object on the flight track of the unmanned aerial vehicle is determined, the second influence area covers all possible future motion ranges of the target collision object, the purpose of determining the area is to evaluate the risk of collision of the unmanned aerial vehicle and the high maneuvering target, the unmanned aerial vehicle track is adjusted according to the second influence area, and all possible motion directions of the target can be avoided in advance, so that the collision is avoided.
Optionally, the calculating, based on the motion information and the second influence area, a risk score value of the target collision object on the unmanned aerial vehicle flight includes: determining a motion risk score of the target collision object on the unmanned aerial vehicle flight based on the acceleration, the speed and the motion angle of the target collision object; dividing the first influence area into a plurality of subareas, wherein each subarea corresponds to a risk coefficient; calculating the duty ratio of the second influence area in each subarea, taking the subarea with the largest duty ratio as a target subarea, and acquiring a target risk coefficient of the target subarea; and multiplying the athletic risk score by the target risk coefficient to obtain a risk score of the target collision object on the unmanned aerial vehicle.
By adopting the technical scheme, the collision threat of the target collision object is calculated based on the target acceleration, speed and movement angle parameters of the target collision object, meanwhile, the risk of the flight area of the unmanned aerial vehicle is considered, the target risk coefficient is obtained, and more comprehensive and accurate risk assessment can be realized by fusing the collision threat of the target and the risk of the unmanned aerial vehicle area.
Optionally, the determining the motion risk score of the target collision object on the unmanned aerial vehicle flight based on the acceleration, the speed and the motion angle includes: obtaining an acceleration influence value according to the acceleration and a preset acceleration scoring function; obtaining a speed influence value according to the speed and a preset speed scoring function; obtaining an angle influence value according to the movement angle and a preset angle scoring function, wherein the movement angle is an included angle between the target collision object and the movement track; and adding the acceleration influence value, the speed influence value and the angle influence value to obtain a motion risk score of the target collision object on the unmanned aerial vehicle.
By adopting the technical scheme, in order to construct the quantitative scoring model of the collision risk, the influence values of all factors of the risk need to be calculated based on the motion parameters of the target collision object, the acceleration influence value, the speed influence value and the angle influence value are calculated and obtained based on the acceleration, the speed and the motion angle parameters of the target collision object and a preset scoring function respectively, the three influence values are summed to obtain the comprehensive scoring of the motion risk of the target, and the collision risk of the target can be comprehensively reflected based on the scoring mode of multiple motion parameters of the target collision object.
Optionally, adjusting the flight trajectory based on the risk score value to obtain a target flight trajectory, including: judging whether the risk score value is larger than a first risk score threshold value or not; and if the risk score value is larger than the first risk score threshold value, triggering a track avoidance mode, and calculating a target flight track based on the flight parameter, the running information and a preset track avoidance algorithm, wherein the risk score value corresponding to the target flight track is smaller than a second risk score threshold value, and the first risk score threshold value is larger than the second risk score threshold value.
By adopting the technical scheme, when the risk score value is larger than the first risk score threshold value, the existence of higher collision risk is determined, the active track avoidance mode is triggered at the moment, and a plurality of candidate tracks are calculated by adopting an avoidance algorithm according to the current flight parameters and state of the unmanned aerial vehicle. And selecting a track with the corresponding risk score lower than a preset second threshold as a target track. The unmanned aerial vehicle is controlled to fly according to the target track, the active collision avoidance action is completed, the double-threshold judgment mode is adopted, unnecessary track adjustment on the low-risk condition can be prevented, meanwhile, the adjusted track risk can be ensured to be in a controllable range, and the collision risk in the flying process of the unmanned aerial vehicle is reduced to the greatest extent.
In a second aspect of the present application, there is provided a drone collision avoidance system, the system comprising:
the first influence area determining module is used for acquiring inherent flight parameters and flight tracks of the unmanned aerial vehicle and determining a first influence area affecting the flight tracks according to the inherent flight parameters;
the collision object motion information acquisition module is used for determining a target collision object in the first influence area and acquiring motion information of the target collision object;
a second influence region determining module, configured to determine a second influence region of the target collision object on the flight trajectory in the first influence region based on the motion information;
the risk score calculating module is used for calculating a risk score of the target collision object on the unmanned aerial vehicle flight based on the motion information and the second influence area;
and the flight track adjusting module is used for adjusting the flight track based on the risk score value to obtain a target flight track and controlling the unmanned aerial vehicle to fly according to the target flight track.
In a third aspect the present application provides a computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the above-described method steps.
In a fourth aspect of the present application, there is provided an electronic device comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the above-mentioned method steps.
In summary, one or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
1. according to the method, the first influence area of the unmanned aerial vehicle is determined through the inherent flight parameters of the unmanned aerial vehicle, the maneuvering range of the unmanned aerial vehicle in the current state is reasonably predicted, the flight characteristics of the unmanned aerial vehicle are considered in collision detection, the second influence area of the target collision object on the flight track is determined in the first influence area, the potential influence area of the target collision object on the flight track of the unmanned aerial vehicle can be determined, the risk scoring value of the target collision object on the flight of the unmanned aerial vehicle is calculated based on the movement information of the target collision object and the second influence area, more comprehensive and accurate risk assessment can be achieved by fusing the collision threat of the target object and the risk of the unmanned aerial vehicle area, the flight track of the unmanned aerial vehicle is actively planned according to the collision risk scoring, the collision risk can be avoided in advance in a targeted manner, and the collision risk in the flight process of the unmanned aerial vehicle is reduced;
2. According to the method, through the technical means of active detection and algorithm analysis of the network detection equipment, various hidden or high-speed moving collision targets in the first influence area can be comprehensively found, the motion parameters of the collision targets are accurately acquired, the transition from passive collision detection to active target detection is realized, and the detection efficiency and the accuracy of the collision targets can be greatly improved;
3. according to the method, the movement trend of the target collision object is further predicted based on the movement information of the target collision object, the potential influence area of the target collision object on the flight track of the unmanned aerial vehicle is determined, the second influence area covers all possible future movement ranges of the target collision object, the purpose of determining the area is to evaluate the risk of collision between the unmanned aerial vehicle and the high maneuvering target, the unmanned aerial vehicle track is adjusted according to the second influence area, and all possible movement directions of the target can be avoided in advance so that collision is avoided.
