CN117935625B - Intelligent air traffic unmanned aerial vehicle route management system and method - Google Patents
Intelligent air traffic unmanned aerial vehicle route management system and method Download PDFInfo
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Abstract
The invention discloses a system and a method for managing an intelligent air traffic unmanned aerial vehicle route, wherein the system comprises the following steps: the system comprises a route structure dividing module, an in-route operation management module and a route cross operation management module, wherein: the route structure dividing module is used for dividing a public pipeline space above the city and comprises an operation space, a protection space and an emergency treatment space; the in-route operation management module is used for setting intelligent air traffic route intervals, making emergency operation programs and configuring priorities; the route cross operation management module is used for carrying out route cross operation management by adopting a multi-machine artificial potential field and artificial bee colony optimization conflict resolution method when the route cross exists in the condition that the flight frame has higher secondary density. The invention designs a route structure, carries out route cross operation management by a multi-machine artificial potential field and artificial bee colony optimization conflict resolution method, is convenient for automatic conflict resolution, improves intelligent air traffic automation level and safety level, reduces collision probability and improves route operation efficiency.
Description
Technical Field
The invention relates to the technical field of intelligent air traffic management, in particular to an intelligent air traffic unmanned aerial vehicle route management system and method.
Background
Currently, unmanned aviation is continuously and rapidly developed and iteratively evolved, and becomes a new social life and economic production mode, representing the development trend of the global aviation industry.
The unmanned aerial vehicle has the characteristics of high digitalization, networking, intellectualization and the like, and is continuously integrated into an airspace system in the future. There has been a great development for many unmanned aerial vehicle logistics merchants in China. However, unmanned aerial vehicles are various in types and complex in scenes, a great challenge is provided for traditional aviation supervision systems and technical means, and how to ensure collision-free flight of multiple unmanned aerial vehicles in an urban airspace, so that safe and efficient urban air traffic management is achieved, and the system and the method are time challenges facing civil aviation management together.
At present, urban air traffic supervision mainly takes policies as main, and lacks effective and complete service guarantee and technical means. The traditional air traffic management mainly adopts a mode of providing control service based on a sector controller to realize the operation safety interval and efficiency of the high-density aircraft, the general aviation mainly provides weather information monitoring information through a flight service mechanism, and a pilot independently visualizes the flight to ensure the safety interval. However, compared with transportation aviation and general aviation, the smart city air traffic has stronger digital and intelligent properties in the aspects of operation environment, unmanned aerial vehicle, operation management, service guarantee and the like. Therefore, the current air traffic management based on personal decision and the infrastructure, informatization system and service guarantee mode of general aviation cannot meet the air traffic development requirements of smart cities.
In the prior art, a mature and efficient unmanned aerial vehicle air traffic management system under intelligent city air traffic operation scenes such as unmanned aerial vehicle logistics transportation and instant distribution is not provided temporarily, a mature and efficient unmanned aerial vehicle route management strategy is not provided, the existing management means are very simple and lack of automatic setting, manual access is needed, technical conditions are weak, and route operation efficiency is required to be improved. Therefore, new ideas and schemes are required to be provided in the aspects of air traffic scale, digitalization, fine management and the like.
Disclosure of Invention
In view of this, the present invention provides a system and a method for route management of an intelligent air traffic unmanned aerial vehicle, which at least solve the above-mentioned part of technical problems, so as to facilitate automatic conflict resolution, improve automation level and security level, reduce collision probability, and improve route operation efficiency.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
In a first aspect, the present invention provides an intelligent air traffic unmanned aerial vehicle route management system, the system comprising: the system comprises a route structure dividing module, an in-route operation management module and a route cross operation management module, wherein:
The route structure dividing module is used for dividing public pipeline space above a city, and the divided public pipeline space comprises: a run space, a protected space, and an emergency disposal space; the in-route operation management module is used for setting intelligent air traffic route intervals, making emergency operation programs and configuring priorities; the route cross operation management module is used for carrying out route cross operation management by adopting a multi-machine artificial potential field and artificial bee colony optimization conflict resolution method when the route cross exists in the condition that the flight frame frequency density is higher.
Further, an operation space marked by the route structure dividing module adopts a nine-grid operation mode.
