CN110728857A - Low-altitude isolation airspace traffic management method based on vertically-taking-off and landing unmanned aerial vehicle - Google Patents

Low-altitude isolation airspace traffic management method based on vertically-taking-off and landing unmanned aerial vehicle Download PDF

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CN110728857A
CN110728857A CN201911003132.9A CN201911003132A CN110728857A CN 110728857 A CN110728857 A CN 110728857A CN 201911003132 A CN201911003132 A CN 201911003132A CN 110728857 A CN110728857 A CN 110728857A
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全权
李梦芯
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Flying Bull Intelligent Technology (nanjing) Co Ltd
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    • G08G5/0069—Navigation or guidance aids for a single aircraft specially adapted for an unmanned aircraft
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    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
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    • G—PHYSICS
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Abstract

A low-altitude isolation airspace traffic management method based on a vertically taking-off and landing unmanned aerial vehicle comprises plan auditing and taking-off authorization of the unmanned aerial vehicle before taking off, and conflict detection and flow control of the unmanned aerial vehicle in flight. The method comprises the steps of carrying out plan audit and takeoff authorization on the unmanned aerial vehicle before takeoff, establishing an estimation function, modeling a key problem of the plan audit, approximately solving an algorithm and carrying out takeoff authorization on the unmanned aerial vehicle before takeoff. And performing conflict detection and flow control on the unmanned aerial vehicle in flight, wherein the conflict detection and the flow control comprise a conflict detection, and a key problem modeling and approximate solving algorithm of the flow control. The invention ensures that the unmanned aerial vehicles keep the spacing distance, the waiting time is shortest, and the flight meets the assumption of traffic regulations. The method is beneficial to the safe, rapid and orderly operation of air traffic in a low-altitude isolation airspace, and further improves the intelligent and economic effects.

Description

Low-altitude isolation airspace traffic management method based on vertically-taking-off and landing unmanned aerial vehicle
Technical Field
The invention belongs to the technical field of unmanned aerial vehicles, and particularly relates to a low-altitude isolation airspace traffic management method based on a vertical take-off and landing unmanned aerial vehicle.
Background
Unmanned aerial vehicle is called unmanned aerial vehicle for short, as emerging scientific and technological products, has obtained rapid development in recent years, and unmanned aerial vehicle not only obtains large-scale application in fields such as fire control, patrolling and examining, agriculture, commodity circulation, etc., also is accepted by common people gradually, like company such as big jiang, zero degree, Hao Xiang, 3DR have gradually promoted numerous consumption level products.
However, more and more unmanned aerial vehicles also bring numerous problems, and as national laws and regulations are not perfect, owners of unmanned aerial vehicles are not familiar with relevant requirements, and most unmanned aerial vehicles actually fly in a black flight state. Unmanned aerial vehicles bring unprecedented air threats to important station facilities such as civil aviation airports, military facilities, petroleum and petrochemical enterprises, nuclear power stations and the like, and the problem to be solved by the nation is gradually mentioned for flight supervision of unmanned aerial vehicles.
And as more and more unmanned aerial vehicles fly at low-altitude, the unmanned aerial vehicles at low-altitude are more and more intensive, and the collision risk of the unmanned aerial vehicles becomes larger and larger. The unmanned aerial vehicle unified management in will flying lets unmanned aerial vehicle fly in order, will be an effective way of solving this problem, and this is called low latitude unmanned machine traffic management. The low-altitude unmanned aerial vehicle traffic management is to evaluate the operation safety and risks of the whole low-altitude unmanned aerial vehicle, and meanwhile, the low-altitude unmanned aerial vehicle is effectively planned, so that reasonable and orderly flight of the low-altitude unmanned aerial vehicle is guaranteed, and the healthy development of the unmanned aerial vehicle industry is promoted.
For a typical civil aircraft, the Air Traffic Management (ATM) generic goal is: effectively maintains and promotes air traffic safety, maintains air traffic order and ensures smooth air traffic. The air traffic management comprises three major parts, namely air traffic service, air traffic flow management and airspace management.
Air traffic service means that an air traffic control unit should provide air traffic service for civil aircraft in flight, including: air traffic control services, flight intelligence services, and alert services. Air Traffic Flow Management (ATFM) refers to a service that is set up to facilitate safe, orderly, and rapid circulation of air traffic to ensure maximum utilization of the capacity of air traffic control services and to comply with standards and capacities promulgated by the relevant air traffic service authorities. The airspace management refers to management work for maintaining national security, considering civil and military aviation needs and public interests, planning uniformly, and reasonably, fully and effectively utilizing airspaces. The airspace is generally divided into airport flight airspace, air routes, air restricted areas, air danger areas and the like. In order to meet the requirements of airspace management and flight tasks, an air corridor, an air oil release area and a temporary flight airspace can be planned. Important factors for determining the quality of traffic management, traffic management and air traffic control services are the load capacity and traffic proficiency of ground controllers, etc.
However, the existing air traffic management for civil aircraft cannot be adapted to millions of future drones. This is because: (1) the number of the low-altitude unmanned machines is large, the volume is small, and the execution tasks are complex and various; (2) the CNS technical means of the existing civil aviation air traffic management are difficult to be applied to low-altitude targets, and the effective detection and tracking of unmanned aerial vehicles are difficult; (3) unmanned aerial vehicle lacks effectual information acquisition means, is difficult to comprehensive, timely perception and avoids the barrier, leads to the risk increase of air collision. In addition, the existing air traffic management is still a management mode developed in the 30 s of the 20 th century, and a technical approach for assisting a pilot to drive is followed; and the driver of the unmanned aerial vehicle is on the ground, and aiming at low-altitude unmanned aerial vehicle traffic management, the existing Internet of things, an information technology and the like need to be combined.
Therefore, the invention mainly focuses on unmanned aerial vehicle traffic management in low-altitude airspace. The low-altitude airspace here refers to an airspace below 1000m (inclusive) in principle, the ultra-low-altitude airspace refers to an airspace below 120m (inclusive) in principle, and the invention focuses on an airspace below 300m (inclusive) in true height. According to the statistics of cloud data of unmanned aerial vehicles in the first quarter 2019 of the China civil aviation administration: unmanned aerial vehicles with the running height of below 120 meters account for 96.5 percent, and unmanned aerial vehicles with the running height of below 1000 meters account for 99.9 percent. Therefore, unmanned aerial vehicle traffic management in low-altitude airspace covers most unmanned aerial vehicles.
In addition, due to the characteristics of low cost, convenience in taking off and landing, strong maneuverability and the like, the unmanned aerial vehicle capable of taking off and landing vertically is widely applied to the practical fields of logistics transportation, cruise monitoring, agricultural plant protection and the like in a low-altitude airspace. However, as low-altitude unmanned traffic flow continues to grow, black flies and even crash events that affect civil economy and safety continue to occur. Aiming at a low-altitude isolation airspace, a traffic management method based on a vertical take-off and landing unmanned aerial vehicle is designed, so that the economic effect can be improved, and the safe, ordered and rapid operation of air traffic is facilitated. Therefore, it is very important and meaningful to research a low-altitude isolated airspace traffic management method based on a vertically taking-off and landing unmanned aerial vehicle.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a low-altitude isolation airspace traffic management method based on a vertical take-off and landing unmanned aerial vehicle. The aim is as follows: considering a series of flight processes, airport and airspace capacity limitations and geo-fence limitations, the flight plan (takeoff time, waypoints and the like) of each unmanned aerial vehicle is set before flight, the speed of each unmanned aerial vehicle is controlled during flight, and the like, so that the given economic benefit and the like are guaranteed to be the highest on the premise of flight safety.