Drawings
Fig. 1 is a schematic flow chart of an anti-collision detection method for an unmanned aerial vehicle according to an embodiment of the present application;
fig. 2 is a schematic diagram of a module of an unmanned aerial vehicle collision avoidance system according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Reference numerals illustrate: 300. an electronic device; 301. a processor; 302. a communication bus; 303. a user interface; 304. a network interface; 305. a memory.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present application, but not all embodiments.
In the description of embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described herein as "such as" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "or" for example "is intended to present related concepts in a concrete fashion.
In the description of the embodiments of the present application, the term "plurality" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating an indicated technical feature. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless expressly specified otherwise.
Referring to fig. 1, a schematic flow chart of an unmanned aerial vehicle collision avoidance method is provided, the method may be implemented by a computer program, may be implemented by a single chip microcomputer, may also be run on an unmanned aerial vehicle collision avoidance system, the computer program may be integrated in an unmanned aerial vehicle control center, and may also be run as an independent tool application, and specifically the method includes steps 10 to 50, where the steps are as follows:
step 10: and acquiring the inherent flight parameters and the flight trajectory of the unmanned aerial vehicle, and determining a first influence area affecting the flight trajectory according to the inherent flight parameters.
The application scenario of the embodiment of the application scenario may be in the unmanned aerial vehicle application test process, or may be in the actual use process of various unmanned aerial vehicles, and is not limited herein.
Specifically, in order to implement collision risk assessment and trajectory adjustment of the unmanned aerial vehicle, a possible influence area of the unmanned aerial vehicle needs to be determined first. The first area of influence refers to a spatial region that the drone may fly to in the current state within a certain period of time (e.g., 0.1 seconds) based on its inherent maneuverability. The first area of influence is calculated because in this area the drone may collide with other objects, so that the risk of collision in this area needs to be detected and assessed in advance. And acquiring inherent flight parameters of the unmanned aerial vehicle, wherein the inherent flight parameters comprise parameters such as a maximum sideslip angle, a maximum attack angle, a maximum overload and the like, and calculating the maximum maneuvering acceleration and the maximum maneuvering angular velocity of the unmanned aerial vehicle according to the inherent flight parameters. Further, the maximum maneuvering parameters are substituted into a dynamics equation of the unmanned aerial vehicle, so that the maximum displacement in the preset risk assessment time can be calculated, and a first influence area affecting the flight track is determined according to the maximum displacement.
On the basis of the above embodiment, as an alternative embodiment, the step of determining the first influence area affecting the flight trajectory according to the intrinsic flight parameter may further include the steps of:
step 101: and calculating the maximum maneuvering acceleration of the unmanned aerial vehicle according to the maximum overload, and calculating the maximum maneuvering angular speed of the unmanned aerial vehicle according to the maximum sideslip angle and the maximum attack angle.
Specifically, in order to calculate the first influence area of the unmanned aerial vehicle, a maximum maneuvering capability of the unmanned aerial vehicle needs to be determined, including a maximum maneuvering acceleration and a maximum maneuvering angular velocity. In the embodiment of the application, the maximum overload reflects the maximum acceleration that the unmanned aerial vehicle can bear in the flight process, and the maximum acceleration is usually in units of G values and comprises longitudinal overload (facing the aerodynamic direction) and transverse overload; the relation between the maximum overload n-trans and the maximum maneuver acceleration a_max of the drone is such that a_max=n×g, where g is the gravitational acceleration.
The maximum sideslip angle is the maximum transverse sliding angle which can be achieved by the unmanned aerial vehicle in the flight process, and reflects the transverse movement capability of the unmanned aerial vehicle; the maximum attack angle refers to the maximum attack angle or sideslip angle which can be achieved by the unmanned aerial vehicle relative to the flight direction, and reflects the longitudinal maneuvering capability of the unmanned aerial vehicle. The maximum angular velocity of the unmanned aerial vehicle is limited by the maximum sideslip angle and the maximum attack angle, and the maximum sideslip angle beta_max limits the transverse acceleration a of the unmanned aerial vehicle in the transverse rolling plane y _max，a y Max=g×tan β_max, the maximum angle of attack α_max limiting the longitudinal acceleration a of the unmanned aerial vehicle in the pitch plane x _max:，a x Max=g×tan α_max, and the acceleration in two directions is synthesized, so that the synthesized maximum lateral acceleration a of the unmanned aerial vehicle can be obtained max . Wherein, unmanned aerial vehicle's biggest maneuver angular velocity is:
ω_max=vmax/r_min, where ω_max is the maximum maneuver angular velocity, vmax is the maximum velocity of the drone, r_min is the minimum turning radius of the drone, where r_min=vmax 2/a max . Which is a kind ofIn which the overload limit determines the maximum acceleration and turning radius of the unmanned aerial vehicle. The attack angle and sideslip angle limit the maximum angle of rotation of the control surface of the unmanned aerial vehicle.
