CN112462804A - Unmanned aerial vehicle perception and avoidance strategy based on ADS-B and ant colony algorithm - Google Patents
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Abstract
The invention discloses an unmanned aerial vehicle sensing and avoiding strategy based on ADS-B and an ant colony algorithm, and belongs to the technical field of unmanned aerial vehicle safety. The invention mainly provides a cooperative sensing and avoiding strategy for the collision avoidance problem of an unmanned aerial vehicle under an airspace fusion background, which mainly comprises the following steps: firstly, acquiring flight state data of a target aircraft based on an ADS-B technology; secondly, performing deterministic collision detection, and determining a collision target by fully utilizing ADS-B information to perform geometric distance judgment from a horizontal plane and a vertical plane; and finally, performing route re-planning by adopting an ant colony algorithm based on an introduced comprehensive factor heuristic function and a sequencing mechanism to realize collision avoidance. The method can effectively identify the possible threat targets around the unmanned aerial vehicle, and provides a high-quality release strategy with high adaptability.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle safety, relates to an unmanned aerial vehicle perception and avoidance strategy based on an ADS-B technology and an ant colony algorithm, and particularly relates to a conflict resolution method based on an improved ant colony algorithm.
Background
Unmanned aerial vehicle perception and avoidance means that unmanned aerial vehicle can independently discern the barrier, keep safe interval and avoid the collision, roughly contains three functional modules of environmental perception, conflict detection, conflict resolution.
The environment sensing mainly depends on sensors, including radar, infrared system, and Automatic Dependent Surveillance-Broadcast (ADS-B) technology. The ADS-B technology is a main direction of a new generation of monitoring technology in a global air transport System, and relies on a Global Navigation Satellite System (GNSS) and an advanced air-to-air and air-to-ground data link communication technology to generate and exchange a variety of information over a long distance. At present, relevant landing plans of ADS-B are gradually completed in the United states, Europe and China, and the ADS-B technology can be widely applied in the future. However, in the field of unmanned aerial vehicles, the application of ADS-B is yet to be further explored.
The collision detection method is mainly divided into a geometric method and a probability method. The geometric method is mainly used for judging whether the safety zones are overlapped or not by dividing the safety zones and performing linear prediction on future tracks based on the current flight state, for example, a Traffic Collision Avoidance System (TCAS) used by a civil aircraft is the adopted geometric method. The probability law considers random factors such as wind disturbance, navigation error and the like, can calculate the possibility of collision, and is more accurate but also more complex.
The conflict resolution is realized by adopting a route planning method, which comprises an artificial potential field method, a heuristic algorithm represented by a genetic algorithm, an A-algorithm and the like. The ant colony algorithm is one of heuristic global optimization algorithms, is a probabilistic algorithm for searching for an optimized path, has the characteristics of distribution calculation, information positive feedback and heuristic search, and has the advantages of stronger robustness and searchability, but also has the defects of higher requirement on calculation performance and easiness in falling into local optimization. At present, most of route planning methods are based on geometry to search, aircraft dynamics constraint is not considered, search efficiency depends on the size of a planning space, and a plurality of learners focus on the defects of the algorithm and the solving speed, and the judgment of high-quality routes only refers to the standard of delay distance, so that the adaptability of the algorithm has a larger improvement space.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides an unmanned aerial vehicle perception and avoidance strategy based on ADS-B and an ant colony algorithm. The technical scheme adopted by the invention is as follows:
an unmanned aerial vehicle perception and avoidance strategy based on ADS-B and ant colony algorithm comprises the following steps:
And 2, processing the ADS-B message data of the aircraft in the detection domain to obtain the position coordinates, the horizontal velocity value, the course and the vertical velocity of the aircraft in a three-dimensional Cartesian coordinate system.
And 3, carrying out horizontal primary selection on the target machine, and judging whether the target machine gradually approaches according to the formula (1).
S0＝AB·Vr_hori (1)
Wherein AB represents the relative position vector of the target machine on the X-Y two-dimensional horizontal plane, Vr_horiAnd the relative velocity vector of the target machine on the X-Y two-dimensional horizontal plane is shown. If S0If the value is more than or equal to 0, no conflict is generated, and skipping to the step 6; otherwise, continue step 4.
And 4, calculating the horizontal distance of the target machine subjected to horizontal primary selection, and judging according to a formula (2).
And the HMD represents the horizontal offset distance of the target machine relative to the closest point of the flight path on the X-Y two-dimensional horizontal plane. If S1If the value is more than or equal to 0, no conflict is generated, and skipping to the step 6; otherwise, continue with step 5.