Further, the emergency running program formulated by the in-line running management module comprises:
① When special conditions occur in the airlines, the airlines deviate from the airlines to enter a protection space immediately, the airlines enter an operation space again after the special conditions are relieved, and if the operation cannot be recovered, the airlines are lowered in an emergency disposal space;
② One side avoidance rule is adopted in the channel in the route; the unmanned aerial vehicle newly enters the air route to avoid other unmanned aerial vehicles; the unmanned aerial vehicle in the route changes the flying height and needs to avoid other unmanned aerial vehicles; the unmanned aerial vehicle is positioned at the rear in the same direction in the route to avoid the front unmanned aerial vehicle; the same direction overrun flight is allowed in the route, and the overrun is completed by means of the protection space.
Further, the calculation method of the multi-machine artificial potential field adopted by the route cross operation management module comprises the following steps:
wherein, Representing the resultant force of the basic artificial repulsive force applied by the aircraft i; /(I)The number of the aircraft j generating artificial repulsive force to the aircraft i; /(I)Representing a basic manual repulsive force; /(I)Representing the fundamental resultant force experienced by aircraft i; /(I)Indicating that aircraft i is subject to power directed from the airline entrance to the airline exit; η represents the rejection gain; d represents the distance between aircraft i and aircraft j; d_min represents the minimum value of the rejection distance; d_max represents the maximum value of the repulsive distance; a_max represents the maximum repulsive force value.
Further, the course cross operation management module adopts artificial bee colony optimization process comprising:
1) Determining an optimization index
The optimization metrics include distance cost and time cost, wherein:
Distance cost:
wherein, Representing the total number of aircraft frames occurring in a conflict resolution range within a time T, and i represents a corresponding aircraft; t represents the current time,/>Representing the initial velocity of the current product segment of aircraft i,/>Representing the mass of aircraft i,/>Representing superimposed artificial forces of aircraft i,/>Representing the fundamental resultant force experienced by aircraft i; /(I)Indicating that the aircraft i is located on the route p before passing through the intersection, and the route on the route is located on the route k,/>After the aircraft passes through the intersection, the route number of the aircraft i is q, and the route number of the aircraft i is m; /(I)Is the expected flight distance of aircraft i; /(I)Representing the weighting coefficients;
Time cost:
wherein, Representing the length of aircraft i that has actually flown in a discrete calculation interval within the conflict resolution range,/>Representing the actual flight length of aircraft i within the discrete computation interval within the conflict resolution range,/>Representing the maximum value of the actual flight length of aircraft i within the conflict resolution range; /(I)Expressed at/>Actual flight time in a discrete calculation interval of the position; /(I)Representing the ending speed of the current discrete computation interval; /(I)Representing an initial velocity of the current differential interval; /(I)Representing a time of flight of aircraft i on a desired course path within the conflict resolution range;
The final cost function is:
Wherein W is a weighting function for allocating the ratio between the distance cost and the time cost;
Superimposed artificial force A value representing a change with the calculation section; /(I)Respectively representing forces in different coordinate directions; the optimization target is to select the optimal sequence/>Solution is optimalMake/>Minimum;
2) Artificial bee colony method optimization
① Initializing population solutions
Setting the population scale of the NS group, and randomly generating initial population solutions of the NS groupEach solution is a sequence value of superimposed artificial force/>The honey is marked as an initial honey source of the bees; calculating the cost function J sequence of initial bee population solution
Setting fitness functionsCharacterization/>Smaller,/>The greater the value
② Leading bee calculation
Setting leading bees to search new honey sources
Wherein,Representing random numbers within [ -1,1 ]/>，/>When the fitness function of the new honey source is superior to that of the old honey source, enabling the new honey source to replace the old honey source according to the greedy principle;
③ Following bee calculation
Calculating the following probability；
The following bees are arranged to select the leading bees by roulette, i.e. to generate a random number of uniform distribution in [0,1], ifIf the number is larger than the random number, generating a new honey source, and judging whether to reserve the honey source by using the same calculation principle as that of the leading bees;
④ Calculation of scout bees
Judging whether the honey source meets the abandoned condition or not by the step ③; if yes, changing the corresponding leading bee role into a reconnaissance bee, searching a new solution in a specified solution domain, otherwise, directly transferring to a step ⑤;
⑤ Iteration of the loop
Judging whether the cycle times are greater than a preset maximum value, if so, stopping population optimization iteration, or if soIf the variation of (2) is less than 1e-8, stopping population optimization iteration; if none of them is satisfied, the population continues to be optimized.