Step 102: and carrying the maximum maneuvering acceleration and the maximum maneuvering angular speed into a corresponding preset dynamics equation of the unmanned aerial vehicle, and calculating the maximum displacement of the unmanned aerial vehicle within a preset risk assessment time.
Step 103: and taking the area with the current position of the unmanned aerial vehicle as the center and the maximum displacement as the radius as a first influence area for determining the influence on the flight track.
Specifically, the calculated maximum maneuvering acceleration and maximum maneuvering angular velocity of the unmanned aerial vehicle are used as known conditions and substituted into a preset three-degree-of-freedom dynamics equation of the unmanned aerial vehicle. The three-degree-of-freedom dynamics equation fully considers the kinematics and dynamics characteristics of the unmanned aerial vehicle, and can accurately describe the motion state of the unmanned aerial vehicle. Substituting the maximum maneuver acceleration and the angular velocity into the motion trajectory and the maximum displacement of the unmanned aerial vehicle under the condition of performing the maximum maneuver from the current state can be simulated through iterative calculation. The risk assessment time is a time period preset according to the flight characteristics of the unmanned aerial vehicle. And drawing a circular area by taking the current real-time position of the unmanned aerial vehicle as a circle center and taking the maximum displacement as a radius. This satisfies the isotropic character of the maximum maneuver of the drone from the current position, while the circular area also covers all possible maximum maneuver trajectories of the drone. The round maximum displacement area taking the real-time position of the unmanned aerial vehicle as the center can accurately reflect the possible flight range of the unmanned aerial vehicle in the evaluation time, and is determined to be the first influence area of the unmanned aerial vehicle, so that the space area which the unmanned aerial vehicle needs to pay attention to in advance is limited, and the collision target can be detected and avoided in time.
Step 20: and determining the target collision object in the first influence area, and acquiring the motion information of the target collision object.
Specifically, after the first influence area of the unmanned aerial vehicle is determined, whether a collision risk target exists in the area needs to be identified, and movement information of the target collision object is acquired, so that a basis is provided for subsequent risk assessment. The first influence area can be scanned through the network detection equipment, detection information in a period of time in the area can be obtained, so that a target collision object in the first influence area can be determined, the detection information is analyzed, and movement information of the target collision object can be obtained, wherein the movement information can comprise the movement speed, acceleration, movement angle and the like of the target collision object.
On the basis of the above embodiment, as an alternative embodiment, the step of determining the target collision object in the first influence region and acquiring the movement information of the target collision object may further include the steps of:
step 201: and acquiring a detection result of the network detection equipment in a first preset time period in the first influence area.
Specifically, the first influence area is scanned by the network detection device, and the network detection device can integrate multiple detection devices, such as a radar detector, an infrared detector, a laser detector and the like, so as to construct a detection network device, so as to realize omnibearing detection of the first influence area, acquire a detection result of the network detection device in a first preset time period in the first influence area, and the first preset time period can be a continuous shorter time, so as to obtain the motion information of the collision object.
Step 202: and determining whether the identifiable object exists in the detection result according to the target detection algorithm, and taking the identifiable object as a target collision object.
Specifically, data of a detection result obtained by the network detection equipment is input into a target detection algorithm, whether identifiable moving object targets exist in the data is judged through processes of feature extraction, matching, classification and the like, and the algorithm can effectively identify different types of collidable targets. The object detection algorithm refers to an algorithm technique that discovers and locates object instances in images or other sensor data. All the identified target examples are extracted from the detection result, the position and the motion information of the identified target examples are recorded, and the extracted identifiable moving objects are determined to be target collided objects in the first influence area.
Step 203: and analyzing the detection result according to a motion detection algorithm to obtain the motion information of the target collision object.
Specifically, to evaluate the collision risk of the target with the unmanned aerial vehicle, it is not enough to determine only the existence of the target collision object, and it is necessary to further analyze the motion state of the target collision object. Therefore, after the target collision object is identified by the target detection algorithm, motion information of the target collision object needs to be resolved based on continuous detection data. And selecting a motion detection algorithm suitable for radar and infrared multi-mode detection results, such as a motion estimation method based on an optical flow method. The algorithm can analyze a series of detection images, judge the movement and position change of each pixel point between the sequence images, further detect a moving target and output movement information thereof, wherein the movement information comprises acceleration, speed and movement angle of the target collision object.
Step 30: a second region of influence of the target collision object on the flight trajectory is determined in the first region of influence based on the motion information.
Specifically, the motion information includes acceleration, velocity, and motion angle of the target collision object. After the motion information of the target collision object is obtained, the motion trend of the target collision object needs to be further predicted based on the motion information of the target collision object, and the potential influence area of the target collision object on the flight track of the unmanned aerial vehicle is determined. Information such as the speed, acceleration, motion angle and the like of the target collision object is input into a motion prediction model, and the model can be a Bayesian sequence estimation model based on particle filtering. Setting a time period as a second preset time, simulating possible motion states and tracks of the target in the time period according to the second preset time by the motion prediction model, wherein the second preset time is required to cover the time range where the target possibly meets the unmanned aerial vehicle, extracting boundary points of the track path from a plurality of simulated possible motion tracks, drawing the boundary of the motion range of the target by the boundary points, expanding the boundary points outwards according to a certain distance to form an area covering all possible track ranges, and taking the area as a second influence area, wherein the expansion distance is determined according to the sizes of the target and the unmanned aerial vehicle. The second area of influence covers all possible future ranges of motion of the target collision object. The purpose of determining this area is to assess the risk of collision of the unmanned aerial vehicle with a highly maneuver target, and to adjust the unmanned aerial vehicle trajectory according to the second impact area, all possible directions of movement of the target can be avoided in advance so as to avoid the collision.