And 5, vertically detecting the target machine subjected to horizontal detection, and judging according to formulas (3) and (4).
S2＝[k·(xM-xB)+zB-zM]·[k·(xE-xB)+zB-zE] (3)
S3＝[k·(xN-xB)+zB-zN]·[k·(xF-xB)+zB-zF] (4)
Where k represents the slope of the target machine relative trajectory in the X-Z plane, and point E, F, M, N is the security domain vertical rectangle tangent plane vertex. If S1Is not less than 0 and S2Not less than 0 indicates no conflict generation; otherwise, the target machine is marked as an invader.
And 6, judging whether all target machines in the detection domain complete conflict detection. If not, skipping to step 3; otherwise, modeling the releasing environment based on a specific conflict scene, wherein the size of a conflict releasing sector is set, and discretizing the future routes of the n aircrafts in the releasing sector.
And 7, respectively placing the n aircrafts on the n starting points, recording the n aircrafts as a batch, and simultaneously starting to respectively perform path selection on the n aircrafts. For each step, the selection probability of each strategy is calculated according to the pheromone concentration, the specific calculation formula is formula (5), and random selection is carried out by adopting a roulette mode.
Wherein, tauci(t) is the pheromone value; α and β represent the relative degree of importance of the respective parts; heuristic part eta of comprehensive factorsci(t) is determined by equation (6).
Wherein the content of the first and second substances,
representing the distance deviated from the original track point through index processing, and representing the difference between the angle corresponding to the connecting line of the departure point and the angle corresponding to the conflict path by angle, and representing the path deviation factor together; and the direction and speed represent the difference between the direction and the speed of the previous selection strategy, and represent smoothness factors.
And 8, performing conflict judgment on the planned path, and calculating the distance d between the aircrafts in each step according to a formula (7). If d is larger than or equal to R, no conflict is generated; otherwise, skipping to step 7 for replanning.
And 9, repeating the steps 7-8 until M batches are completed, and recording the M batches as one iteration. And sequencing according to the sum of the final delay distances of each batch of aircrafts in the iteration, and updating the pheromone concentration according to a formula (8).
Wherein rho is more than 0 and less than 1, and is an pheromone attenuation coefficient and represents that the pheromone gradually volatilizes along with time;
the distribution mode of the new pheromone quantity is determined according to the formula (9) by introducing a sequencing mechanism.
Wherein Q is the pheromone release amount of each aircraft,
the final delay distance for the aircraft.
And step 10, repeating the steps 7-9 until T iterations are completed. And outputting the current optimal strategy and the final average delay distance thereof.
The invention has the following beneficial effects:
(1) and planning the route by using a basic ant colony algorithm, and designing a heuristic part around a target function, namely only considering delay distance or omitting the heuristic part under the condition of larger solution space so as to reduce randomness and accelerate convergence speed. On the basis of the basic ant colony algorithm, the comprehensive factor elicitation part is introduced, smoothness factors of delay distance, flight path and speed are considered, and the adaptability of planning the flight path is improved.
(2) The invention provides a pheromone updating method introducing a sequencing mechanism. On one hand, the traditional ant quantity model takes the final total delay of n aircrafts as a standard, is too strong in global property and cannot reflect the difference of each aircraft, and the other ant quantity model considers the delay of each step of each aircraft and is too local. The pheromone updating model provided by the invention takes the final delay of each aircraft as a standard, and takes global and local conditions into consideration. On the other hand, the introduction of the sequencing mechanism can accelerate the convergence of the algorithm, and balance the problems of improvement of randomness and reduction of convergence speed caused by the introduction of comprehensive heuristic factors.
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FIG. 1 is a diagram of a model architecture of the perception and avoidance strategy of an unmanned aerial vehicle based on ADS-B and ant colony algorithm according to the present invention;
FIG. 2 is a schematic diagram of horizontal detection based on ADS-B data information according to the present invention;
FIG. 3 is a schematic diagram of vertical detection based on ADS-B data information according to the present invention;
FIG. 4 is a diagram of simulation results of collision detection based on ADS-B data information according to the present invention;
FIG. 5 is a graph of conflict resolution simulation comparison results based on the basic ant colony algorithm of the present invention;
FIG. 6 is a diagram of a conflict resolution simulation result of the ant colony algorithm based on the heuristic part of the comprehensive factors and the introduction of the ranking mechanism according to the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the present invention more apparent, the following description is made more completely with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, the unmanned aerial vehicle perception and avoidance strategy based on ADS-B and ant colony algorithm mainly includes two parts of conflict detection and conflict resolution on the basis of fully utilizing ADS-B data, and specifically includes the following steps:
And 2, processing the ADS-B message data of the aircraft in the detection domain to obtain the position coordinates, the horizontal velocity value, the course and the vertical velocity of the aircraft in a three-dimensional Cartesian coordinate system.