In a second aspect, the present invention further provides a method for managing a route of an intelligent air traffic unmanned aerial vehicle, which is applied to the above system for managing a route of an intelligent air traffic unmanned aerial vehicle, to implement efficient management of a route of an intelligent air traffic unmanned aerial vehicle, and the method includes:
a public pipeline space is marked up in the urban overhead space, and the marked public pipeline space comprises: a run space, a protected space, and an emergency disposal space;
when the unmanned aerial vehicle runs in the route channel without crossing, the route running management is carried out through the set route interval, the emergency running program and the priority configuration;
When the flight frame has higher secondary density and has the route crossing, the route crossing operation management is carried out by adopting a multi-machine artificial potential field and artificial bee colony optimization conflict resolution method.
Compared with the prior art, the invention has at least the following beneficial technical effects:
The invention provides an intelligent air traffic unmanned aerial vehicle route management system and method, wherein the system comprises the following steps: the system comprises a route structure dividing module, an in-route operation management module and a route cross operation management module; the invention designs the route structure, defines the division of the safety area, and carries out route cross operation management by a multi-machine artificial potential field and artificial bee colony optimization conflict resolution method when the route cross exists in higher flight frame density, thereby facilitating automatic conflict resolution, improving automation level and safety level, reducing collision probability, improving route operation efficiency, and being beneficial to realizing safe and efficient route management under the operation scenes of unmanned aerial vehicle logistics transportation, instant distribution and the like.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention.
Fig. 1 is a schematic structural diagram of an intelligent air traffic unmanned aerial vehicle route management system according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a SAM route section model according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a SAM route plane model according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of an operation mode of the nine-square grid according to the embodiment of the present invention.
Fig. 5 is a schematic diagram of SAM linewidth/height according to an embodiment of the present invention.
Fig. 6 is a schematic diagram of a first emergency running procedure according to an embodiment of the present invention.
Fig. 7 is a schematic diagram of a second emergency running procedure according to an embodiment of the present invention.
FIG. 8 is a schematic diagram of two intersecting routes provided by an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Referring to fig. 1, an embodiment of the present invention provides an intelligent air traffic unmanned aerial vehicle route management system, which includes: the system comprises a route structure dividing module, an in-route operation management module and a route cross operation management module, wherein: the route structure dividing module is used for dividing public pipeline space above the city, and the divided public pipeline space comprises: a run space, a protected space, and an emergency disposal space; the in-route operation management module is used for setting intelligent air traffic route intervals, making emergency operation programs and configuring priorities; the route cross operation management module is used for carrying out route cross operation management by adopting a multi-machine artificial potential field and artificial bee colony optimization conflict resolution method when the route cross exists in the condition that the flight frame has higher secondary density.
The following describes the working principle of the specific embodiment and each functional module of the present invention in detail:
In the embodiment of the invention, the intelligent air traffic (SAM SMART AIR TRAFFIC) refers to a public pipeline space which is marked in the air in the city for meeting the requirement of the isolated operation of the civil unmanned aerial vehicle. The SAM unmanned aerial vehicle runs in the urban space, and the SAM air line running rule is adopted, so that the requirement of the SAM unmanned aerial vehicle on the over-the-horizon running of the urban low-altitude airspace can be met.
1. Route structure division
In this embodiment, referring to fig. 2 and 3, the route structure dividing module divides a public pipeline space in the space above the city, where the divided public pipeline space includes: run space, protected space, and emergency handling space. Wherein: the operation space is the space in which the unmanned aerial vehicle flies in the normal operation program. The protection space is a space for protecting abnormal operation programs under the conditions of accidents, gusts and the like of the unmanned aerial vehicle, and can also be used as a temporary 'borrowing' space for the homodromous overrunning of the unmanned aerial vehicle in the channel. The emergency disposal space is a passage space where the unmanned aerial vehicle needs to fly out of the operation space when an emergency situation occurs, such as infrastructure failure, unmanned aerial vehicle failure forced landing, and the like. Intersecting routes in the plan view are intersecting routes, and if the intersecting routes have space intersecting points in the same height layer, the intersecting points are marked as route intersecting points.