Step 40: and calculating the risk scoring value of the target collision object on the unmanned aerial vehicle flight based on the motion information and the second influence area.
Specifically, in order to evaluate the collision risk degree of the target collision object on the unmanned aerial vehicle, after determining the second influence area of the target, a collision risk score needs to be calculated based on the motion information of the target collision object and the unmanned aerial vehicle. A collision risk scoring model may be established. The input variables in the model include the speed, acceleration, collision avoidance capability of the target collision object, and the intersection of the drone with the second region of influence. The output of the model is a score of a preset range, and the larger the score value is, the higher the collision risk is.
On the basis of the above embodiment, as an optional embodiment, the step of calculating the risk score value of the target collision object for the unmanned aerial vehicle flying based on the movement information and the second influence area may further include the steps of:
step 401: and determining a motion risk score of the target collision object on the unmanned plane based on the acceleration, the speed and the motion angle of the target collision object.
Specifically, in order to construct a quantitative scoring model of collision risk, the impact value of each factor of risk needs to be calculated based on the motion parameters of the target. Substituting the acceleration value of the target into a preset acceleration scoring function, setting risk influence values corresponding to different acceleration values by the acceleration scoring function, and calculating to obtain the acceleration influence value. And substituting the target speed into a preset speed scoring function to obtain a speed influence value corresponding to the speed, wherein the speed scoring function sets risk influence values corresponding to different speeds. And extracting the movement angle of the target collision object, namely the included angle between the target collision object and the unmanned aerial vehicle track, substituting a preset angle scoring function to obtain an angle influence value corresponding to the angle, wherein the risk influence value corresponding to different angle values is set by the angle scoring function.
Illustratively, the acceleration scoring function is f (a) =a/10, substituting a=3 m/s 2 The acceleration influence value is f (a) =3/10=0.3, the velocity scoring function is f (v) =v/20, v=15 m/s is substituted, the velocity influence value is f (v) =15/20=0.75, the angle scoring function is f (θ) =θ/90, θ=30°, the angle influence value is f (θ) =30/90=0.33, and the three influence values are summed to obtain the target collision risk score of 0.3+0.75+0.33=1.38.
Step 402: the first influence area is divided into a plurality of subareas, and each subarea corresponds to one risk coefficient.
Step 403: and calculating the duty ratio of the second influence area in each sub-area, taking the sub-area with the largest duty ratio as a target sub-area, and acquiring the target risk coefficient of the target sub-area.
Step 404: and multiplying the motion risk score by a target risk coefficient to obtain a risk score value of the target collision object on the unmanned aerial vehicle.
Specifically, the collision risk is not fully estimated based on the motion parameters of the target collision object, and the risk of the unmanned aerial vehicle in the self-flying area needs to be considered. Dividing the first influence area of the unmanned aerial vehicle into a plurality of subareas according to the distance between the first influence area and the unmanned aerial vehicle, setting a risk coefficient for each subarea, wherein the risk coefficient is set higher when the area is close to the unmanned aerial vehicle. And calculating the coverage ratio of the second influence area in each subarea, determining the subarea with the largest coverage ratio as a target subarea, and extracting a risk coefficient corresponding to the subarea as a target risk coefficient. And multiplying the motion risk score by a target risk coefficient to obtain a risk score value of the target collision object on the unmanned aerial vehicle. By fusing the collision threat of the target and the danger of the unmanned aerial vehicle area, more comprehensive and accurate risk assessment can be realized.
Step 50: and adjusting the flight trajectory based on the risk score value to obtain a target flight trajectory, and controlling the unmanned aerial vehicle to fly according to the target flight trajectory.
Specifically, after the risk of collision of the target is evaluated, the flight track of the unmanned aerial vehicle needs to be correspondingly adjusted according to the size of the risk, so that high-risk collision is avoided. Setting different track adjustment amplitudes according to the size of the risk score, wherein the larger the risk is, the larger the track adjustment amplitude is, presetting a first risk score threshold value, comparing with the calculated risk score, and if the risk score value is larger than the first risk score threshold value, determining that higher collision risk exists, and needing track avoidance. And starting a track avoidance mode, and calculating a plurality of candidate tracks by adopting an avoidance algorithm according to the current flight parameters and the current flight state of the unmanned aerial vehicle. And selecting a track with the corresponding risk score lower than a preset second threshold as a target track. And controlling the unmanned aerial vehicle to fly according to the target track, and completing the active collision avoidance action. The second threshold is lower than the first threshold so as to ensure that the risk after adjustment is in an acceptable range, unnecessary track adjustment on the low-risk condition can be prevented by adopting a double-threshold judging mode, and meanwhile, the adjusted track risk can be ensured to be in a controllable range so as to reduce the collision risk in the flight process of the unmanned aerial vehicle to the greatest extent.