The machine is marked as A, and the target machine is marked as B. In the X-Y-Z coordinate system, the position coordinate of the machine A is (X)1,y1,z1) The position coordinate of the target machine B is (x)2,y2,z2). On the X-Y two-dimensional horizontal plane, the navigation speed vector of A is
The speed vector of B is
On the X-Z two-dimensional plane, the navigation speed vector of A is
The speed vector of B is
And 3, carrying out horizontal primary selection on the target machine, and judging whether the target machine gradually approaches according to the formula (1).
S0＝AB·Vr_hori (1)
Wherein AB represents the relative position vector of the target machine on the X-Y two-dimensional horizontal plane, Vr_horiAnd the relative velocity vector of the target machine on the X-Y two-dimensional horizontal plane is shown. If S0If the value is more than or equal to 0, no conflict is generated, and skipping to the step 6; otherwise, continue step 4.
And 4, calculating the horizontal distance of the target machine subjected to horizontal primary selection, and judging according to a formula (2) as shown in figure 2.
And the HMD represents the horizontal offset distance of the target machine relative to the closest point of the flight path on the X-Y two-dimensional horizontal plane. If S1If the value is more than or equal to 0, no conflict is generated, and skipping to the step 6; otherwise, continue with step 5.
And 5, performing vertical detection on the target machine subjected to horizontal detection, and judging according to formulas (3) and (4) as shown in fig. 3.
S2＝[k·(xM-xB)+zB-zM]·[k·(xE-xB)+zB-zE] (3)
S3＝[k·(xN-xB)+zB-zN]·[k·(xF-xB)+zB-zF] (4)
Where k represents the slope of the target machine relative trajectory in the X-Z plane, and point E, F, M, N is the security domain vertical rectangle tangent plane vertex. If S1Is not less than 0 and S2Not less than 0 indicates no conflict generation; otherwise, the target machine is marked as an invader.
And 6, judging whether all target machines in the detection domain complete conflict detection. If not, skipping to step 3; otherwise, modeling the release environment based on the specific conflict scenario. Setting the size of a conflict resolution sector, discretizing future routes of n aircrafts in the resolution sector, and dividing each original route section in the resolution sector into K steps according to equal time length.
Each route is divided into K sections, corresponding to K +1 nodes, the 1 st node is marked as a starting point, and the K +1 st node is marked as a target point; there are 9 strategies available for each step, including speed adjustment (deceleration 20%, initial speed hold, acceleration 20%), heading adjustment (left turn 30 °, initial heading hold, right turn 30 °) and simultaneous speed and heading adjustment, denoted as C1-C9。
And 7, respectively placing the n aircrafts on the n starting points, recording the n aircrafts as a batch, and simultaneously starting to respectively perform path selection on the n aircrafts. For each step, the probability of selection for each strategy is calculated according to the pheromone concentration and randomly selected using a roulette method. Recording the current aircraft as the ith (i is more than or equal to 1 and less than or equal to n) frame, the current step is the kth (K is more than or equal to 1 and less than or equal to K), and strategy CiThe selected probability calculation formula is formula (5).
Wherein, tauci(t) is strategy CiPheromone values of corresponding road sections; α and β represent the relative degree of importance of the respective parts; heuristic part eta of comprehensive factorsci(t) is determined by equation (6).
Wherein the content of the first and second substances,
the distance of the ith (i is more than or equal to 1 and less than or equal to n) aircraft deviating from the original track point in the kth (K is more than or equal to 1 and less than or equal to K) is represented,
representing the distance deviated from the original track point through index processing, and representing the difference between the angle corresponding to the connecting line of the departure point and the angle corresponding to the conflict path by angle, and representing the path deviation factor together; and the direction and speed represent the difference between the direction and the speed of the previous selection strategy, and represent smoothness factors.
And 8, performing conflict judgment on the planned path, and calculating the distance d between the aircrafts in each step according to a formula (7). If d is larger than or equal to R, no conflict is generated; otherwise, skipping to step 7 for replanning.