In a specific embodiment, the operation space parameter setting of the SAM route is preferably determined according to the operation capability of the unmanned aerial vehicle enterprise and the flight precision of the unmanned aerial vehicle, and is obtained through repeated demonstration of simulation and experimental test. For example, for a multi-rotor unmanned aerial vehicle with a maximum size of 2.3m, a cruising speed of 14m/s, a flying height of 200-300m and a maximum takeoff weight of 45kg, a normal running space of the SAM route can be provided with a "nine-grid" with a cross-sectional area of 400 meters by 40 meters, as shown in FIG. 4, 9 "flights" are marked in the nine-grid, and the numbers are 1-9 respectively. The running space adopts a nine-grid running mode, for example, 1, 9 or 3 and 7 bidirectional running 'flight paths' are used, and the maximum bidirectional running interval in the channel is ensured.
In the embodiment of the invention, the SAM route protection space mainly considers the range of a risk buffer zone of the unmanned aerial vehicle in an emergency, and the risk buffer zone comprises an empty risk and a ground risk. Still taking the above operation parameters as an example, referring to fig. 5, according to the standard of maximum flight height/protection zone width=1, the SAM route protection zone width is preferably set to 300 meters, and by setting the protection zone, the risk of the unmanned aerial vehicle to the ground and the air after being out of control can be greatly reduced. In conclusion, the SAM route protection area extends 300 meters respectively to the left and right of the normal operation space and extends 10 meters respectively to the top and bottom. The width of the final SAM course = running space width + protection zone width = 1000 meters, the height of the SAM course = running space height + protection zone height = 60 meters. The SAM route is preferably planned by comprehensively considering factors such as a low-altitude airspace, ground risks, guarantee facilities and the like, and considering the limitation of a part of airspace due to geographic environment and airspace environment, the space size can be adjusted according to the running airspace condition, but the horizontal width is not less than 300 meters, and meanwhile, the running program and the admittance conditions are correspondingly adjusted.
2. SAM in-flight operations management
In embodiments of the present invention, the separation between unmanned aerial vehicles of the same operator within a SAM route may be responsible for that operator. The preferred arrangement between unmanned aerial vehicles of different operators within the SAM route is maintained at a horizontal spacing of not less than 100 meters or a vertical spacing of not less than 10 meters.
In the embodiment of the invention, an emergency running program is shown in fig. 6, an emergency event happened to an operator in a SAM route needs to deviate right from the route immediately to enter a protection space, and enters the running space again after the special condition is relieved, if the running cannot be restored, the operator needs to fall in the next emergency disposal space.
Another emergency running procedure is shown in fig. 7, wherein the right side avoidance principle is preferably adopted in the channel in the SAM route; the unmanned aerial vehicle newly enters the air route to avoid other unmanned aerial vehicles; the unmanned aerial vehicle in the route changes the flying height and should avoid other unmanned aerial vehicles; the unmanned aerial vehicle is positioned at the rear in the same direction in the route to avoid the front unmanned aerial vehicle; the same direction overrun flight is allowed in the route, and the overrun can be completed by means of the protection space. The unmanned aerial vehicle is added into or exits from the channel after keeping the vertical take-off and landing to reach the height of the channel, and other flying inside and outside the channel is avoided.
3. SAM route cross operation management
In the present invention, if SAM route intersections occur, there are several cases:
1. The crossing points are layered in height, namely no crossing exists in space, so that crossing airlines are distinguished in height, and a 50m height interval redundancy is reserved preferably, so that an unmanned aerial vehicle air collision event is avoided;
2. If, due to the dense airspace, it is not possible to run in layers in altitude, i.e. two or more channels cross at the same altitude, there is a course crossing. If the flight frame secondary density is very low, the method can judge whether unmanned aerial vehicles of other channels are running or not at the position 500m in front of the intersection based on the principle of 'first arrival', if not, the unmanned aerial vehicles pass through, if the unmanned aerial vehicles pass through, the unmanned aerial vehicles hover and wait, and after the unmanned aerial vehicles pass through, the unmanned aerial vehicles pass through. If the flying frame has higher secondary density, the fish passes through the junction by adopting a multi-machine artificial potential field and artificial bee colony optimization conflict resolution method.
The following focuses on the method for solving the conflict of multi-machine artificial potential field and artificial bee colony optimization adopted by the route cross operation management module:
referring to FIG. 8, the cross point is taken at the same level The circle of (2) is used as the conflict resolution range. Consider a total of n intersecting routes (two intersecting routes in fig. 8, which is common), which are calculated from the time when any aircraft enters the conflict resolution range, assuming that the duration from the first aircraft to the last aircraft leaves is T (which may be a calculation unit on a daily basis if the flight frames are less dense). At a certain moment, for a single aircraft i, the aircraft i receives power/>, which is directed from the airline entrance to the airline exit, due to the action of the aircraft power and control system. In order to prevent collision conflict among multiple aircraft, in the embodiment, an artificial potential field method is adopted, and basic artificial repulsive force/>, from other aircraft j, is superimposed on control force of the aircraft(I.e., the resultant force of the fundamental artificial repulsive force experienced by aircraft i)/>The number of aircraft within D_max, i.e., the number of aircraft j that may generate a manual repulsive force to aircraft i. The aircraft is subjected to a fundamental resultant force。
Basic manual repulsive forceIs calculated by (1): first, a rejection distance threshold (d_min, d_max), a rejection gain η and a peak a_max between two aircraft (i and j) are defined. If the distance D between aircraft i and aircraft j is between D_min and D_max, then let the repulsive force/>. If D is greater than D_max, the repulsive force is zero. And if D is smaller than D_min, let/>=A_max, the maximum repulsive force value. By/>The acceleration required for the flight of the aircraft can be calculated to determine the value superimposed on the control force and thus influence the movement of the aircraft. When two aircraft approach, the force of the artificial potential field increases, slowing down the two aircraft. When the two objects are kept at a safe distance, the artificial potential field forces are eliminated. The control force needs to set an output limit to balance the relation of power and repulsive force, reduce the excessive amount, and adjust the distance of acting artificial potential force according to the airplane conditions of different weight levels so as to obtain the optimal control effect.
Superimposed artificial forceIs calculated by (1): the calculation of the basic manual repulsive force is equivalent to a fixed basic value, which is only related to the distance between the airplanes and the characteristics of the airplanes, but is not related to the flow in the conflict resolution range. Therefore, a superposition factor needs to be introduced to comprehensively allocate the whole traffic situation in the conflict resolution range of the intersection. The artificial bee colony method is adopted for optimization, and the method comprises the following steps:
1) Determining an optimization index
In this embodiment, the optimization index considers two factors: one is that the aircraft has a switching cost between the crossing points, which flight path of which course is changed into which flight path of which course, and also avoids the flight flow of the cross course, and the larger the flying length of avoiding or bypassing, the larger the distance cost; one is the time factor, and the need to slow down or bypass the aircraft through the intersection in order not to collide with other airlines, creates time costs. Wherein:
Distance cost:
wherein, Represents the total number of aircraft frames occurring within the conflict resolution range within time T, i represents
The calculation unit (or integration interval) corresponds to the aircraft.Representing the sum of the distances travelled by avoiding, bypassing, conflicting aircraft after considering the superposition factor, t representing the current moment,/>Representing the initial velocity of the current product segment of the ith aircraft,/>Representing the mass of the ith aircraft,/>Representing the superimposed artificial force of the ith aircraft,/>Representing the resultant force of the ith aircraft. /(I)Representing the estimated lane change distance, whereinRepresenting the course number p of the aircraft i before passing through the intersection, the course number k of the aircraft i (where k is only two choices, either 1 or 7 or 3 or 9, depending on the direction or reverse, p is equal to or less than the total course number n),/>, as described aboveRepresenting that after passing through the intersection, the aircraft i is positioned with a route number q, the route number m is positioned with the route number m, and q is less than or equal to the total route number n,/>Represents the slave/>Change channel to/>Is the shortest straight line distance of (2). /(I)Representing the sum of the distances an aircraft is expected to travel within the conflict resolution range,/>For the desired flight distance of the ith aircraft, it may be determined based on the course parameter settings of the aircraft prior to flight. /(I)Representing weighting coefficients for use in assigning weights between avoidance, detour, and lane change.
Time cost:
Wherein the formula is The first part represents the actual time of flight under the influence of the superimposed artificial force, and the second part represents the desired time of flight, wherein/>For the length that the ith aircraft has actually flown in the discrete computation interval within the conflict resolution range,/>For the actual flight length of the ith aircraft in the discrete calculation interval within the conflict resolution range,Is the maximum value of the actual flight length of the ith aircraft within the conflict resolution range. /(I)Represented by/>Actual flight time in discrete calculation interval of position, and overlapping artificial force/>, under non-uniform state, is consideredCan be obtained by dividing the velocity deviation by the acceleration (estimation process according to uniform acceleration), wherein/>For the ending speed of the current discrete calculation interval,An initial speed for the current differential interval; in a constant speed state,/>Dividing the actual flight length of the discrete calculation interval by the current speed. /(I)For the actual flight time of the ith aircraft in the conflict resolution range, the actual flight time can be solved by a numerical calculation method. /(I)Representing the sum of the expected flight times of the aircraft within the conflict resolution range,/>For the time of flight of the ith aircraft on the desired airline path within the range of conflict resolution (without regard to avoidance, etc.), it may be determined based on the set of airline parameters for the aircraft prior to flight.
Final cost function
W is a weighting function for blending the ratio between the distance cost and the time cost.
Superimposed artificial forceA value representing a change with the calculation section; /(I)Respectively representing forces in different coordinate directions;
the optimization objective is to select the proper Sequence/>Solution to the optimum/>Make/>Minimum.
2) Adopting the optimization step of the artificial bee colony method
① Initializing population solutions
Setting the population scale of the NS group, and randomly generating initial population solutions of the NS group(Ns=50 may be set), each solution is a sequence value of superimposed artificial force/>The initial honey source of the bees is recorded. Calculating the cost function J sequence of initial bee population solution
Setting fitness functionsCharacterization/>Smaller,/>The greater the value
② Leading bee calculation
Setting leading bees to search new honey sources according to the following method
Is a random number within [ -1,1 ]/>，/>When the fitness function of the new honey source is better than that of the old honey source, the new honey source is made to replace the old honey source according to the greedy principle.
③ Following bee calculation
Calculating the following probability。
The following bees are arranged to select the leading bees by roulette, i.e. to generate a random number of uniform distribution in [0,1], ifGreater than the random number, follow the bee press/>The formula generates a new honey source around the i honey source, and judges whether to reserve the honey source by using the same calculation principle as the leading bees.
④ Calculation of scout bees
From the previous step, it is determined whether the honey source i satisfies the discarded condition. If yes, the corresponding leading bee role becomes a reconnaissance bee, searching for a new solution in a specified solution domain, otherwise, directly turning to the step 5;
⑤ Iteration of the loop
Determining if the number of loops is greater than a maximum (e.g., can be set to 500), stopping the population optimization iteration if satisfied, or if satisfiedIf the variation of (2) is less than 1e-8, stopping the population optimization iteration if the variation is satisfied, and if the variation is not satisfied, continuing to optimize the population according to the steps.
3) Obtaining the result of optimizing and overlapping the manual force
According to artificial bee colony algorithm, getRelatively small solution/>Obtaining the optimized value/>, of the superposition artificial force of each calculation interval。
Finally, the embodiment of the invention carries out cross route simulation verification, and verifies whether the unmanned aerial vehicle passes through the cross point smoothly without conflict and transfers to a new channel. The verification optimization algorithm is effective, and the multiple unmanned aerial vehicles smoothly pass through the intersection without collision and are transferred to a new channel.
From the above description of embodiments, those skilled in the art will appreciate that the present invention provides an intelligent air traffic unmanned aerial vehicle route management system, comprising: the system comprises a route structure dividing module, an in-route operation management module and a route cross operation management module, wherein: the route structure dividing module is used for dividing public pipeline space above the city, and the divided public pipeline space comprises: a run space, a protected space, and an emergency disposal space; the in-route operation management module is used for setting intelligent air traffic route intervals, making emergency operation programs and configuring priorities; the route cross operation management module is used for carrying out route cross operation management by adopting a multi-machine artificial potential field and artificial bee colony optimization conflict resolution method when the route cross exists in the condition that the flight frame has higher secondary density. The invention designs the route structure, defines the division of the safe area, carries out route cross operation management by a multi-machine artificial potential field and artificial bee colony optimization conflict resolution method, is convenient for automatic conflict resolution, improves the automation level and the safety level, reduces the collision probability, improves the route operation efficiency, and is beneficial to realizing safe and efficient route management under the operation scenes of unmanned plane logistics transportation, instant distribution and the like.
Further, the embodiment of the invention also provides a smart air traffic unmanned aerial vehicle route management method, which is applied to the smart air traffic unmanned aerial vehicle route management system of the embodiment, and is used for performing smart air traffic unmanned aerial vehicle route management, and the method comprises the following steps:
A public pipeline space is marked up in the urban overhead, and the marked public pipeline space comprises: a run space, a protected space, and an emergency disposal space; when the unmanned aerial vehicle runs in the route channel without crossing, the route running management is carried out through the set route interval, the emergency running program and the priority configuration; when the flight frame has higher secondary density and has the route crossing, the route crossing operation management is carried out by adopting a multi-machine artificial potential field and artificial bee colony optimization conflict resolution method.
The implementation principle and the generated technical effects of the intelligent air traffic unmanned aerial vehicle route management method provided by the embodiment of the invention are the same as those of the system embodiment, and for the sake of brief description, the details of the embodiment are not mentioned, and reference is made to the corresponding content in the system embodiment, so that no redundant description is provided.
In addition, an embodiment of the present invention further provides a storage medium, on which one or more programs readable by a computing device are stored, the one or more programs including instructions, which when executed by the computing device, cause the computing device to perform the intelligent air traffic unmanned aerial vehicle route management method in the above embodiment.
In an embodiment of the present invention, the storage medium may be, for example, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the storage medium include: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, and any suitable combination of the foregoing.
It will be appreciated by those skilled in the art that embodiments of the invention may be provided as a system, method, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
It is to be noticed that the term 'comprising', does not exclude the presence of elements or steps other than those listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (3)
1. An intelligent air traffic unmanned aerial vehicle route management system, which is characterized in that the system comprises: the system comprises a route structure dividing module, an in-route operation management module and a route cross operation management module, wherein:
The route structure dividing module is used for dividing public pipeline space above a city, and the divided public pipeline space comprises: a run space, a protected space, and an emergency disposal space; the in-route operation management module is used for setting intelligent air traffic route intervals, making emergency operation programs and configuring priorities; the route cross operation management module is used for carrying out route cross operation management by adopting a multi-machine artificial potential field and artificial bee colony optimization conflict resolution method when the route cross exists in the higher-order density of the flight frames;
the operation space marked by the route structure dividing module adopts a nine-grid operation mode;
the emergency running program formulated by the in-route running management module comprises the following steps:
① When an emergency happens in the air route, the air route deviates from the air route immediately to enter a protection space, enters the operation space again after the special situation is relieved, and if the operation cannot be recovered, the emergency treatment space is reserved for descent;
② One side avoidance rule is adopted in the channel in the route; the unmanned aerial vehicle newly enters the air route to avoid other unmanned aerial vehicles; the unmanned aerial vehicle in the route changes the flying height and needs to avoid other unmanned aerial vehicles; the unmanned aerial vehicle is positioned at the rear in the same direction in the route to avoid the front unmanned aerial vehicle; allowing the same-direction overrun flight in the route, and completing overrun by means of a protection space;
the calculation method of the multi-machine artificial potential field adopted by the route cross operation management module comprises the following steps:
wherein, Representing the resultant force of the basic artificial repulsive force applied by the aircraft i; n j is the number of the planes j generating artificial repulsive force to the plane i; /(I)Representing a basic manual repulsive force; f i represents the fundamental resultant force experienced by aircraft i; /(I)Indicating that aircraft i is subject to power directed from the airline entrance to the airline exit; η represents the rejection gain; d represents the distance between aircraft i and aircraft j; d_min represents the minimum value of the rejection distance; d_max represents the maximum value of the repulsive distance; a_max represents the maximum repulsive force value.
2. The intelligent air traffic unmanned aerial vehicle route management system of claim 1, wherein the route cross-over operation management module employs artificial bee colony optimization comprising:
1) Determining an optimization index
The optimization metrics include distance cost and time cost, wherein:
Distance cost:
Wherein, N i represents the total number of airplane frames occurring in a conflict resolution range within a time T range, and i represents a corresponding airplane; t represents the current time of day and, Representing the initial velocity of the current product segment of aircraft i, m i representing the mass of aircraft i, F i representing the superimposed artificial force of aircraft i, and F i representing the fundamental resultant force experienced by aircraft i; /(I)Indicating that the aircraft i is located on the route p before passing through the intersection, and the route on the route is located on the route k,/>After the aircraft passes through the intersection, the route number of the aircraft i is q, and the route number of the aircraft i is m; l i is the desired flight distance of aircraft i; w 1、W2 represents a weighting coefficient;
Time cost:
where dl i represents the length that aircraft i has actually flown in the discrete calculated interval within the conflict resolution range, ddl i represents the actual length of flight of aircraft i in the discrete calculated interval within the conflict resolution range, and ∈ddl i represents the maximum value of the actual length of flight of aircraft i within the conflict resolution range; dt ni represents the actual time of flight in the discrete calculation interval of the dl i position; Representing the ending speed of the current discrete computation interval; /(I) Representing an initial velocity of the current differential interval; t i represents the time of flight of aircraft i on the desired course path within the conflict resolution range;
The final cost function is:
J＝Jdist+WJt
Wherein W is a weighting function for allocating the ratio between the distance cost and the time cost;
Providing a superimposed artificial force f= [ x f,yf,zf ] to represent a value varying with the calculation interval; x f,yf,zf represents forces in different coordinate directions, respectively; the optimization target is to select an optimal sequence x, and the solution optimal x= [ x f0,yf0,zf0,xf1,yf1,zf1, ] is the smallest J;
2) Artificial bee colony method optimization
① Initializing population solutions
Setting the population scale of the NS group, and randomly generating initial population solutions of the NS groupEach solution is a sequence value/>, which superimposes artificial forcesThe honey is marked as an initial honey source of the bees; calculating the cost function J sequence of initial bee population solution
Setting a fitness function fit, wherein the smaller the characterization J is, the larger the fit value is
② Leading bee calculation
Setting leading bees to search new honey sources
Wherein,Representing random numbers in [ -1,1], i, j epsilon {1,2, … NS }, i not equal to j, when the fitness function of the new honey source is better than that of the old honey source, enabling the new honey source to replace the old honey source according to greedy principle;
③ Following bee calculation
Calculating the following probability
Setting a following bee to select a leading bee in a roulette manner, namely generating a random number which is uniformly distributed in [0,1], if P i is larger than the random number, generating a new honey source, and judging whether to reserve the honey source by utilizing the same calculation principle as that of the leading bee;
④ Calculation of scout bees
Judging whether the honey source meets the abandoned condition or not by the step ③; if yes, changing the corresponding leading bee role into a reconnaissance bee, searching a new solution in a specified solution domain, otherwise, directly transferring to a step ⑤;
⑤ Iteration of the loop
Judging whether the cycle times are greater than a preset maximum value, if so, stopping the population optimization iteration, or if the change of J is less than 1e-8, stopping the population optimization iteration; if none of them is satisfied, the population continues to be optimized.
3. A method for managing intelligent air traffic unmanned aerial vehicle, which is applied to the intelligent air traffic unmanned aerial vehicle route management system as claimed in any one of claims 1-2, and comprises the following steps:
a public pipeline space is marked up in the urban overhead space, and the marked public pipeline space comprises: a run space, a protected space, and an emergency disposal space;
when the unmanned aerial vehicle runs in the route channel without crossing, the route running management is carried out through the set route interval, the emergency running program and the priority configuration;
when the flight frame has higher secondary density and has the route crossing, a multi-machine artificial potential field and artificial bee colony optimization conflict resolution method is adopted to carry out route crossing operation management;
The operation space is marked by adopting a nine-grid operation mode;
the set emergency running program comprises the following steps:
① When an emergency happens in the air route, the air route deviates from the air route immediately to enter a protection space, enters the operation space again after the special situation is relieved, and if the operation cannot be recovered, the emergency treatment space is reserved for descent;
② One side avoidance rule is adopted in the channel in the route; the unmanned aerial vehicle newly enters the air route to avoid other unmanned aerial vehicles; the unmanned aerial vehicle in the route changes the flying height and needs to avoid other unmanned aerial vehicles; the unmanned aerial vehicle is positioned at the rear in the same direction in the route to avoid the front unmanned aerial vehicle; allowing the same-direction overrun flight in the route, and completing overrun by means of a protection space;
The adopted calculation method of the multi-machine artificial potential field comprises the following steps:
wherein, Representing the resultant force of the basic artificial repulsive force applied by the aircraft i; n j is the number of the planes j generating artificial repulsive force to the plane i; /(I)Representing a basic manual repulsive force; f i represents the fundamental resultant force experienced by aircraft i; /(I)Indicating that aircraft i is subject to power directed from the airline entrance to the airline exit; η represents the rejection gain; d represents the distance between aircraft i and aircraft j; d_min represents the minimum value of the rejection distance; d_max represents the maximum value of the repulsive distance; a_max represents the maximum repulsive force value.
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