Please refer to fig. 2, which is a schematic diagram of an unmanned aerial vehicle anti-collision detection system module provided in an embodiment of the present application, the unmanned aerial vehicle anti-collision detection system may include: the system comprises a first influence area determining module, a collision object motion information acquiring module, a second influence area determining module, a risk score calculating module and a flight track adjusting module, wherein:
the first influence area determining module is used for acquiring inherent flight parameters and flight tracks of the unmanned aerial vehicle and determining a first influence area affecting the flight tracks according to the inherent flight parameters;
the collision object motion information acquisition module is used for determining a target collision object in the first influence area and acquiring motion information of the target collision object;
a second influence region determining module, configured to determine a second influence region of the target collision object on the flight trajectory in the first influence region based on the motion information;
the risk score calculating module is used for calculating a risk score of the target collision object on the unmanned aerial vehicle flight based on the motion information and the second influence area;
and the flight track adjusting module is used for adjusting the flight track based on the risk score value to obtain a target flight track and controlling the unmanned aerial vehicle to fly according to the target flight track.
Optionally, the first influence area determining module may be further configured to calculate a maximum maneuver acceleration of the unmanned aerial vehicle according to the maximum overload, and calculate a maximum maneuver angular velocity of the unmanned aerial vehicle according to the maximum sideslip angle and the maximum attack angle; the maximum maneuvering acceleration and the maximum maneuvering angular speed are brought into a preset dynamics equation corresponding to the unmanned aerial vehicle, and the maximum displacement of the unmanned aerial vehicle in a preset risk assessment time is calculated; and taking the area with the current position of the unmanned aerial vehicle as the center and the maximum displacement as the radius as a first influence area for determining to influence the flight track.
Optionally, the collision object motion information obtaining module is further configured to obtain a detection result of a network detection device in the first preset time period in the first influence area, where the network detection device is at least composed of one or more of a radar detector, an infrared detector and a laser detector; determining whether an identifiable object exists in the detection result according to a target detection algorithm, and taking the identifiable object as a target collision object; and analyzing the detection result according to a motion detection algorithm to obtain the motion information of the target collision object.
Optionally, the second influence area determining module is further configured to predict a motion track of the target collision object in the first influence area after a second preset time period according to an acceleration, a speed and a motion angle of the target collision object; extracting all boundary points of the motion trail, and expanding each boundary point outwards by a preset distance to obtain a second influence area.
Optionally, the risk score value calculation module is further configured to determine a motion risk score of the target collision object on the unmanned aerial vehicle based on the acceleration, the speed and the motion angle of the target collision object; dividing the first influence area into a plurality of subareas, wherein each subarea corresponds to a risk coefficient; calculating the duty ratio of the second influence area in each subarea, taking the subarea with the largest duty ratio as a target subarea, and acquiring a target risk coefficient of the target subarea; and multiplying the athletic risk score by the target risk coefficient to obtain a risk score of the target collision object on the unmanned aerial vehicle.
Optionally, the risk score calculating module is further configured to obtain an acceleration influence value according to the acceleration and a preset acceleration scoring function; obtaining a speed influence value according to the speed and a preset speed scoring function; obtaining an angle influence value according to the movement angle and a preset angle scoring function, wherein the movement angle is an included angle between the target collision object and the movement track; and adding the acceleration influence value, the speed influence value and the angle influence value to obtain a motion risk score of the target collision object on the unmanned aerial vehicle.
Optionally, the flight trajectory adjustment module is further configured to determine whether the risk score is greater than a first risk score threshold; and if the risk score value is larger than the first risk score threshold value, triggering a track avoidance mode, and calculating a target flight track based on the flight parameter, the running information and a preset track avoidance algorithm, wherein the risk score value corresponding to the target flight track is smaller than a second risk score threshold value, and the first risk score threshold value is larger than the second risk score threshold value.
It should be noted that: in the system provided in the above embodiment, when implementing the functions thereof, only the division of the above functional modules is used as an example, in practical application, the above functional allocation may be implemented by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to implement all or part of the functions described above. In addition, the system and method embodiments provided in the foregoing embodiments belong to the same concept, and specific implementation processes of the system and method embodiments are detailed in the method embodiments, which are not repeated herein.
The embodiment of the application further provides a computer storage medium, where the computer storage medium may store a plurality of instructions, where the instructions are suitable for being loaded by a processor and executed by the processor, and a specific execution process may refer to a specific description of the foregoing embodiment, and will not be described herein.
Please refer to fig. 3, the present application also discloses an electronic device. Fig. 3 is a schematic structural diagram of an electronic device according to the disclosure in an embodiment of the present application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, at least one communication bus 302.
Wherein the communication bus 302 is used to enable connected communication between these components.
The user interface 303 may include a Display screen (Display), a Camera (Camera), and the optional user interface 303 may further include a standard wired interface, and a wireless interface.
The network interface 304 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
Wherein the processor 301 may include one or more processing cores. The processor 301 utilizes various interfaces and lines to connect various portions of the overall server, perform various functions of the server and process data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and invoking data stored in the memory 305. Alternatively, the processor 301 may be implemented in hardware in at least one of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 301 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 301 and may be implemented by a single chip.
The Memory 305 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). Memory 305 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 305 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-described respective method embodiments, etc.; the storage data area may store data or the like involved in the above respective method embodiments. Memory 305 may also optionally be at least one storage device located remotely from the aforementioned processor 301. Referring to fig. 3, an operating system, a network communication module, a user interface module, and an application program of a collision avoidance method of the unmanned aerial vehicle may be included in the memory 305 as a computer storage medium.