Wherein the content of the first and second substances,
and representing the corresponding two-dimensional horizontal plane coordinate after the aircraft i (i is more than or equal to 1 and less than or equal to n) runs k steps from the starting point.
And 9, repeating the steps 7-8 until M batches are completed, and recording the M batches as one iteration. And (4) recording the T (T is more than or equal to 1 and less than or equal to T) iterations which are completed at present, sequencing the iterations according to the sum of the final delay distances of each batch of aircrafts in the iterations, and updating the pheromone concentration according to a formula (8).
Wherein rho is more than 0 and less than 1, and is an pheromone attenuation coefficient and represents that the pheromone gradually volatilizes along with time;
then for the mth (1. ltoreq. m.ltoreq.M) lot of the iteration, aircraft i (1. ltoreq. i.ltoreq.n) has selected policy C at this stepiThen, the distribution mode of the newly added pheromone quantity left on the road section introduces a sorting mechanism and is determined according to the formula (9).
Wherein Q is the pheromone release amount of each aircraft,
the final delay distance of the aircraft i (i is more than or equal to 1 and less than or equal to n).
And step 10, repeating the steps 7-9 until T iterations are completed. And outputting the current optimal strategy and the final delay thereof.
In order to verify the feasibility and effectiveness of the perception and avoidance strategy of the unmanned aerial vehicle based on the ADS-B and the ant colony algorithm, the effect of the invention is further described through simulation experiments.
The experimental hardware operating environment mainly comprises a 2.8GHz Intel Core i5 processor and a 4GB memory; the software running platform is MATLAB 9.1.
In order to verify the effectiveness of the collision detection method, 100 target aircrafts exist in a range of 40km around a local machine with a coordinate origin through simulation, and key flight state data of the target aircrafts are randomly generated according to the following conditions:
(1) target aircraft speed value: v. ofhori∈[180,360]，vvert∈[-30,30]The unit is km/h;
(2) the course of the target aircraft is as follows: horizontal course and X axisIncluded angle
(3) Target aircraft coordinates: x belongs to (9.26cos theta, 40cos theta), y belongs to (9.26sin theta, 40sin theta), theta belongs to (0,2 pi), z belongs to [1,3], and the unit is km.
The safety interval criterion is 5nmile and 2000 ft. After 100000 monte carlo experiments, the probability distribution result of fig. 4 is obtained, and in 100 conflict target aircrafts, the final average number of conflicts is 7.4597, and the filtering effect is obvious.
To verify the effectiveness of the conflict resolution method, a four-aircraft conflict scenario was simulated, assuming that there were 4 aircraft simultaneously entering a sector with a radius of 50km at the same speed of 360km/h, and the initial flight state settings are as in table 1.
TABLE 1 initial flight State settings
Other relevant parameters are set to be alpha-1, beta-2, the attenuation coefficient rho is 0.3, the pheromone quantity Q is 100, the batch number M is 20, the total step number K is 20, the iteration number T is 200, and the comprehensive factor heuristic part is that C-R is 5 nmile.
The release strategy obtained by using the basic ant colony algorithm without introducing the heuristic part is shown in fig. 5, and corresponds to a final average delay distance of 7.8941 km. The release strategy obtained by adopting the ant colony algorithm introduced with the sequencing mechanism and the comprehensive factor heuristic part is shown in fig. 6, and the corresponding final average delay distance is 0.8562 km.
In conclusion, the effectiveness of the invention is verified by simulation experiments. The foregoing is a more detailed description of the invention that is presented in connection with specific embodiments, and the practice of the invention is not intended to be limited to these descriptions.
Claims (4)
1. An unmanned aerial vehicle perception and avoidance strategy based on ADS-B and ant colony algorithm is characterized by comprising the following steps:
step 1, modeling an initial environment;
step 2, processing the ADS-B message data of the aircraft in the detection domain;
step 3, carrying out horizontal primary selection on the target machine;
step 4, carrying out horizontal detection on the target machine subjected to horizontal primary selection;
step 5, carrying out vertical detection on the target machine subjected to horizontal detection;
step 6, judging whether all target machines in the detection domain complete conflict detection, and if not, skipping to the step 3; otherwise, modeling the releasing environment based on a specific conflict scene;
step 7, performing pheromone concentration-based path selection on the aircraft;
step 8, performing conflict judgment on the planned path, and if a conflict exists, skipping to the step 7 to perform re-planning;
step 9, repeating the steps 7-8 until one iteration is completed, sequencing the sum of the final delay distances of the aircrafts, and updating the pheromone concentration;
and 10, repeating the steps 7-9 until all iterations are completed, and outputting the current optimal strategy and the final average delay distance of the current optimal strategy.
2. The ADS-B and ant colony algorithm-based unmanned aerial vehicle perception and avoidance strategy of claim 1, wherein the deterministic detection of the target machine comprises horizontal primary selection, horizontal detection, vertical detection:
the horizontal initial selection judgment index is the flight tendency of the target aircraft, and the specific judgment index is a formula (1):
S0＝AB·Vr_hori (1)
wherein AB represents the relative position vector of the target machine on the X-Y two-dimensional horizontal plane, Vr_horiRepresenting the relative velocity vector of the target machine on the X-Y two-dimensional horizontal plane if S0The value of more than or equal to 0 represents that the target machine gradually gets away from or keeps the distance without generating conflict; otherwise, further judgment is needed;
the horizontal detection is to calculate the staggered distance of the closest point of the horizontal plane, so as to judge whether the target machine breaks through the horizontal minimum safety interval R, and the specific judgment index is formula (2):
wherein, HMD represents the horizontal offset distance of the target machine relative to the nearest point of the track if S1The condition that the safety interval is not broken through and the conflict is not generated is represented by more than or equal to 0; otherwise, further judgment is needed;
the vertical detection is to judge whether the target machine enters a security domain vertical section or not based on geometric knowledge, and the specific judgment indexes are formulas (3) and (4):
S2＝[k·(xM-xB)+zB-zM]·[k·(xE-xB)+zB-zE] (3)
S3＝[k·(xN-xB)+zB-zN]·[k·(xF-xB)+zB-zF] (4)
wherein k represents the slope of the relative trajectory of the target machine on the X-Z plane, point E, F, M, N is the vertex of the security domain perpendicular to the rectangular tangent plane, if S is1Is not less than 0 and S2The condition that the security domain cannot be entered is more than or equal to 0, and no conflict is generated; otherwise, a conflict may arise.
3. The ADS-B and ant colony algorithm-based unmanned aerial vehicle perception and avoidance strategy of claim 1, wherein in the conflict resolution based on the ant colony algorithm, the aircraft performs path selection based on pheromone concentration, and the specific calculation formula is formula (5):
wherein, tauci(t) is the pheromone value; α and β represent the relative degree of importance of the respective parts; introduced comprehensive factor heuristic part etaci(t), in particularThe calculation formula is formula (6):
wherein the content of the first and second substances,
representing the distance deviated from the original track point through index processing, and representing the difference between the angle corresponding to the connecting line of the departure point and the angle corresponding to the conflict path by angle, and representing the path deviation factor together; and the direction and speed represent the difference between the direction and the speed of the previous selection strategy, and represent smoothness factors.
4. The ADS-B and ant colony algorithm-based unmanned aerial vehicle perception and avoidance strategy of claim 1, wherein in the ant colony algorithm-based conflict resolution, the pheromone concentration is updated after one iteration, and the specific calculation formula is formula (7):
wherein rho is more than 0 and less than 1, and is an pheromone attenuation coefficient and represents that the pheromone gradually volatilizes along with time;
the distribution mode of the newly added pheromone quantity is introduced into a sequencing mechanism, and the specific calculation formula is formula (8):
wherein Q is the pheromone release amount of each aircraft,
for ultimate delay of aircraftDistance.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113340306A (en) * | 2021-08-05 | 2021-09-03 | 北京航空航天大学 | Multi-source navigation enhancement method suitable for low-altitude airspace unmanned aerial vehicle complex flight |
| CN115390575A (en) * | 2022-08-11 | 2022-11-25 | 北京航空航天大学杭州创新研究院 | Automatic unmanned aerial vehicle following implementation method and device based on ADS-B |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103869822A (en) * | 2014-04-01 | 2014-06-18 | 西北工业大学 | Multiple-rotor-wing unmanned aerial vehicle sensing and avoiding system and avoiding method thereof |
| US20160104382A1 (en) * | 2014-10-14 | 2016-04-14 | The Boeing Company | Method for creating and choosing a determinate piloting strategy for an aircraft |
| CN105718997A (en) * | 2016-01-15 | 2016-06-29 | 中国民航管理干部学院 | Hybrid multi-aircraft conflict resolution method based on artificial potential field method and ant colony algorithm |
| CN108413959A (en) * | 2017-12-13 | 2018-08-17 | 南京航空航天大学 | Based on the Path Planning for UAV for improving Chaos Ant Colony Optimization |
| CN108428004A (en) * | 2018-01-16 | 2018-08-21 | 河北科技大学 | Flying object conflict Resolution paths planning method based on ant group algorithm |
| CN109521794A (en) * | 2018-12-07 | 2019-03-26 | 南京航空航天大学 | A kind of multiple no-manned plane routeing and dynamic obstacle avoidance method |
| CN109917811A (en) * | 2019-04-12 | 2019-06-21 | 中国人民解放军国防科技大学 | Unmanned aerial vehicle cluster cooperative obstacle avoidance-reconstruction processing method |
| CN109960802A (en) * | 2019-03-19 | 2019-07-02 | 四川大学 | The information processing method and device of narrative text are reported for aviation safety |
| CN110793522A (en) * | 2019-07-24 | 2020-02-14 | 西安电子科技大学 | Flight path planning method based on ant colony algorithm |
| CN111897316A (en) * | 2020-06-22 | 2020-11-06 | 北京航空航天大学 | Multi-aircraft autonomous decision-making method under scene fast-changing condition |
-
2020
- 2020-12-24 CN CN202011553006.3A patent/CN112462804B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103869822A (en) * | 2014-04-01 | 2014-06-18 | 西北工业大学 | Multiple-rotor-wing unmanned aerial vehicle sensing and avoiding system and avoiding method thereof |
| US20160104382A1 (en) * | 2014-10-14 | 2016-04-14 | The Boeing Company | Method for creating and choosing a determinate piloting strategy for an aircraft |
| CN105718997A (en) * | 2016-01-15 | 2016-06-29 | 中国民航管理干部学院 | Hybrid multi-aircraft conflict resolution method based on artificial potential field method and ant colony algorithm |
| CN108413959A (en) * | 2017-12-13 | 2018-08-17 | 南京航空航天大学 | Based on the Path Planning for UAV for improving Chaos Ant Colony Optimization |
| CN108428004A (en) * | 2018-01-16 | 2018-08-21 | 河北科技大学 | Flying object conflict Resolution paths planning method based on ant group algorithm |
| CN109521794A (en) * | 2018-12-07 | 2019-03-26 | 南京航空航天大学 | A kind of multiple no-manned plane routeing and dynamic obstacle avoidance method |
| CN109960802A (en) * | 2019-03-19 | 2019-07-02 | 四川大学 | The information processing method and device of narrative text are reported for aviation safety |
| CN109917811A (en) * | 2019-04-12 | 2019-06-21 | 中国人民解放军国防科技大学 | Unmanned aerial vehicle cluster cooperative obstacle avoidance-reconstruction processing method |
| CN110793522A (en) * | 2019-07-24 | 2020-02-14 | 西安电子科技大学 | Flight path planning method based on ant colony algorithm |
| CN111897316A (en) * | 2020-06-22 | 2020-11-06 | 北京航空航天大学 | Multi-aircraft autonomous decision-making method under scene fast-changing condition |
Non-Patent Citations (5)
| Title |
|---|
| CHENCHEN XU DENG: "《Recent Research Progress of Unmanned Aerial Vehicle Regulation Policies and Technologies in Urban Low Altitude》", 《IEEE》 * |
| 揭东: "《无人机冲突探测与解脱问题研究》", 《中国优秀博硕士学位论文全文数据库(硕士) 工程科技Ⅱ辑》 * |
| 揭东等: "改进蚁群算法的多无人机冲突解脱技术研究", 《武汉理工大学学报(交通科学与工程版)》 * |
| 李猛等: "采用多重启发蚁群优化算法的无人机航迹规划", 《华南理工大学学报(自然科学版)》 * |
| 贾云聪: "《基于ADS-B制式的无人机冲突解脱方法与避险装备研究》", 《中国优秀博硕士学位论文全文数据库(硕士) 工程科技Ⅱ辑》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113340306A (en) * | 2021-08-05 | 2021-09-03 | 北京航空航天大学 | Multi-source navigation enhancement method suitable for low-altitude airspace unmanned aerial vehicle complex flight |
| CN113340306B (en) * | 2021-08-05 | 2021-12-10 | 北京航空航天大学 | Multi-source navigation enhancement method suitable for low-altitude airspace unmanned aerial vehicle complex flight |
| CN115390575A (en) * | 2022-08-11 | 2022-11-25 | 北京航空航天大学杭州创新研究院 | Automatic unmanned aerial vehicle following implementation method and device based on ADS-B |
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