In order to achieve the purpose, the invention adopts the following technical scheme:
a low-altitude isolation airspace traffic management method based on a vertical take-off and landing unmanned aerial vehicle is characterized by comprising the following steps:
the method comprises the following steps: plan auditing and takeoff authorization are carried out on the unmanned aerial vehicle before takeoff, and the method specifically comprises the following steps:
step 1.1: establishing an estimation function of the state of the unmanned aerial vehicle;
step 1.2: establishing a key problem model for plan auditing based on a pre-estimation function;
step 1.3: approximately solving a key problem model of plan auditing, determining whether the flight plan of the unmanned aerial vehicle passes or not, and outputting takeoff time and route points if the flight plan of the unmanned aerial vehicle passes;
step 1.4: carrying out take-off authorization on the unmanned aerial vehicle before take-off;
step two: carrying out collision detection and flow control on the unmanned aerial vehicle in flight, specifically as follows:
step 2.1: predicting whether the unmanned aerial vehicle passing the takeoff authorization conflicts, and if so, outputting the pair of the unmanned aerial vehicles most likely to conflict and predicting conflict time;
step 2.2: establishing a key problem model of flow control according to the result of the conflict detection;
step 2.3: and (4) approximately solving a key problem model of flow control, determining whether the conflict is solved, and if the conflict is solved, outputting a new nominal speed of the unmanned aerial vehicle on the current route.
In order to optimize the technical scheme, the specific measures adopted further comprise:
further, the setting of the vertical take-off and landing unmanned aerial vehicle flying in the road network is as follows:
1) abstracting a navigation network into a set of directed graphs G ═ (V, E, A);
2) in the navigation network, the jth intersection node is njThe coordinates are
Figure BDA0002240814880000031
j ═ 1, 2, …, M; total M intersection nodes, denoted as V ═ n1，n2，...，nM}; wherein the distance between any two nodes is
Figure BDA0002240814880000032
3) For the straight-line route existing between the nodes, the straight-line route is represented by E in graph theory; airway (n)i，nj) E fixed capacity ofThe number of the unmanned aerial vehicles can be accommodated; the unmanned aerial vehicle flies linearly on the air path;
4) there are N unmanned aerial vehicles that can take off and land perpendicularly, and known ith unmanned aerial vehicle's flying site Ni，1Point of descent
Figure BDA0002240814880000034
Figure BDA0002240814880000034
1, 2, N, wherein the number of waypoints of the ith unmanned aerial vehicle is Mi(ii) a The acceptable execution time period of the ith unmanned aerial vehicle is known as ti，apts，ti，apte]A safety distance of raPriority is priorityi；
5) On the way (n)i，nj) The unmanned aerial vehicle belonging to E has the nominal speed according to the route
Figure BDA0002240814880000035
Flying, further defining the adjacency matrix a elements of the directed graph G as follows:
Figure BDA0002240814880000036
6) the ground flying point and the node right above the ground appear in pairs.
Further, step 1.1 is specifically as follows:
establishing a predictor function P (t)0，i，tpre，UiAnd A) predicting the position p of the ith unmanned aerial vehicle at the predicted timei(tpre) And state Si(tpre) E { power off, wait for authorization, flight, other }, where t0，iIndicating the takeoff time, t, of the ith unmanned aerial vehiclepreIndicates the estimated time, Ui＝Ui(pi，vi，hi，hv，i，ni，cur，Si) Plan information, p, representing the ith unmanned aerial vehiclei，viRespectively representing the current position and speed of the ith unmanned aerial vehicle,
Figure BDA0002240814880000041
indicating the waypoint of the ith drone,
Figure BDA0002240814880000042
representing a sequence of nominal speeds between waypoints of the ith drone, ni，curRepresenting the current target waypoint, S, of the ith unmanned aerial vehicleiThe current state of the ith unmanned aerial vehicle is represented;
firstly, calculating the time length from the current position to the current target waypoint of the ith unmanned aerial vehicle as
Figure BDA0002240814880000043
Wherein the content of the first and second substances,
Figure BDA0002240814880000044
representing a target waypoint ni，curIn the position of (a) in the first,
Figure BDA0002240814880000045
indicating a target waypoint n on the ith unmanned aerial vehiclei，cur-1And a current target waypoint ni，curA nominal velocity in between;
the time required from the current position to the next waypoint is
Figure BDA0002240814880000046
Let T equal TpreT, t represents the current moment, and the position expression of the estimated ith unmanned aerial vehicle is obtained by analogy
Wherein the content of the first and second substances,indicates the Mth unmanned plane of the ith frameiA position of an individual waypoint;
similarly, the state expression of the estimated ith unmanned aerial vehicle is
Figure BDA0002240814880000049
Further, step 1.2 is specifically as follows:
at present, a new unmanned aerial vehicle i needs to enter a navigation network and is based on a prediction function P (t)0，i，tpre，UiA), the following optimization problem is established:
Figure BDA0002240814880000051
s.t
||pi(t0，i+s)-pk(t0，i+s)||≥2ra，s∈(0，Ti)，k∈Uactive(6)
Figure BDA0002240814880000052
ti，apts≤t0，i≤ti，apte-Ti(8)
Figure BDA0002240814880000053
equation (5) is an optimization objective, and the takeoff time of the ith unmanned aerial vehicle is expected to be earliest;
equation (6) is a conflict constraint, the conflict being determined by whether the distance between drones is less than a safe distance raJudging; for safety, the ith unmanned aerial vehicle does not conflict with any unmanned aerial vehicle waiting for takeoff authorization and passing takeoff authorization at the current moment in the flight process at any moment, and the judgment distance is limited to be more than or equal to 2r in predictiona；UactiveA set of drones that indicate that the current time has passed the takeoff authorization;
equation (7) is the capacity constraint, the fixed capacity is defined by the safety radius raThe distance between the node and the node is determined; in the flight process of the ith unmanned aerial vehicle, the number of the unmanned aerial vehicles on the airway at any moment is less than or equal to the fixed capacity of the airway;
Figure BDA0002240814880000054
indicates that the ith unmanned plane is at tpreWhether or not it is on the way (n)i，nj) In the above, thenIndicates that the k-th unmanned plane is at t0，iWhether the time + s is located at the last target waypoint ni，cur-1And a current target waypoint ni，curBetween the air routes (n)i，cur-1，ni，cur) The above step (1);
Figure BDA0002240814880000056
represents ni，cur-1And ni，curThe distance between them;
Figure BDA0002240814880000057
indicates the ith waypoint n of the unmanned planei，lAnd the l +1 th waypoint ni，l+1Fixed capacity of the inter-route;
the formula (8) is a takeoff time constraint, the takeoff time of the ith unmanned aerial vehicle should be greater than or equal to the starting time of the acceptable execution time period, and the sum of the takeoff time and the time required by the flight complete journey should be less than or equal to the ending time of the acceptable execution time period, that is, the ith unmanned aerial vehicle is ensured to fly in the whole flight process within the acceptable execution time period [ t [ [ t ]i，apts，ti，apte]Internal;
equation (9) is the electrical quantity constraint; the time required by the ith unmanned aerial vehicle to fly to the full range is divided into the distance between the distributed waypoints and the corresponding nominal speed hv，iDetermining that the maximum duration of the unmanned aerial vehicle is less than the maximum duration of the ith unmanned aerial vehicle;
Figure BDA0002240814880000058
represents ni，lAnd ni，l+1The distance between them;
Figure BDA0002240814880000061
represents ni，lAnd ni，l+1A nominal velocity in between;
furthermore, for the optimized result t0，iTime-out time constraint as follows
t0，i-t＜Twait(10)
Wherein, TwaitRepresents a timeout time; when t is0，i-T is greater than or equal to TwaitIn time, the I-th unmanned aerial vehicle still needs to wait for a long time to take off, and T is recommendedwaitAnd resubmit the application after the time length.
Further, step 1.3 is specifically as follows:
s1: obtaining the safety distance r of the newly added unmanned aerial vehicle iaAnd overall planning information Ui；
S2: updating the navigation network information A and the current time t, and obtaining the waypoint h of the unmanned aerial vehicle through the Dijkstra algorithmiAnd calculating the time T required for the flight completioni；
S3: estimate s ∈ (0, T)i) Position information p ofi(t0，i+ s), solving the optimization problem represented by equation (5-9);
s4: if yes, directly executing S5;
if the unmanned aerial vehicle has no solution and the reason is the conflict problem, judging the priority of all the unmanned aerial vehicles which conflict with the unmanned aerial vehiclek，k∈Ui，conllisionWhether all are smaller than self, Ui，conllisionRepresenting a set of drones predicted to collide with the ith drone; if yes, refusing the application of the unmanned aerial vehicle k and then executing S3 again; otherwise, refusing the application of the unmanned aerial vehicle i, waiting for TwaitRe-executing S2 after the duration;
if no solution exists and the reason is the capacity problem, temporarily setting the fixed capacity of the route corresponding to the super capacity to be 0, and then executing S2 again;
all other cases suggest TwaitRe-executing S2 after the duration;
s5: if the overtime constraint condition of the formula (10) is met, outputting the route point and the takeoff time of the unmanned aerial vehicle; otherwise T is suggestedwaitAnd re-executes S2 after the time period.
Further, step 2.1 is specifically as follows:
defining a distance between an ith unmanned aerial vehicle and a jth unmanned aerial vehicle
Figure BDA0002240814880000062
Wherein, Tmax> 0 denotes the estimated time, Uactive(s) set of drones, p, representing that s has been authorized by takeoffi(s)、pj(s) respectively showing the positions of the ith unmanned aerial vehicle and the jth unmanned aerial vehicle at the moment s;
if d < raThen a conflict may occur; if r isa＜d＜2raAlarming and prompting; otherwise, indicating safety; in the event of a conflict, the system will,the pair of drones most likely to collide at present is output and the predicted collision time.
Further, step 2.2 is specifically as follows:
and carrying out traffic scheduling according to the result of the conflict detection, and establishing the following mathematical model:
Figure BDA0002240814880000071
s.t
||pi(s)-pj(s)||≥ra，s∈(t，t+Tmax)，j∈Uactive(s) (13)
Figure BDA0002240814880000072
t0，i+Ti≤ti，apte(15)
Figure BDA0002240814880000073
Figure BDA0002240814880000074
equation (12) is an optimization objective, expecting to adjust the speed between waypoints of conflicting drones within a minimum range;
Figure BDA0002240814880000075
the nominal speed of the ith unmanned aerial vehicle after traffic scheduling is represented;
equation (13) is a collision constraint, and the ith drone estimates time T from the current timemaxAny time in the unmanned aerial vehicle conflicts with any unmanned aerial vehicle waiting for the takeoff authorization and passing the takeoff authorization at the current time;
equation (14) is a capacity constraint, estimating the time T from the current time on the ith dronemaxAt any time, the number of the unmanned aerial vehicles on the route is less than or equal to the fixed capacity of the route;
equation (15) is a deadline constraint, and the sum of the takeoff time of the ith unmanned aerial vehicle and the time required for the flight to complete the flight should be less than or equal to the deadline of the acceptable execution time period;
and the formula (16) is electric quantity constraint, wherein the time when the ith unmanned aerial vehicle flies, the time from the current position to the target waypoint and the time from the arrival of the target waypoint to the end of the flight are respectively represented by t-t0，i，
Figure BDA0002240814880000076
Represents;
equation (17) is the velocity constraint, vi，minAnd vi，maxRespectively representing the minimum speed and the maximum speed of the ith unmanned aerial vehicle.
Further, step 2.3 is specifically as follows:
s10: obtaining an estimated time TmaxAnd a safety distance ra；
S20: updating the information A of the airspace navigation network, the current time t, and all the information U of the unmanned aerial vehicles which pass the takeoff authorizationi，i∈Uactive(ii) a Carrying out conflict detection on all unmanned aerial vehicles authorized to take off in the airspace, and outputting the unmanned aerial vehicle U with conflict if the unmanned aerial vehicles with conflict existcollisionAnd possible collision times; otherwise, executing S50;
s30: solving the optimization problem shown in the formula (12-17);
s40: if the solution exists, outputting the new speed of the unmanned aerial vehicle on the current route
Figure BDA0002240814880000081
Then, S50 is executed;
if no solution exists, starting an anti-collision algorithm of the unmanned aerial vehicle, and executing S20 after the conflict is solved;
s50: interval TmaxAfter the time period, S20 is repeatedly executed once.
The invention has the beneficial effects that: the distance between unmanned aerial vehicles is kept, the waiting time is shortest, and the flight meets the traffic standard assumption. The method is beneficial to the safe, rapid and orderly operation of air traffic in a low-altitude isolation airspace, and further improves the intelligent and economic effects. The problem of low-altitude orderly flight of a large number of unmanned aerial vehicles is systematically solved in a hierarchical manner, unmanned aerial vehicle conflict and collision are avoided to the greatest extent, and relevant loss and casualties are greatly reduced.
Drawings
FIG. 1: schematic illustration of a navigation network.
FIG. 2: a closed loop block diagram of a traffic management system.
FIG. 3: schematic diagram of unmanned aerial vehicle flight in the road network.
FIG. 4: a traffic management simulation platform interface diagram.
FIG. 5: and (5) a traffic management simulation verification result diagram.
FIG. 6: schematic diagram of unmanned aerial vehicle traffic management mode.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings.
The final objective of low-altitude Unmanned aerial vehicle Traffic Management (UAS) Traffic Management (UTM) is to promote Unmanned aerial vehicles with different capabilities to orderly fly in a low-altitude airspace based on different geographical conditions (from rural areas to cities) and different application purposes (from air surveillance, facility inspection to logistics, etc.) and further expand to manned aircrafts, etc. More particularly, to maintain safe separation of drones from other airborne flying objects (e.g., drones, manned vehicles, self-contained balloons, airships, etc.) and obstacles, and to provide an efficient and orderly traffic flow control and capacity management. The low-altitude unmanned aerial vehicle traffic management system is a complex system, and the safety level required by the operation of the unmanned aerial vehicle is required to be ensured according to the performance of the unmanned aerial vehicle. The content of the unmanned aerial vehicle comprises the whole life cycle process from production and manufacturing to flying use, product maintenance and the like, and relates to relevant systems of unmanned aerial vehicles, drivers, operators, manufacturing enterprises and the like. The low-altitude unmanned traffic control is not a control of a controller on a driver, more traffic control functions are automatically processed by a background system, and the low-altitude unmanned air space management and the capacity management are deeply integrated with real-time low-altitude traffic flow control, collision detection and the like.
The invention designs an unmanned aerial vehicle traffic management method architecture, which comprises four layers: strategic management, tactical management, edge management, individual management. They exhibit a progressive relationship as shown in fig. 6. Wherein strategic management is achieved through navigation network planning; tactical management is realized by flight plan declaration, air route planning and take-off authorization and flow control; edge management is achieved through collision detection and avoidance; the individual management is realized by the obstacle avoidance capability of the unmanned aerial vehicle.
First, strategy Management (Strategic Management): based on geographic information, communication navigation monitoring capability and the like, a navigation network is designed and planned, the problem of airspace management is solved, and system capacity (maximum capacity) is obtained.
The strategic management method mainly comprises the steps of establishing flight order, wherein a unified representation method, such as gridding, is firstly provided for an airspace; secondly, designing and planning a navigation network based on the representation method.
(1) Spatial domain representation: gridding
At present, the general research for constructing the airspace environment adopts a Grid method (Grid) to establish the low-altitude unmanned aerial vehicle low-altitude flight airspace environment. The low-altitude airspace is divided into a plurality of three-dimensional grids in geographic space, and the purpose is to use the central point of a grid block as a sampling point of the airspace to perform any calculation, simulation, measurement and analysis, and use the grid block to perform color coding visualization, so that the whole urban airspace can be managed by a system in a discrete mode. Although all of the lattice blocks are assumed to have the same size, the sizes of the individual lattice blocks in latitude, longitude, and altitude are not necessarily the same. They may vary according to different analytical models, requirements, capacities, constraints, etc. A typical gridding airspace representation example is the Beidou satellite navigation grid code in China at present, and the importance of the gridding code in the aviation field is self-evident. The airspace is represented by gridding, and the airspace maps three-dimensional Geographic Information (GIS) to grids, so that different precision ranges can be provided for different industry applications, and information leakage of national resources is avoided.
(2) Spatial domain organization: navigation network
The spatial domain may be further structured based on the gridded representation. Firstly, dividing an airspace environment into grid blocks, and then according to the space blocks contained in grids: waypoints (communication points, airports, temporary landing areas, landing waiting areas, etc.), restricted areas, severe weather areas, airport clearance protection areas and communication, navigation and monitoring capabilities, divide the grid into obstacle and free grids. Therefore, the airspace environment is composed of the free grids and the obstacle grids, and a connected graph is formed, so that the air route planning problem is converted into a planning problem of the free grids, namely, an optimal path for avoiding obstacles from the starting grid to the terminal grid is searched on the connected graph.
Thus, the structure of the airspace is similar to that of a road network of a city. The drone can only enter three areas: an airway functioning similarly to a road, an intersection formed by at least two airways, and a node. The air route is an airspace with a certain width for the airplane to make a flight route according to the ground navigation facilities. The airspace has an upper limit height and a lower limit height and a width defined by taking a straight line connecting the navigation facilities as a center line. The node here may be an airport or a piece of free flight airspace.
Second, Tactical Management (Tactical Management): and (4) evaluating the possible operation capacity in a future period of time by combining with the flight plan declaration, completing the examination and approval of the flight plan and solving the capacity management before operation. The problem of flow control is solved in the cruising process, the orderly flight of the unmanned aerial vehicle is realized, the maximization of the instant operation capacity is realized, and the occurrence of conflict is prevented. In the cruising process, the collision problem which may occur is solved by combining a dynamic electronic fence, temporary traffic control and the like. At this point, if an anomaly occurs, it would be a better option to operate the drone by a ground controller (maintainer) rather than a driver.
Tactical management is to control and schedule long-period and slow-speed traffic of unmanned aerial vehicles on the basis of a road network, and the interval frequency is usually more than a second level. The method mainly comprises three scenes of before flight, in flight and after flight. The pre-flight and post-flight can also be grouped into one type, collectively referred to as ground scenes.
(1) Before flight (Pre-flight phase), the problems of flight plan declaration, air route planning and takeoff authorization are solved.
Plan declaration: by combining with the flight plan declaration, estimating the semi-static capacity of the whole air network in a long period of time in the future, and examining, approving and authorizing the flight plan;
planning a route: planning a more suitable flight route based on the air network according to the taking-off and landing place of the flight task, the flight risk requirement, the time requirement, other airplane flight tasks and the like;
and (3) taking-off authorization: during the takeoff phase (considering the airport), the request for flying is authorized by calculating the current operating capacity.
(2) In flight (In-flight phase), various problems of flow management, collision detection and avoidance, landing and the like In flight are solved.
And (3) cruising: calculating the current airport or air route running capacity or conflict condition in real time by combining abnormal conditions, traffic control, abnormal weather, obstacles, other unmanned aerial vehicles and the like, and triggering flow control; flow control, namely: adjusting the running speed, air routes and the like of the aircraft by combining the collision risk, the running capacity, the air traffic control and the like; also understood as collision detection and avoidance services.
A descending stage: and by calculating the operation capacity of the region, the unmanned aerial vehicle in the airport region can be reliably navigated and orderly landed.
(3) Post-flight (Post-flight phase), analysis and recording aspects, and other related Post-flight business and obligations, insurance and charging, maintenance, and the like.
Third, Edge Management (Edge Management): the method solves the regional (sight distance range, point-to-point communication distance between the unmanned aerial vehicle and the remote controller) problems, such as the taking-off and landing (approach) management related to the airport, and mainly solves the problems of taking-off authorization, capacity control, taking-off and landing and the like in the airport region. Because the driver of the unmanned aerial vehicle is on the ground, it is unrealistic to keep a high reliable command and control Link (C2 Link) in the whole flight process, especially during over-the-horizon flight, so that the unmanned aerial vehicle driver is deployed in an airport area, if an abnormal condition occurs, the unmanned aerial vehicle is locally operated to take off and land by the ground driver, and the reliability and time delay requirements of the C2 Link are greatly higher than those of the cruise process.
The edge management mainly solves the problem of collision risk in a regional (sight distance range, point-to-point communication distance between the unmanned aerial vehicle and the remote controller) range through a quick scheduling means. The area is mainly the area where the unmanned aerial vehicles are more dense, such as an unmanned aerial vehicle airport. Here not only rely on unmanned aerial vehicle to report data, also can combine radar, the camera data of airport to acquire the state that unmanned aerial vehicle was located fast, can adopt marginal calculation technique to unmanned aerial vehicle at this moment, and ground management and control system keeps away barrier control in real time to the aircraft, gives the supervision or the control command of the below frequency of interval second level, and the dispatch guides past, take off and land unmanned aerial vehicle and avoids bumping, provides collision detection and avoids the service.
Fourthly, Individual Management (industrial Management): the problem of collision of the obstacles is solved through the onboard processor, and the unmanned aerial vehicle, the unmanned aerial vehicle and the obstacles adopt an effective onboard path planning technology, bypass the obstacles and broadcast related information.
According to the unmanned aerial vehicle traffic management method framework, the invention provides a low-altitude isolated airspace traffic management method based on a vertically taking-off and landing unmanned aerial vehicle, which comprises plan audit and take-off authorization of the unmanned aerial vehicle before taking-off, and conflict detection and flow control of the unmanned aerial vehicle in flight.
The traffic management method of the invention is based on the unmanned aerial vehicle capable of vertically taking off and landing flying in the air network shown in figure 1 aiming at the low-altitude isolation airspace. The navigation network comprises intersection nodes, airports, flow corridors with different speed limits and the like. Like the automobile driving on the road, the unmanned aerial vehicle flying in the road network complies with the traffic rules. For convenience of expression, we make some assumptions and mathematical descriptions:
(1) the navigation network can be abstracted into a set of directed graphs G ═ V, E, a.
(2) In the navigation network, the jth intersection node is njThe coordinates are
Figure BDA0002240814880000111
j is 1, 2. Total M intersection nodes, denoted as V ═ n1，n2，...，nM}. Wherein the distance between any two nodes is
(3) And a straight-line route exists between some nodes and is represented by E in graph theory. Airway (n)i，nj) E fixed capacity of
Figure BDA0002240814880000113
Can hold the unmanned aerial vehicle number. Suppose that the drone is flying in a straight line and must be on the way.
(4) There are N unmanned aerial vehicles that can hang down, and the flying point N of known ith unmanned aerial vehiclei，1Point of descent
Figure BDA0002240814880000114
Figure BDA0002240814880000114
1, 2, N, wherein the number of waypoints of the ith unmanned aerial vehicle is Mi. The acceptable execution time period of the ith unmanned aerial vehicle is known as ti，apts，ti，apte]A safety distance of raPriority is priorityi(the larger the default value the higher the priority).
(5) On the way (n)i，nj) The unmanned aerial vehicle belonging to E needs to be at the nominal speed of the route
Figure BDA0002240814880000115
And (5) flying. The adjacency matrix a elements of the directed graph G are further defined as follows:
Figure BDA0002240814880000121
(6) the ground flying point and the node right above the ground appear in pairs.
The overall flow chart of the invention is shown in fig. 2, and the specific implementation steps are as follows:
step 1: and carrying out plan audit and takeoff authorization on the unmanned aerial vehicle before takeoff.
1.1, establishing an estimation function
According to requirements, we define a series of parameters as shown in table 1:
TABLE 1 parameters used in the mathematical model
Figure BDA0002240814880000122
Figure BDA0002240814880000131
Whether unmanned aerial vehicles conflict or not and the number of the unmanned aerial vehicles in the navigation network all depend on the position information of the unmanned aerial vehicles. Therefore, it is necessary to first establish a predictor P (t)0，i，tpre，UiAnd A) predicting the position p of the ith unmanned aerial vehicle at the predicted timei(tpre) And state Si(tpre) E { power off, wait for authorization, flight, other }. The present position of the drone, as shown in fig. 3, can be calculated by the flight path.
Firstly, calculating the time length from the current position to the current target waypoint of the ith unmanned aerial vehicle as
Figure BDA0002240814880000132
The time from the current position to the next waypoint is
Figure BDA0002240814880000133
Let T equal TpreT, and the position expression of the estimated ith unmanned aerial vehicle can be obtained by analogy
Figure BDA0002240814880000134
The same principle can be obtained, and the state expression of the estimated ith unmanned aerial vehicle is
Figure BDA0002240814880000135
1.2 planning audit Key problem modeling
Now, a new unmanned plane i needs to enter the navigation network based on the above prediction function P (t)0，i，tpre，UiA), we can establish the following optimization problem:
s.t
||pi(t0，i+s)-pk(t0，i+s)||≥2ra，s∈(0，Ti)，k∈Uactive(6)
Figure BDA0002240814880000141
ti，apts≤t0，i≤ti，apte-Ti(8)
Figure BDA0002240814880000142
equation (5) is an optimization objective, and the takeoff time of the ith unmanned aerial vehicle is expected to be earliest.
Equation (6) is a conflict constraint, the conflict being determined by whether the distance between drones is less than a safe distance raAnd (4) judging. For safety, the ith unmanned aerial vehicle does not conflict with any unmanned aerial vehicle waiting for takeoff authorization and having passed the takeoff authorization at the current moment in the flight process at any moment, and the judgment distance is strictly limited to be more than or equal to 2r in predictiona。
Equation (7) is a capacity constraint. The fixed capacity is defined by a safety radius raThe distance to the node. In the flight process of the ith unmanned aerial vehicle, the number of the unmanned aerial vehicles on the airway at any moment is less than or equal to the fixed capacity of the airwayAmount of the compound (A). Wherein the content of the first and second substances,
Figure BDA0002240814880000143
equation (8) is the takeoff time constraint. The takeoff time of the ith unmanned aerial vehicle is more than or equal to the starting time of the acceptable execution time period, and meanwhile, the sum of the takeoff time and the time required by the flight complete journey is less than or equal to the cut-off time of the acceptable execution time period, namely, the ith unmanned aerial vehicle is ensured to fly in the whole flight process in the acceptable execution time period [ t [ [ t ]i，apts，ti，apte]And (4) the following steps.
Equation (9) is the electrical quantity constraint. The time required by the ith unmanned aerial vehicle to fly to the full range is divided into the distance between the distributed waypoints and the corresponding nominal speed hv，iIt is decided that it needs less than the longest endurance of the ith drone.
Furthermore, it is noted that the entire calculation is based on the predictor function P (t)0，i，tpre，UiA), there are many uncertainties and the time for the drone to wait for takeoff is too long. Therefore, we optimize the result t0，iTime-out time constraint as follows
t0，i-t＜Twait(10)
When t is0，i-T is greater than or equal to TwaitIn time, the I-th unmanned aerial vehicle still needs to wait for a long time to take off, and T is recommendedwaitAnd resubmit the application after the time length.
1.3 approximate solution Algorithm
Inputting: airspace navigation network information A, current time t, and safety distance r of newly-added unmanned aerial vehicle iaAnd overall planning information Ui。
And (3) outputting: whether the flight plan of the unmanned aerial vehicle i passes or not, and if the flight plan passes, the takeoff time t needs to be output0，iAnd waypoints hi。
The plan review algorithm is shown in table 2:
TABLE 2 plan review Algorithm
Figure BDA0002240814880000151
1.4, carrying out takeoff authorization on the unmanned aerial vehicle before takeoff
Approach takeoff time t0，iIn the first 2 minutes, the system again plans the information U of the unmanned aerial vehicleiConfirmation is performed. The takeoff authorization is basically consistent with the judgment content of plan audit, and the secondary confirmation is performed when the takeoff time is close to, so that the detailed development is not performed.
Step 2: collision detection and flow control for unmanned aerial vehicle in flight
2.1, Collision detection
During the flight of the unmanned aerial vehicle, the actual flight may not be consistent with the expectation due to various uncertain factors. The collision detection is mainly used for predicting whether the unmanned aerial vehicle passing the takeoff authorization collides. We define the distance
Figure BDA0002240814880000161
Wherein T ismax> 0 denotes the estimated time.
If d < raThen a conflict may occur. If r isa＜d＜2raIndicating an alarm prompt. Otherwise, it indicates security. If conflict occurs, the conflict detection module outputs the pair of the unmanned aerial vehicles which are most likely to conflict at present and the predicted conflict time.
2.2 flow control Key problem modeling
And according to the result of the conflict detection, the traffic scheduling module performs small-range adjustment. We can build the following mathematical model:
Figure BDA0002240814880000162
s.t
||pi(s)-pj(s)||≥ra，s∈(t，t+Tmax)，j∈Uactive(s) (13)
Figure BDA0002240814880000163
t0，i+Ti≤ti，apte(15)
Figure BDA0002240814880000164
Figure BDA0002240814880000165
equation (12) is an optimization objective, with the desire to adjust the speed between waypoints of conflicting drones within a minimum range.
Equation (13) is a collision constraint. The ith unmanned aerial vehicle estimates time T from the current momentmaxAny time in the unmanned aerial vehicle conflicts with any unmanned aerial vehicle waiting for the takeoff authorization and passing the takeoff authorization at the current time.
Equation (14) is a capacity constraint. Estimating time T from the current moment at the ith unmanned aerial vehiclemaxAt any time, the number of the unmanned aerial vehicles on the route is less than or equal to the fixed capacity of the route. Wherein the content of the first and second substances,
Figure BDA0002240814880000166
equation (15) is the cutoff constraint. The sum of the takeoff time of the ith unmanned aerial vehicle and the time required for the flight to complete the flight should be less than or equal to the cut-off time of the acceptable execution time period.
Equation (16) is the charge constraint. The time when the ith unmanned aerial vehicle flies, the time from the current position to the target waypoint and the time from the arrival of the target waypoint to the completion of the flight are respectively from t-t0，i，And (4) showing.
Equation (17) is the speed constraint. v. ofi，minAnd vi，maxRespectively representing the minimum speed and the maximum speed of the ith unmanned aerial vehicle.
2.3 approximate solution Algorithm
Inputting: airspace navigation network information A, current time T and estimated time TmaxAll unmanned aerial vehicle information U authorized by takeoffi，i∈UactiveAnd a safety distance ra。
And (3) outputting: whether the conflict is solved or not, if the conflict is solved, the new nominal speed of the unmanned aerial vehicle on the current route is output
Figure BDA0002240814880000172
The algorithm for collision detection and traffic scheduling is shown in table 3:
TABLE 3 Conflict detection and traffic scheduling Algorithm
The invention is explained below by means of simulation and calculation processes. The simulation and calculation process is carried out on a computer with main frequency of 3.70Ghz and internal memory of 32.0GB and MATLAB R2018b under a Win10 professional version operating system.
(1) The present invention is implemented in a traffic management simulation platform as shown in fig. 4. The method comprises the following specific steps:
the method comprises the following steps: and carrying out plan audit and takeoff authorization on the unmanned aerial vehicle before takeoff.
Firstly, importing the navigation network information G ═ V, E and A in a parameter input frame of a GUI (graphical user interface), and setting relevant parameters of the unmanned aerial vehicle, including the number N of unmanned aerial vehicles and the flying starting point N of each unmanned aerial vehiclei，1Point of descent
Figure BDA0002240814880000174
Acceptable execution time period ti，apts，ti，apte]A safe distance raAnd priorityi. Wherein, i is 1, 2.
Then clicking a START button of a GUI (graphical user interface), and automatically calculating the takeoff time t of each unmanned aerial vehicle by the system according to the algorithm provided in the table 20，iAnd waypoints hiAnd the results are displayed in the flight log at the bottom right of the simulation results as shown in fig. 5.
Step two: and carrying out collision detection and flow control on the unmanned aerial vehicle in flight.
In the whole simulation process, the system schedules the aerial unmanned aerial vehicle according to the algorithm provided in table 3. Firstly, collision detection is carried out on the unmanned aerial vehicle which passes the takeoff authorization according to the formula (11), and collision information is displayed in a flight log at the lower right of a simulation result shown in fig. 5.
Once a conflict is predicted to occur, it is prioritized whether the conflict can be resolved by changing the speed according to the optimization problem represented by the formula (1217). If so, outputting the new nominal speed of the unmanned aerial vehicle on the current route
Figure BDA0002240814880000181
And the results are displayed in the flight log at the lower right of the simulation results as shown in fig. 5; if not, then send early warning information to unmanned aerial vehicle, solve by unmanned aerial vehicle's anticollision module by oneself.
(2) Analysis of results
The simulation results are shown in fig. 5. As can be seen from the real-time information feedback table of the unmanned aerial vehicles below, all unmanned aerial vehicles can successfully start from the flying point and reach the landing point through the navigation network. The correctness of the system for scheduling methods such as speed adjustment, air route adjustment and the like of the unmanned aerial vehicle can be seen in real time from the flight log at the lower right. As can be seen from the upper safety interval diagram, the distances among all unmanned aerial vehicles are kept at the safe distance r in the whole flight processaAnd (c) other than. The traffic management method can ensure that the current unmanned aerial vehicle can always keep enough safety distance with other unmanned aerial vehicles and can pass through as fast as possible while the unmanned aerial vehicle completes the self task, and the traffic management method is proved to be feasible.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.

Claims (8)

1. A low-altitude isolation airspace traffic management method based on a vertical take-off and landing unmanned aerial vehicle is characterized by comprising the following steps:
the method comprises the following steps: plan auditing and takeoff authorization are carried out on the unmanned aerial vehicle before takeoff, and the method specifically comprises the following steps:
step 1.1: establishing an estimation function of the state of the unmanned aerial vehicle;
step 1.2: establishing a key problem model for plan auditing based on a pre-estimation function;
step 1.3: approximately solving a key problem model of plan auditing, determining whether the flight plan of the unmanned aerial vehicle passes or not, and outputting takeoff time and route points if the flight plan of the unmanned aerial vehicle passes;
step 1.4: carrying out take-off authorization on the unmanned aerial vehicle before take-off;
step two: carrying out collision detection and flow control on the unmanned aerial vehicle in flight, specifically as follows:
step 2.1: predicting whether the unmanned aerial vehicle passing the takeoff authorization conflicts, and if so, outputting the pair of the unmanned aerial vehicles most likely to conflict and predicting conflict time;
step 2.2: establishing a key problem model of flow control according to the result of the conflict detection;
step 2.3: and (4) approximately solving a key problem model of flow control, determining whether the conflict is solved, and if the conflict is solved, outputting a new nominal speed of the unmanned aerial vehicle on the current route.
2. The low-altitude isolation airspace traffic management method based on the VTOL unmanned aerial vehicle of claim 1, characterized in that: the setting of the vertical take-off and landing unmanned aerial vehicle flying in the road network is as follows:
1) abstracting a navigation network into a set of directed graphs G ═ (V, E, A);
2) in the navigation network, the jth intersection node is nj, and the coordinate is
Figure FDA0002240814870000011
A total of M crossesSink node, denoted as V ═ n1，n2，...，nM}; wherein the distance between any two nodes is
3) For the straight-line route existing between the nodes, the straight-line route is represented by E in graph theory; airway (n)i，nj) E fixed capacity of
Figure FDA0002240814870000013
The number of the unmanned aerial vehicles can be accommodated; the unmanned aerial vehicle flies linearly on the air path;
4) there are N unmanned aerial vehicles that can take off and land perpendicularly, and known ith unmanned aerial vehicle's flying site Ni，1Point of descent
Figure FDA0002240814870000014
Wherein the number of waypoints of the ith unmanned aerial vehicle is Mi(ii) a The acceptable execution time period of the ith unmanned aerial vehicle is known as ti，apts，ti，apte]A safety distance of raPriority is priorityi；
5) On the way (n)i，nj) The unmanned aerial vehicle belonging to E has the nominal speed according to the route
Figure FDA0002240814870000015
Flying, further defining the adjacency matrix a elements of the directed graph G as follows:
Figure FDA0002240814870000021
6) the ground flying point and the node right above the ground appear in pairs.
3. The low-altitude isolation airspace traffic management method based on the VTOL unmanned aerial vehicle of claim 2, characterized in that: step 1.1 is specifically as follows:
establishing a predictor functionP(t0，i，tpre，UiAnd A) predicting the position p of the ith unmanned aerial vehicle at the predicted timei(tpre) And state Si(tpre) E { power off, wait for authorization, flight, other }, where t0，iIndicating the takeoff time, t, of the ith unmanned aerial vehiclepreIndicates the estimated time, Ui＝Ui(pi，vi，hi，hv，i，ni，cur，Si) Plan information, p, representing the ith unmanned aerial vehiclei，viRespectively representing the current position and speed of the ith unmanned aerial vehicle,
Figure FDA0002240814870000022
indicating the waypoint of the ith drone,
Figure FDA0002240814870000023
representing a sequence of nominal speeds between waypoints of the ith drone, ni，curRepresenting the current target waypoint, S, of the ith unmanned aerial vehicleiThe current state of the ith unmanned aerial vehicle is represented;
firstly, calculating the time length from the current position to the current target waypoint of the ith unmanned aerial vehicle as
Figure FDA0002240814870000024
Wherein the content of the first and second substances,
Figure FDA0002240814870000025
representing a target waypoint ni，curIn the position of (a) in the first,
Figure FDA0002240814870000026
indicating a target waypoint n on the ith unmanned aerial vehiclei，cur-1And a current target waypoint ni，curA nominal velocity in between;
the time required from the current position to the next waypoint is
Figure FDA0002240814870000027
Let T equal TpreT, t represents the current moment, and the position expression of the estimated ith unmanned aerial vehicle is obtained by analogy
Figure FDA0002240814870000028
Wherein the content of the first and second substances,
Figure FDA0002240814870000031
indicates the Mth unmanned plane of the ith frameiA position of an individual waypoint;
similarly, the state expression of the estimated ith unmanned aerial vehicle is
4. The low-altitude isolation airspace traffic management method based on the VTOL unmanned aerial vehicle of claim 3, characterized in that: step 1.2 is specifically as follows:
at present, a new unmanned aerial vehicle i needs to enter a navigation network and is based on a prediction function P (t)0，i，tpre，UiA), the following optimization problem is established:
Figure FDA0002240814870000033
s.t
||pi(t0，i+s)-pk(t0，i+s)||≥2ra，s∈(0，Ti)，k∈Uactive(6)
Figure FDA0002240814870000034
ti，apts≤t0，i≤ti，apte-Ti(8)
Figure FDA0002240814870000035
equation (5) is an optimization objective, and the takeoff time of the ith unmanned aerial vehicle is expected to be earliest;
equation (6) is a conflict constraint, the conflict being determined by whether the distance between drones is less than a safe distance raJudging; for safety, the ith unmanned aerial vehicle does not conflict with any unmanned aerial vehicle waiting for takeoff authorization and passing takeoff authorization at the current moment in the flight process at any moment, and the judgment distance is limited to be more than or equal to 2r in predictiona；UactiveA set of drones that indicate that the current time has passed the takeoff authorization;
equation (7) is the capacity constraint, the fixed capacity is defined by the safety radius raThe distance between the node and the node is determined; in the flight process of the ith unmanned aerial vehicle, the number of the unmanned aerial vehicles on the airway at any moment is less than or equal to the fixed capacity of the airway;
Figure FDA0002240814870000036
indicates that the ith unmanned plane is at tpreWhether or not it is on the way (n)i，nj) In the above, thenIndicates that the k-th unmanned plane is at t0，iWhether the time + s is located at the last target waypoint ni，cur-1And a current target waypoint ni，curBetween the air routes (n)i，cur-1，ni，cur) The above step (1);represents ni，cur-1And ni，curThe distance between them;
Figure FDA0002240814870000042
indicates the ith waypoint n of the unmanned planei，lAnd the l +1 th waypoint ni，l+1Fixed capacity of the inter-route;
the formula (8) is a takeoff time constraint, the takeoff time of the ith unmanned aerial vehicle should be greater than or equal to the starting time of the acceptable execution time period, and the sum of the takeoff time and the time required by the flight complete journey should be less than or equal to the ending time of the acceptable execution time period, that is, the ith unmanned aerial vehicle is ensured to fly in the whole flight process within the acceptable execution time period [ t [ [ t ]i，apts，ti，apte]Internal;
equation (9) is the electrical quantity constraint; the time required by the ith unmanned aerial vehicle to fly to the full range is divided into the distance between the distributed waypoints and the corresponding nominal speed hv，iDetermining that the maximum duration of the unmanned aerial vehicle is less than the maximum duration of the ith unmanned aerial vehicle;
Figure FDA0002240814870000043
represents ni，lAnd ni，l+1The distance between them;represents ni，lAnd ni，l+1A nominal velocity in between;
furthermore, for the optimized result t0，iTime-out time constraint as follows
t0，i-t＜Twait(10)
Wherein, TwaitRepresents a timeout time; when t is0，i-T is greater than or equal to TwaitIn time, the I-th unmanned aerial vehicle still needs to wait for a long time to take off, and T is recommendedwaitAnd resubmit the application after the time length.
5. The low-altitude isolation airspace traffic management method based on the VTOL unmanned aerial vehicle of claim 4, characterized in that: step 1.3 is specifically as follows:
s1: obtaining the safety distance r of the newly added unmanned aerial vehicle iaAnd overall planning information Ui；
S2: updating the navigation network information A and the current time t through a Dijkstra algorithmObtaining an unmanned aerial vehicle waypoint hiAnd calculating the time T required for the flight completioni；
S3: estimate s ∈ (0, T)i) Position information p ofi(t0，i+ s), solving the optimization problem represented by equation (5-9);
s4: if yes, directly executing S5;
if the unmanned aerial vehicle has no solution and the reason is the conflict problem, judging the priority of all the unmanned aerial vehicles which conflict with the unmanned aerial vehiclek，k∈Ui，conllisionWhether all are less than self, Ui，conllisionRepresenting a set of drones predicted to collide with the ith drone; if yes, refusing the application of the unmanned aerial vehicle k and then executing S3 again; otherwise, refusing the application of the unmanned aerial vehicle i, waiting for TwaitRe-executing S2 after the duration;
if the reason for no solution is the capacity problem, temporarily setting the fixed capacity of the route corresponding to the super capacity to be 0, and then executing S2 again;
all other cases suggest TwaitRe-executing S2 after the duration;
s5: if the overtime constraint condition of the formula (10) is met, outputting the route point and the takeoff time of the unmanned aerial vehicle; otherwise T is suggestedwaitAnd re-executes S2 after the time period.
6. The low-altitude isolation airspace traffic management method based on the VTOL unmanned aerial vehicle of claim 5, characterized in that: step 2.1 is specifically as follows:
defining a distance between an ith unmanned aerial vehicle and a jth unmanned aerial vehicle
Figure FDA0002240814870000051
Wherein, Tmax> 0 denotes the estimated time, Uactive(s) set of drones, p, representing that s has been authorized by takeoffi(s)、pj(s) respectively showing the positions of the ith unmanned aerial vehicle and the jth unmanned aerial vehicle at the moment s;
if d < raThen a conflict may occur; if r isa＜d＜2raAlarming and prompting; otherwise, indicating safety; and if the conflict occurs, outputting the pair of the unmanned aerial vehicles which are most likely to conflict at present and predicting the conflict time.
7. The low-altitude isolation airspace traffic management method based on the VTOL unmanned aerial vehicle of claim 6, characterized in that: step 2.2 is specifically as follows:
and carrying out traffic scheduling according to the result of the conflict detection, and establishing the following mathematical model:
Figure FDA0002240814870000052
s.i
||pi(s)-pj(s)||≥ra，s∈(t，t+Tmax)，j∈Uactive(s) (13)
Figure FDA0002240814870000053
t0，i+Ti≤ti，apte(15)
Figure FDA0002240814870000054
Figure FDA0002240814870000055
equation (12) is an optimization objective, expecting to adjust the speed between waypoints of conflicting drones within a minimum range;
Figure FDA0002240814870000056
the nominal speed of the ith unmanned aerial vehicle after traffic scheduling is represented;
equation (13) is a collision constraint, and the ith drone estimates time T from the current timemaxAny time in the system is not waiting for the takeoff authorization or passing the takeoff authorization at any time different from the current timeThe unmanned aerial vehicle is in conflict;
equation (14) is a capacity constraint, estimating the time T from the current time on the ith dronemaxAt any time, the number of the unmanned aerial vehicles on the route is less than or equal to the fixed capacity of the route;
equation (15) is a deadline constraint, and the sum of the takeoff time of the ith unmanned aerial vehicle and the time required for the flight to complete the flight should be less than or equal to the deadline of the acceptable execution time period;
and the formula (16) is electric quantity constraint, wherein the time when the ith unmanned aerial vehicle flies, the time from the current position to the target waypoint and the time from the arrival of the target waypoint to the end of the flight are respectively represented by t-t0，i，
Figure FDA0002240814870000061
Represents;
equation (17) is the velocity constraint, vi，minAnd vi，maxRespectively representing the minimum speed and the maximum speed of the ith unmanned aerial vehicle.
8. The low-altitude isolation airspace traffic management method based on the VTOL unmanned aerial vehicle of claim 7, characterized in that: step 2.3 is specifically as follows:
s10: obtaining an estimated time TmaxAnd a safety distance ra；
S20: updating the information A of the airspace navigation network, the current time t, and all the information U of the unmanned aerial vehicles which pass the takeoff authorizationi，i∈Uactive(ii) a Carrying out conflict detection on all unmanned aerial vehicles authorized to take off in the airspace, and outputting the unmanned aerial vehicle U with conflict if the unmanned aerial vehicles with conflict existcollisionAnd possible collision times; otherwise, executing S50;
s30: solving the optimization problem shown in the formula (12-17);
s40: if the solution exists, outputting the new speed of the unmanned aerial vehicle on the current routeThen, S50 is executed;
if no solution exists, starting an anti-collision algorithm of the unmanned aerial vehicle, and executing S20 after the conflict is solved;
s50: interval TmaxAfter the time period, S20 is repeatedly executed once.
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CN113310491A (en) * 2021-05-17 2021-08-27 北京航空航天大学 Unmanned aerial vehicle navigation network automatic generation method considering specific structure
CN113570910A (en) * 2021-06-30 2021-10-29 北京百度网讯科技有限公司 Air traffic flow management method and device and electronic equipment
CN113627742A (en) * 2021-07-21 2021-11-09 齐鲁空天信息研究院 Aircraft airspace capacity evaluation method
CN114355978A (en) * 2022-01-06 2022-04-15 北京大唐永盛科技发展有限公司 Unmanned aerial vehicle low-altitude flight management method
WO2022140924A1 (en) * 2020-12-28 2022-07-07 深圳市大疆创新科技有限公司 Control method, control system, unmanned aerial vehicle, and interactive device
CN114743408A (en) * 2022-04-18 2022-07-12 北京大唐永盛科技发展有限公司 Low-altitude flight management system based on meshing
WO2022233179A1 (en) * 2021-05-07 2022-11-10 北京三快在线科技有限公司 Landing control of unmanned aerial vehicle
CN116959296A (en) * 2023-08-28 2023-10-27 中国民航管理干部学院 Aircraft flight conflict detection method, device and system

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105117792A (en) * 2015-08-06 2015-12-02 北京航空航天大学 Flight airport scene operation optimization method considering runway port waiting time
CN105259916A (en) * 2015-10-30 2016-01-20 杨珊珊 Scheduling apparatus of unmanned aircraft and scheduling method of scheduling apparatus
WO2017044079A1 (en) * 2015-09-09 2017-03-16 Thomson Licensing Method and apparatus for allocation of air space for unmanned aerial vehicles
CN107818396A (en) * 2016-09-13 2018-03-20 泰勒斯公司 For changing the decision assistant of flight plan
CN108304958A (en) * 2017-12-08 2018-07-20 中国科学院空间应用工程与技术中心 A kind of list star multi-load Complex Constraints mission planning method and system
CN108513560A (en) * 2016-12-23 2018-09-07 瑞典爱立信有限公司 Unmanned vehicle in control zone
US10332405B2 (en) * 2013-12-19 2019-06-25 The United States Of America As Represented By The Administrator Of Nasa Unmanned aircraft systems traffic management

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10332405B2 (en) * 2013-12-19 2019-06-25 The United States Of America As Represented By The Administrator Of Nasa Unmanned aircraft systems traffic management
CN105117792A (en) * 2015-08-06 2015-12-02 北京航空航天大学 Flight airport scene operation optimization method considering runway port waiting time
WO2017044079A1 (en) * 2015-09-09 2017-03-16 Thomson Licensing Method and apparatus for allocation of air space for unmanned aerial vehicles
CN105259916A (en) * 2015-10-30 2016-01-20 杨珊珊 Scheduling apparatus of unmanned aircraft and scheduling method of scheduling apparatus
CN107818396A (en) * 2016-09-13 2018-03-20 泰勒斯公司 For changing the decision assistant of flight plan
CN108513560A (en) * 2016-12-23 2018-09-07 瑞典爱立信有限公司 Unmanned vehicle in control zone
CN108304958A (en) * 2017-12-08 2018-07-20 中国科学院空间应用工程与技术中心 A kind of list star multi-load Complex Constraints mission planning method and system

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
QUAN QUAN: "Iterative Learning Control and Initial Value Estimation for Probe-Drogue Autonomous Aerial Refueling of UAVs", 《AEROSPACE ENCE AND TECHNOLOGY》 *
RAYMOND YOUNG: "Advances in UAS Ground-Based Detect and Avoid Capability", 《2019 INTEGRATED COMMUNICATIONS, NAVIGATION AND SURVEILLANCE CONFERENCE (ICNS)》 *
于拓: "面向四旋翼飞行器的室内定位系统", 《中国优秀硕士学位论文全文数据库信息科技辑》 *
张兆宁: "远端绕行滑行道的机场滑行路径优化", 《科学技术与工程》 *
柳丹: "基于改进蚁群算法的多无人机航路规划研究与应用", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》 *

Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111477035B (en) * 2020-04-03 2021-05-28 飞牛智能科技（南京）有限公司 Low-altitude navigation network geometric structure generation method oriented to safety distance constraint
CN111477035A (en) * 2020-04-03 2020-07-31 飞牛智能科技（南京）有限公司 Low-altitude navigation network geometric structure generation method oriented to safety distance constraint
CN112198896B (en) * 2020-09-11 2022-05-27 中国航空工业集团公司成都飞机设计研究所 Unmanned aerial vehicle multi-mode electronic fence autonomous flight method
CN112198896A (en) * 2020-09-11 2021-01-08 中国航空工业集团公司成都飞机设计研究所 Unmanned aerial vehicle multi-mode electronic fence autonomous flight method
WO2022140924A1 (en) * 2020-12-28 2022-07-07 深圳市大疆创新科技有限公司 Control method, control system, unmanned aerial vehicle, and interactive device
WO2022233179A1 (en) * 2021-05-07 2022-11-10 北京三快在线科技有限公司 Landing control of unmanned aerial vehicle
CN113310491A (en) * 2021-05-17 2021-08-27 北京航空航天大学 Unmanned aerial vehicle navigation network automatic generation method considering specific structure
CN113222277A (en) * 2021-05-27 2021-08-06 南京航空航天大学 Urban area logistics unmanned aerial vehicle demand prediction method based on dynamic allocation algorithm
CN113570910B (en) * 2021-06-30 2022-09-27 北京百度网讯科技有限公司 Air traffic flow management method and device and electronic equipment
CN113570910A (en) * 2021-06-30 2021-10-29 北京百度网讯科技有限公司 Air traffic flow management method and device and electronic equipment
CN113627742A (en) * 2021-07-21 2021-11-09 齐鲁空天信息研究院 Aircraft airspace capacity evaluation method
CN114355978A (en) * 2022-01-06 2022-04-15 北京大唐永盛科技发展有限公司 Unmanned aerial vehicle low-altitude flight management method
CN114743408A (en) * 2022-04-18 2022-07-12 北京大唐永盛科技发展有限公司 Low-altitude flight management system based on meshing
CN116959296A (en) * 2023-08-28 2023-10-27 中国民航管理干部学院 Aircraft flight conflict detection method, device and system

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