In the electronic device 300 shown in fig. 3, the user interface 303 is mainly used for providing an input interface for a user, and acquiring data input by the user; and the processor 301 may be configured to invoke an application of a drone collision avoidance method stored in the memory 305, which when executed by the one or more processors 301, causes the electronic device 300 to perform the method as described in one or more of the embodiments above. It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required in the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided herein, it should be understood that the disclosed apparatus may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, such as a division of units, merely a division of logic functions, and there may be additional divisions in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some service interface, device or unit indirect coupling or communication connection, electrical or otherwise.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, including several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned memory includes: various media capable of storing program codes, such as a U disk, a mobile hard disk, a magnetic disk or an optical disk.
The foregoing is merely exemplary embodiments of the present disclosure and is not intended to limit the scope of the present disclosure. That is, equivalent changes and modifications are contemplated by the teachings of this disclosure, which fall within the scope of the present disclosure. Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure.
This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a scope and spirit of the disclosure being indicated by the claims.
Claims (6)
1. An unmanned aerial vehicle collision avoidance detection method, the method comprising:
acquiring inherent flight parameters and flight tracks of an unmanned aerial vehicle, wherein the inherent flight parameters comprise a maximum sideslip angle, a maximum attack angle and a maximum overload, calculating the maximum maneuvering acceleration of the unmanned aerial vehicle according to the maximum overload, and calculating the maximum maneuvering angular velocity of the unmanned aerial vehicle according to the maximum sideslip angle and the maximum attack angle;
the maximum maneuvering acceleration and the maximum maneuvering angular speed are brought into a preset dynamics equation corresponding to the unmanned aerial vehicle, and the maximum displacement of the unmanned aerial vehicle in a preset risk assessment time is calculated;
taking a region with the current position of the unmanned aerial vehicle as a center and the maximum displacement as a radius as a first influence region for determining the influence on the flight track;
Determining a target collision object in the first influence area, and acquiring motion information of the target collision object; the motion information comprises acceleration, speed and motion angle of the target collision object;
predicting a motion track of the target collision object in the first influence area after a second preset time period according to the acceleration, the speed and the motion angle of the target collision object;
extracting all boundary points of the motion trail, and expanding each boundary point outwards by a preset distance to obtain a second influence area;
obtaining an acceleration influence value according to the acceleration and a preset acceleration scoring function;
obtaining a speed influence value according to the speed and a preset speed scoring function;
obtaining an angle influence value according to the movement angle and a preset angle scoring function, wherein the movement angle is an included angle between the target collision object and the movement track;
adding the acceleration influence value, the speed influence value and the angle influence value to obtain a motion risk score of the target collision object on the unmanned aerial vehicle;
dividing the first influence area into a plurality of subareas, wherein each subarea corresponds to a risk coefficient, and the risk coefficient is set higher as the subarea is closer to the unmanned aerial vehicle;
Calculating the duty ratio of the second influence area in each subarea, taking the subarea with the largest duty ratio as a target subarea, and acquiring a target risk coefficient of the target subarea;
multiplying the motion risk score by the target risk coefficient to obtain a risk score of the target collision object on the unmanned aerial vehicle;
and adjusting the flight track based on the risk score value to obtain a target flight track, and controlling the unmanned aerial vehicle to fly according to the target flight track.
2. The unmanned aerial vehicle collision avoidance detection method of claim 1, wherein the determining a target collision object in the first region of influence and obtaining motion information of the target collision object comprises:
obtaining a detection result of network detection equipment in a first preset time period in the first influence area, wherein the network detection equipment at least comprises one or more of a radar detector, an infrared detector and a laser detector;
determining whether an identifiable object exists in the detection result according to a target detection algorithm, and taking the identifiable object as a target collision object;
and analyzing the detection result according to a motion detection algorithm to obtain the motion information of the target collision object.
3. The unmanned aerial vehicle collision avoidance detection method of claim 1, wherein the adjusting the flight trajectory based on the risk score value results in a target flight trajectory comprising:
judging whether the risk score value is larger than a first risk score threshold value or not;
and if the risk score value is larger than the first risk score threshold value, triggering a track avoidance mode, and calculating a target flight track based on the flight parameter, the running information and a preset track avoidance algorithm, wherein the risk score value corresponding to the target flight track is smaller than a second risk score threshold value, and the first risk score threshold value is larger than the second risk score threshold value.
4. An unmanned aerial vehicle collision avoidance system, the system comprising:
the first influence area determining module is used for acquiring inherent flight parameters and flight tracks of the unmanned aerial vehicle, wherein the inherent flight parameters comprise a maximum sideslip angle, a maximum attack angle and a maximum overload, the maximum maneuvering acceleration of the unmanned aerial vehicle is calculated according to the maximum overload, and the maximum maneuvering angular speed of the unmanned aerial vehicle is calculated according to the maximum sideslip angle and the maximum attack angle; the maximum maneuvering acceleration and the maximum maneuvering angular speed are brought into a preset dynamics equation corresponding to the unmanned aerial vehicle, and the maximum displacement of the unmanned aerial vehicle in a preset risk assessment time is calculated; taking a region with the current position of the unmanned aerial vehicle as a center and the maximum displacement as a radius as a first influence region for determining the influence on the flight track;
The collision object motion information acquisition module is used for determining a target collision object in the first influence area and acquiring motion information of the target collision object; the motion information comprises acceleration, speed and motion angle of the target collision object;
the second influence area determining module is used for predicting the movement track of the target collision object in the first influence area after a second preset time period according to the acceleration, the speed and the movement angle of the target collision object; extracting all boundary points of the motion trail, and expanding each boundary point outwards by a preset distance to obtain a second influence area;
the risk scoring value calculation module is used for obtaining an acceleration influence value according to the acceleration and a preset acceleration scoring function;
obtaining a speed influence value according to the speed and a preset speed scoring function; obtaining an angle influence value according to the movement angle and a preset angle scoring function, wherein the movement angle is an included angle between the target collision object and the movement track; adding the acceleration influence value, the speed influence value and the angle influence value to obtain a motion risk score of the target collision object on the unmanned aerial vehicle; dividing the first influence area into a plurality of subareas, wherein each subarea corresponds to a risk coefficient, and the risk coefficient is set higher as the subarea is closer to the unmanned aerial vehicle; calculating the duty ratio of the second influence area in each subarea, taking the subarea with the largest duty ratio as a target subarea, and acquiring a target risk coefficient of the target subarea; multiplying the motion risk score by the target risk coefficient to obtain a risk score of the target collision object on the unmanned aerial vehicle;
And the flight track adjusting module is used for adjusting the flight track based on the risk score value to obtain a target flight track and controlling the unmanned aerial vehicle to fly according to the target flight track.
5. A computer readable storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the method of any one of claims 1-3.
6. An electronic device comprising a processor, a memory, a user interface, and a network interface, the memory for storing instructions, the processor for executing the instructions stored in the memory to cause the electronic device to perform the method of any one of claims 1-3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311742611.9A CN117434967B (en) | 2023-12-18 | 2023-12-18 | Unmanned aerial vehicle anti-collision detection method, system, medium and equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311742611.9A CN117434967B (en) | 2023-12-18 | 2023-12-18 | Unmanned aerial vehicle anti-collision detection method, system, medium and equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117434967A CN117434967A (en) | 2024-01-23 |
| CN117434967B true CN117434967B (en) | 2024-03-15 |
Family
ID=89556867
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311742611.9A Active CN117434967B (en) | 2023-12-18 | 2023-12-18 | Unmanned aerial vehicle anti-collision detection method, system, medium and equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117434967B (en) |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107368095A (en) * | 2017-08-29 | 2017-11-21 | 中国人民解放军国防科技大学 | Air anti-collision system and method for small-sized fixed-wing unmanned aerial vehicle |
| KR20200081322A (en) * | 2018-12-27 | 2020-07-07 | (주)자이언트드론 | The device for managing obstacle collision of drone |
| CN111859247A (en) * | 2020-07-20 | 2020-10-30 | 西华大学 | Unmanned aerial vehicle operation risk assessment method based on satellite-based ADS-B data |
| CN112033411A (en) * | 2020-09-07 | 2020-12-04 | 中国民航大学 | Unmanned aerial vehicle route planning method based on safety cost evaluation |
| CN112198901A (en) * | 2020-05-14 | 2021-01-08 | 北京航空航天大学 | Unmanned aerial vehicle autonomous collision avoidance decision method based on three-dimensional dynamic collision area |
| CN112466161A (en) * | 2020-11-27 | 2021-03-09 | 北航（四川）西部国际创新港科技有限公司 | Low-altitude aircraft collision avoidance capability evaluation method based on various environmental factors |
| CN112885155A (en) * | 2021-01-18 | 2021-06-01 | 中国人民解放军空军工程大学 | Unmanned aerial vehicle flight collision risk assessment method in fusion airspace |
| CN113670309A (en) * | 2021-07-21 | 2021-11-19 | 南京航空航天大学 | Urban low-altitude unmanned aerial vehicle path planning method considering safety risk and noise influence |
| CN114153232A (en) * | 2021-12-27 | 2022-03-08 | 歌尔光学科技有限公司 | Obstacle avoidance method, unmanned aerial vehicle and computer readable storage medium |
| CN114371727A (en) * | 2021-12-06 | 2022-04-19 | 特金智能科技(上海)有限公司 | Control method, controller, equipment and medium for safe flight of unmanned aerial vehicle |
| CN115755973A (en) * | 2022-11-21 | 2023-03-07 | 上海特金信息科技有限公司 | Unmanned aerial vehicle low airspace traffic decision-making method, device, system and equipment |
| CN115933754A (en) * | 2021-10-26 | 2023-04-07 | 中国电力科学研究院有限公司 | Electric power inspection unmanned aerial vehicle obstacle avoidance method based on millimeter wave radar and binocular vision |
| CN116249643A (en) * | 2020-10-01 | 2023-06-09 | 埃尔构人工智能有限责任公司 | Method and system for predicting actions of an object by an autonomous vehicle to determine a viable path through a conflict area |
| CN117034456A (en) * | 2023-08-09 | 2023-11-10 | 四川省星时代智能卫星科技有限公司 | Rocket flight trajectory evaluation method and device, storage medium and electronic equipment |
| CN117075621A (en) * | 2023-07-27 | 2023-11-17 | 中国人民解放军69224部队 | Unmanned aerial vehicle safety avoidance method and device, electronic equipment and storage medium |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10657833B2 (en) * | 2017-11-30 | 2020-05-19 | Intel Corporation | Vision-based cooperative collision avoidance |
| CN110658829B (en) * | 2019-10-30 | 2021-03-30 | 武汉理工大学 | Intelligent collision avoidance method for unmanned surface vehicle based on deep reinforcement learning |
| WO2021207321A1 (en) * | 2020-04-07 | 2021-10-14 | Acta, Llc | Flight risk analysis system |
-
2023
- 2023-12-18 CN CN202311742611.9A patent/CN117434967B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107368095A (en) * | 2017-08-29 | 2017-11-21 | 中国人民解放军国防科技大学 | Air anti-collision system and method for small-sized fixed-wing unmanned aerial vehicle |
| KR20200081322A (en) * | 2018-12-27 | 2020-07-07 | (주)자이언트드론 | The device for managing obstacle collision of drone |
| CN112198901A (en) * | 2020-05-14 | 2021-01-08 | 北京航空航天大学 | Unmanned aerial vehicle autonomous collision avoidance decision method based on three-dimensional dynamic collision area |
| CN111859247A (en) * | 2020-07-20 | 2020-10-30 | 西华大学 | Unmanned aerial vehicle operation risk assessment method based on satellite-based ADS-B data |
| CN112033411A (en) * | 2020-09-07 | 2020-12-04 | 中国民航大学 | Unmanned aerial vehicle route planning method based on safety cost evaluation |
| CN116249643A (en) * | 2020-10-01 | 2023-06-09 | 埃尔构人工智能有限责任公司 | Method and system for predicting actions of an object by an autonomous vehicle to determine a viable path through a conflict area |
| CN112466161A (en) * | 2020-11-27 | 2021-03-09 | 北航（四川）西部国际创新港科技有限公司 | Low-altitude aircraft collision avoidance capability evaluation method based on various environmental factors |
| CN112885155A (en) * | 2021-01-18 | 2021-06-01 | 中国人民解放军空军工程大学 | Unmanned aerial vehicle flight collision risk assessment method in fusion airspace |
| CN113670309A (en) * | 2021-07-21 | 2021-11-19 | 南京航空航天大学 | Urban low-altitude unmanned aerial vehicle path planning method considering safety risk and noise influence |
| CN115933754A (en) * | 2021-10-26 | 2023-04-07 | 中国电力科学研究院有限公司 | Electric power inspection unmanned aerial vehicle obstacle avoidance method based on millimeter wave radar and binocular vision |
| CN114371727A (en) * | 2021-12-06 | 2022-04-19 | 特金智能科技(上海)有限公司 | Control method, controller, equipment and medium for safe flight of unmanned aerial vehicle |
| CN114153232A (en) * | 2021-12-27 | 2022-03-08 | 歌尔光学科技有限公司 | Obstacle avoidance method, unmanned aerial vehicle and computer readable storage medium |
| CN115755973A (en) * | 2022-11-21 | 2023-03-07 | 上海特金信息科技有限公司 | Unmanned aerial vehicle low airspace traffic decision-making method, device, system and equipment |
| CN117075621A (en) * | 2023-07-27 | 2023-11-17 | 中国人民解放军69224部队 | Unmanned aerial vehicle safety avoidance method and device, electronic equipment and storage medium |
| CN117034456A (en) * | 2023-08-09 | 2023-11-10 | 四川省星时代智能卫星科技有限公司 | Rocket flight trajectory evaluation method and device, storage medium and electronic equipment |
Non-Patent Citations (2)
| Title |
|---|
| 基于分散式RHC算法的无人机编队碰撞避免研究;王锐;宋科璞;车军;;航空计算技术;20080515;第38卷(第03期);第56-60页，第64页 * |
| 王锐 ; 宋科璞 ; 车军 ; .基于分散式RHC算法的无人机编队碰撞避免研究.航空计算技术.2008,第38卷(第03期),第56-60页，第64页. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117434967A (en) | 2024-01-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109116867B (en) | Unmanned aerial vehicle flight obstacle avoidance method and device, electronic equipment and storage medium | |
| US10710579B2 (en) | Collision prediction system | |
| JP7465615B2 (en) | Smart aircraft landing | |
| KR102032070B1 (en) | System and Method for Depth Map Sampling | |
| US20200331476A1 (en) | Automatic lane change with minimum gap distance | |
| CN109598066B (en) | Effect evaluation method, apparatus, device and storage medium for prediction module | |
| CN108226951B (en) | Laser sensor based real-time tracking method for fast moving obstacle | |
| KR20200067629A (en) | Method and device to process radar data | |
| US20200307589A1 (en) | Automatic lane merge with tunable merge behaviors | |
| US20100066587A1 (en) | Method and System for Controlling a Remote Vehicle | |
| US11518381B2 (en) | Enhanced threat selection | |
| US11507092B2 (en) | Sequential clustering | |
| JP7051366B2 (en) | Information processing equipment, trained models, information processing methods, and programs | |
| CN108279671B (en) | Terahertz-based environment sensing method and device and computer readable storage medium | |
| Vrba et al. | Autonomous capture of agile flying objects using UAVs: The MBZIRC 2020 challenge | |
| CN115509231A (en) | Robot following obstacle avoidance method and device and storage medium | |
| CN117434967B (en) | Unmanned aerial vehicle anti-collision detection method, system, medium and equipment | |
| EP4160269A1 (en) | Systems and methods for onboard analysis of sensor data for sensor fusion | |
| CN111813089B (en) | Simulation verification method, device and system for aircraft obstacle avoidance algorithm | |
| Nguyen et al. | Optimized grid-based environment perception in advanced driver assistance systems | |
| Navarro et al. | Sense and avoid using hybrid convolutional and recurrent neural networks | |
| Lou et al. | A First Physical-World Trajectory Prediction Attack via LiDAR-induced Deceptions in Autonomous Driving | |
| CN114078326B (en) | Collision detection method, device, visual sensor and storage medium | |
| Parmar et al. | FOGMINATOR: LIDAR based device for collision avoidance in fog | |
| US20240221186A1 (en) | Processing for machine learning based object detection using sensor data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |