CN110597276B - Remote planning method for unmanned aerial vehicle aerial safety corridor path - Google Patents
Remote planning method for unmanned aerial vehicle aerial safety corridor path Download PDFInfo
- Publication number
- CN110597276B CN110597276B CN201910504188.6A CN201910504188A CN110597276B CN 110597276 B CN110597276 B CN 110597276B CN 201910504188 A CN201910504188 A CN 201910504188A CN 110597276 B CN110597276 B CN 110597276B
- Authority
- CN
- China
- Prior art keywords
- planning
- unmanned aerial
- aerial vehicle
- path
- flight
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000007726 management method Methods 0.000 claims abstract description 10
- 230000007613 environmental effect Effects 0.000 claims abstract description 5
- 230000008569 process Effects 0.000 claims abstract description 5
- 238000004422 calculation algorithm Methods 0.000 claims description 26
- 241000257303 Hymenoptera Species 0.000 claims description 13
- 230000000694 effects Effects 0.000 claims description 9
- 230000008859 change Effects 0.000 claims description 6
- 230000006835 compression Effects 0.000 claims description 6
- 238000007906 compression Methods 0.000 claims description 6
- 230000006870 function Effects 0.000 claims description 5
- 230000033228 biological regulation Effects 0.000 claims description 4
- 238000012937 correction Methods 0.000 claims description 3
- 238000004891 communication Methods 0.000 claims description 2
- RZVHIXYEVGDQDX-UHFFFAOYSA-N 9,10-anthraquinone Chemical compound C1=CC=C2C(=O)C3=CC=CC=C3C(=O)C2=C1 RZVHIXYEVGDQDX-UHFFFAOYSA-N 0.000 abstract description 5
- 239000003016 pheromone Substances 0.000 description 16
- 238000010586 diagram Methods 0.000 description 15
- 230000000007 visual effect Effects 0.000 description 7
- 238000004458 analytical method Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 230000006872 improvement Effects 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 238000010276 construction Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000002265 prevention Effects 0.000 description 2
- 230000003321 amplification Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 239000004615 ingredient Substances 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 239000003208 petroleum Substances 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000010845 search algorithm Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 239000013598 vector Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Traffic Control Systems (AREA)
Abstract
A remote planning method for an air safety corridor path of an unmanned aerial vehicle provides path planning service in the air safety corridor for a plurality of unmanned aerial vehicles on the premise of avoiding potential safety hazards to ground facilities, air traffic management, social order and citizen life. Firstly, receiving a service application of an unmanned aerial vehicle user; secondly, establishing a three-dimensional task space model based on a plan airspace range; thirdly, considering the terrain features, the safety constraints of ground facilities and crowds, and the air traffic management requirements, establishing an air safety corridor; then, further considering the influence of environmental changes such as attributes, weather and the like of the user aircraft, performing off-line path planning service in the air safety corridor before flight, and providing basis for the user to declare a formal flight plan to a flight control department; and finally, in the actual flight process, combining the actual changes of the aircraft state and the surrounding environment, and performing dynamic path planning service on line.
Description
Technical Field
The invention relates to the field of unmanned aerial vehicles, in particular to a remote planning method and a controller for an unmanned aerial vehicle aerial safety corridor path and an unmanned aerial vehicle.
Background
The low-altitude airspace is absolutely imperative due to the huge economic power, meanwhile, the low-speed small moving targets such as unmanned aircrafts and the like also bring great hidden dangers to ground facilities, social order and the safety of citizens, and the effective technical approach for solving the contradiction is found to be urgent. Ground remote planning of the path in the safe corridor should be emphasized more for drones than for ground vehicles.
In recent years, the low-altitude slow-speed small aircraft has a well blowout development trend. Civil unmanned aerial vehicles are wide in demand (such as logistics transportation, agricultural plant protection, forest fire prevention, power inspection, petroleum pipeline inspection, terrorism prevention and disaster relief, geological exploration and ocean remote sensing), and have huge potential market space.
However, due to the low flying height of the aircraft in the low-altitude airspace, the safety of urban ground population, major activities and building facilities is seriously threatened. How to support and promote the development of unmanned aerial vehicles from the technical means on the premise of meeting air traffic safety? Safe flight is the biggest challenge facing unmanned aerial vehicles.
Disclosure of Invention
Technical problem to be solved
The invention aims to: on the basis of avoiding potential safety hazards possibly brought by ground facilities, air traffic management, social order and citizen life, an air safety corridor is established for the unmanned aerial vehicle of the user in the remote service center, and a flight path in the safety corridor is planned for the unmanned aerial vehicle.
Remote planning is necessary because most unmanned aircraft users generally require a great deal of expense to obtain constraint beliefs for a portion of the path, and even to obtain exact constraint information directly.
(II) technical scheme
The embodiment of the invention provides a remote planning method for an unmanned aerial vehicle aerial safety corridor path, which comprises the following steps:
receiving a service application of an unmanned aerial vehicle user based on a remote planning service center;
establishing a three-dimensional task space model based on an airspace range according to the service application;
establishing an air safety corridor based on the three-dimensional task space model by combining terrain features, safety constraints of ground facilities and crowds and air traffic management requirements;
according to the attribute and environmental change of the user aircraft, performing off-line path planning service in the air safety corridor before flight, and providing a basis for the user to declare a formal flight plan and airspace to a flight control department;
and in the flight process, performing dynamic path planning service on line according to the state of the aircraft and the change of the surrounding environment.
In some embodiments of the invention, the service application comprises a flight planning request for the unmanned aerial vehicle, the flight planning request comprising basic attribute parameters, performance parameters, a flight origin, a destination and a location parameter where the unmanned aerial vehicle has to pass.
In some embodiments of the present invention, building a three-dimensional task space model based on a spatial domain according to the service application comprises:
taking the vertex of the lower left corner of the three-dimensional map as a coordinate origin A of a three-dimensional planning space, and establishing a three-dimensional planning coordinate system A-xyz, wherein the x axis points to the east along the longitude direction, the y axis points to the north along the latitude direction, the A-xy plane coincides with the sea level, and the z axis is vertical to the sea level and faces upwards;
taking a point A as a vertex in a planning coordinate system, taking the maximum warp length AA 'of a planning space along the x-axis direction, taking the maximum weft length AD' of the planning space along the y-axis direction, and taking the maximum altitude AB of the planning space along the z-axis direction to form a cubic area ABCD-A 'B' C 'D', namely a three-dimensional path planning task space;
extracting grid points required by path planning from the three-dimensional space by adopting a method of equally dividing the space, equally dividing the planning space n along the x axis to obtain (n + 1) planes pi i (i =1, …, n); equally dividing along the y-axis m; finally, the three-dimensional task space model is determined by equally dividing along the z-axis l.
In some embodiments of the present invention, establishing an air safety corridor based on the three-dimensional task space model in combination with terrain features, ground facility and crowd safety constraints, and air traffic management requirements, comprises:
acquiring terrain elevation data;
acquiring elevation data of ground facilities;
acquiring data of an air traffic control flight-limiting area;
acquiring geographic information of people with heavy and intensive activities;
and removing terrain elevation data, ground facility elevation data, air traffic control flight limiting area data and geographic information of people with heavy activities from the three-dimensional task space model, and determining the aerial safety corridor.
In some embodiments of the invention, the off-line service of path planning in the air safety corridor before flight, according to the attributes and environmental changes of the user aircraft, is based on performing according to a two-stage ant colony algorithm.
In some embodiments of the invention, the two-stage ant colony algorithm comprises:
the method comprises the steps of discretizing the whole search space into a series of three-dimensional discrete points based on three-dimensional path planning of an ant colony algorithm, wherein the discrete points are nodes needing to be searched by the ant colony algorithm;
selecting a forward movement distance L x,max Is two planes pi i ，Π i+1 Distance therebetween, when plane Π i In the case where the upper point P is moved 1km in the forward direction, the maximum allowable distance of lateral movement is set to L y,max Maximum allowable longitudinal movement distance of L z,max When ants come from point PWhen the position is advanced along the direction of the x axis, a visible area exists for searching the next point;
when the ants move from the current point to the next point, calculating the selection probability of each point in the visible area according to the heuristic function;
based on two-stage search method, compressing the original m x n grid number in horizontal plane into m 1 *n 1 (m 1 <m，n 1 <n) of nodes, or dividing the original m x n nodes into m 1 *n 1 And selecting the lower elevation limit of each node after compression as the maximum value of the lower elevation limit of the original node in each area.
In some embodiments of the present invention, during a flight, a dynamic path planning service is performed online according to changes in the state and the surrounding environment of an aircraft, including:
dynamic information is reported to a remote planning comprehensive supervision platform in real time according to the regulations, and a remote planning center timely performs necessary correction on a dynamic path of the dynamic information;
the actual trajectory is compared with the predicted trajectory, and the result of dynamic planning can be optimized.
An embodiment of the present invention further provides a controller, which communicates with the unmanned aerial vehicle, where the controller includes:
a memory for storing executable instructions;
and the processor is used for executing the remote planning method of the unmanned aerial vehicle air safety corridor path according to the executable instructions stored in the memory.
An embodiment of the present invention further provides an unmanned aerial vehicle, including:
a memory for storing executable instructions;
and the processor is used for executing the remote planning method of the unmanned aerial vehicle air safety corridor path according to the executable instructions stored in the memory.
(III) advantageous effects
Compared with the prior art, the unmanned aerial vehicle remote planning method, the controller and the unmanned aerial vehicle for the aerial safety corridor path have the advantages that:
1. on the basis of avoiding potential safety hazards possibly brought by ground facilities, air traffic management, social order and citizen life, an air safety corridor is established for the unmanned aerial vehicle of the user in the remote service center, and a flight path in the safety corridor is planned for the unmanned aerial vehicle, so that the potential safety hazards possibly brought by surrounding environment facilities and personnel of the unmanned aerial vehicle are avoided;
2. based on an improved ant colony algorithm, the safe air corridor path of the UAV is globally optimized, the algorithm can be further improved, and safety problems caused by the use of the UAV can be reduced or even avoided, so that social order and citizen life safety are maintained.
Drawings
Fig. 1 illustrates the necessity and role of remote planning.
Fig. 2 is a process explanatory diagram.
Fig. 3 is a typical system configuration.
FIG. 4 is a schematic view of the planned airspace in accordance with an embodiment.
Fig. 5 is a schematic diagram of a three-dimensional planning space in the form of a cube.
Fig. 6 is a topographical elevation map in accordance with an embodiment of the present invention.
Fig. 7 is a schematic diagram of effects of the elevation data of the construction facilities in the latitude and longitude areas.
Fig. 8 is a schematic view of a ground flight limiting zone.
Fig. 9 is a schematic diagram of visual area definition for ant colony search.
Fig. 10 is a diagram of the planning result of the light unmanned aerial vehicle.
Fig. 11 is a diagram of a planning result of the drone.
Fig. 12 is a horizontal in-plane grid division diagram for two-level planning.
Fig. 13 is a grid division diagram in a horizontal plane for a two-level plan.
Fig. 14 is a diagram of the primary path planning result.
Fig. 15 is a fitness chart of the primary path plan.
FIG. 16 is a diagram of expected flight patterns of two high-altitude balloon users at a location.
FIG. 17 is a diagram of the height distribution of the upper and lower limits of the zero wind zone of the five-year historical data of the ECMWF in a certain 7-8 months.
FIG. 18 is a diagram of flight trajectory prediction based on three different meteorological data sources.
FIG. 19 is a spatial domain application map based on offline planning recommendations.
Fig. 20 is a remote planning center online monitoring interface.
Detailed Description
In view of the defects of the prior art, the inventor considers that the aim of improving the safety of the unmanned aerial vehicle (unmanned aerial vehicle) is far from enough, and how to avoid the potential safety hazard of the unmanned aerial vehicle to surrounding environment facilities and personnel is needed in the first place. Therefore, the inventor provides a remote planning method for the air safety corridor path of the unmanned aerial vehicle, develops a targeted system and tries to apply the targeted system to actual flight mission service.
FIG. 1 is a diagram illustrating the method of the present invention, which is described in detail below:
(1) Receiving a service application from a user
As shown in fig. 2, a user may download an installation unmanned aerial vehicle app, a ground flight control unit app, and a mobile client app from a WebServices server through a network. When there is a specific flight mission requirement, the app of the user unmanned aerial vehicle or its ground flight control unit may make a planning request to the remote planning center, including providing basic attribute parameters of the unmanned aerial vehicle (for example, referring to basic regulations of different types of UAVs or possible modification requirements of the national air traffic control department), performance parameters (geometric size, maneuverability and mobility, etc.) that need to perform the mission; flight origin, destination and location parameters (longitude, latitude and altitude) where the flight must pass.
(2) Establishing three-dimensional task space model based on plan airspace range
The airspace required for a particular flight mission should be applied to the air management department within a specified time prior to the actual flight. The pre-planned airspace is established according to the user service application, and is generally a range surrounded by specific longitude and latitude, for example, an area surrounded by a red dotted line in fig. 4 is the airspace applied by a certain flight mission.
And then establishing a three-dimensional task space model comprising flight departure place, destination and position parameters (longitude, latitude and altitude) which must pass through in the latitude and longitude range specified by the plan airspace. If the latitude and longitude of the departure place, the destination or the place which must be passed exceeds the airspace, the user is required to change.
Without loss of generality, a three-dimensional planning space in the form of a cube as shown on the left side of fig. 5 can be established within the latitude and longitude range of the pre-planned airspace. Firstly, the vertex of the lower left corner of the three-dimensional map is used as a coordinate origin A of a three-dimensional planning space, a three-dimensional planning coordinate system A-xyz is established, wherein the x axis points to the east along the longitude direction, the y axis points to the north along the latitude direction, the A-xy plane is coincided with the sea plane, and the z axis is vertical to the sea plane and faces upwards.
And taking the point A as a vertex in the planning coordinate system, taking the maximum warp direction length AA 'of the planning space along the x-axis direction, taking the maximum weft direction length AD' of the planning space along the y-axis direction, and taking the maximum altitude AB of the planning space along the z-axis direction. Thus, a cubic region ABCD-A 'B' C 'D' is formed, which is a three-dimensional path planning task space.
And extracting grid points required by path planning from the three-dimensional space by adopting a method of equally dividing the space. Firstly, a planning space n is equally divided along an x axis to obtain (n + 1) planes II i (i = 1...., n), as shown on the right side of fig. 5; then equally dividing along a y axis m; finally equally divided along the z-axis l.
Through the steps, the planning space is discretized into a three-dimensional point set, and any point P in the set corresponds to two coordinates: first order number coordinate P 1 (i, j, k) (i =0,1,.. N; j =0,1,.. M; k =0,1.. 1); second is position coordinate P 2 (x i ，y j ，z k ). Wherein i, j, k are serial numbers of the current point divided along the axes x, y, z, respectively.
The longitude and latitude heights spanned by the memory cube are respectively
I AD | = Δ λ (degree), | AB | = h (degree), and (d | = Δ λ (degree))Meter), the position coordinate of the point P takes the value of
(3) Establishing an aerial safety corridor
(1) Topographic elevation data acquisition
The GIS enhanced information server provides a Digital Elevation Model (DEM) Digital Elevation constraint Model of the terrain in the task planning space, can extract from geographic information data, respectively represents longitude, latitude and altitude by column vectors x, y and z, and can draw a terrain Elevation map of the task space after analysis.
Fig. 6 is a latitude and longitude area [116.265:116.305,40.01:40.045] which is a 255 x 255=65025 grid composed of 256 x 256 nodes. Points a and b in the figure represent the unmanned aerial vehicle takeoff point and the target point, respectively.
(2) Ground facility elevation data acquisition
The elevation data of the ground facility needs to be acquired for a fee by a third party. On the basis of fig. 6, the effect of superimposing the elevation data of the construction facilities of the corresponding latitude and longitude areas is as shown in fig. 7, which includes the relevant houses, communication base stations, high-voltage electric lines, forests, etc., and the elevation protection information according to the degree of importance.
(3) Blank pipe flight limit zone data acquisition
According to the type of the unmanned aerial vehicle, the air traffic control information server obtains a flight-restricted area data model of a planned space through a civil aviation information service organization, for example, a micro unmanned aerial vehicle must meet 9 constraints of 50 meters in true height, an air restricted area, a range of 2000 meters around the micro unmanned aerial vehicle, and a light unmanned aerial vehicle must meet 11 constraints of 120 meters in true height, an air restricted area, a range of 5000 meters around the micro unmanned aerial vehicle, and the like.
Meanwhile, whether the task space has the civil aviation airline limit or not is verified, and dynamic time constraint of airlines is obtained.
In addition, the flight-limiting areas in the airspace range need to be avoided from latitude and longitude, and the shadow areas shown in fig. 8 are flight-limiting areas, and the aircraft must avoid entering the areas when landing.
(4) Acquisition of geographic information for crowd with dense activities of major importance
And acquiring the geographic information of the crowd with heavy activities through related department information and media channels, and establishing a constraint model similar to a flight-limiting area.
In summary, after the above constraint area is deducted from the task space, the air safety corridor can be established.
(4) Path planning offline before actual flight
And further considering the influence of environmental changes such as the attribute of the user aircraft, weather and the like, and performing off-line path planning service in the air safety corridor before flight. Various suitable methods that have been published in the literature may be applied specifically to the planning algorithm, and as an example, an improved two-stage ant colony algorithm is presented for illustration.
(1) Pheromone update
The ant colony algorithm uses pheromones to attract a satisfactory search. The method for setting and updating the pheromone at the node position has extremely important significance for the successful search of the ant colony algorithm.
The three-dimensional path planning based on the ant colony algorithm discretizes the whole search space into a series of three-dimensional discrete points, and the discrete points are nodes needing to be searched by the ant colony algorithm. Thus, the pheromones are stored in discrete points of the model, each discrete point having a pheromone value whose size represents the attraction to the ant. Each point pheromone is updated after each ant passes by.
The updating of the pheromone comprises two parts of local updating and global updating:
local renewal means that for each ant, as the ant passes the spot, the pheromone at the spot decreases. The method aims to increase the probability of searching the points which are not passed by the ants and achieve the aim of global searching. The local pheromone is updated along with the search of ants, and the updating formula is
τ ijk ＝(1-ξ)τ ijk (2)
Wherein, tau ijk Is a pointThe pheromone value and xi carried by (i, j, k) are local update attenuation coefficients of the pheromone.
Global update means that when an ant completes a search for a path, the length of the path is used as an evaluation value, and the shorter the length, the larger the pheromone. And selecting the shortest path from the path set, and increasing pheromone values of all nodes of the shortest path. The updated formula is as follows
τ ijk ＝(1-ρ)τ ijk +ρΔτ ijk (4)
Wherein, length m Is the path length that the mth ant passes through; ρ is the pheromone update coefficient.
(2) Visual search space
Assume that the x-axis direction is the main direction for ant colony search. In order to reduce the complexity of path planning, the movement of the unmanned aerial vehicle is simplified into three modes of forward movement, transverse movement and longitudinal movement.
Selecting a forward movement distance L x，max Is two planes pi i ，Π i+1 The distance between them. Working plane II i In the case where the upper point P is moved 1km in the forward direction, the maximum allowable distance of lateral movement is set to L y，max Maximum allowable longitudinal movement distance of L z，max . Thus, as the ant proceeds from point P along the x-axis, there is a visible area for the next point search.
FIG. 9 illustrates visual area definition for ant search
Thus, when the ants move from the current point to the next point, the searchable area is limited within the visual area searched by the ants, thereby simplifying the search space and improving the search efficiency.
(3) Ant colony search strategy
And when the ants move from the current point to the next point, calculating the selection probability of each point in the visible area according to the heuristic function. The general form of the heuristic function is:
wherein S (i, j, k) is a safety factor, and when the selection point is unreachable, the value is 0, so as to ensure that ants cannot reach dangerous positions; d (i, j, k) is the path length between the current point and the quasi-arrival point, and the ants are prompted to select positions with shorter distances; q (i, j, k) is the path length from the point to be reached to the target point, causing the ant to select the point closer to the target. w is a 1 ，w 2 w 3 Representing the importance of each of the above factors.
The specific calculation formulas are as follows:
where num represents the number of viewpoints in the points (i, j, k), and unum represents the number of points of an unreachable area in the viewpoints. For unmanned aerial vehicles, safety constraints come from two aspects: (1) Altitude constraints from terrain and ground facilities are embodied as the lower limit of the flying height; (2) All unmanned aerial vehicles must satisfy the constraint of the air traffic control no-flight zone, and in addition, the upper limit of the true height of 50m and 120m is embodied for the micro unmanned aerial vehicle and the light unmanned aerial vehicle respectively. For other drones, not only is the upper limit of 3000m true height embodied, but also a flight plan is to be prepared.
Wherein (x) p ，y p ，z p )，(x q ，y q ，z q )，(x d ，y d ，z d ) Respectively current point, next point, target pointAnd (4) coordinates.
Ant on plane II i Current point p of i Selecting plane II i+1 Next point p on i+1 The steps are as follows:
STEP 1: determining plane pi according to current environment of ant i+1 A set of feasible points within;
STEP 2: sequentially calculating point-to-plane pi according to heuristic function formula (5) i+1 Heuristic information values H (i +1, j, k) for the set of feasible points within;
STEP 3: calculating pi in plane i+1 Selection probability of any point (i +1, j, k) in the interior
Wherein tau is (i+1，j，k) Is a plane II i+1 Pheromone values of upper points P (i +1, j, k);
STEP 4: selecting plane pi by roulette according to selection probability of each point i+1 Inner point.
Based on the above analysis, the algorithm flow for the three-dimensional path planning problem is shown in fig. 9, where initialization includes setting parameters such as the number of ants, and the termination condition means that all ants complete the search.
And respectively carrying out path planning examples on the light unmanned aerial vehicle and the micro unmanned aerial vehicle by applying the basic algorithm.
Set at plane pi i+1 The visual search space above is 5 adjacent points across both the horizontal and elevation directions, thus a total of 25 search points. All points out of the 25 search points that do not satisfy the security constraint are considered infeasible, and the probability value in equation (9) is taken to be zero directly.
For local and global updates of the pheromone, the relevant coefficients are chosen as
ξ＝0.5，ρ＝0.5，K＝100. (10)
20 ants are adopted for 100 iterations, and fig. 10 and 11 show the planning results of the light unmanned aerial vehicle and the micro unmanned aerial vehicle respectively.
From the simulation results, the path of the drone is substantially along a straight line. But to miniature unmanned aerial vehicle's route, its projection on the horizontal plane has flown one section to the eastern direction earlier, and the main reason has two: firstly, if flying along a straight line, the contradiction between the safety constraint of ground high-rise building facilities and the constraint of 50m true height upper limit must be faced (while the contradiction is not existed in the case of light unmanned aerial vehicles, because the true height upper limit is 120 m); second, the algorithm searches in increments in the longitudinal direction.
(4) Two-stage search improvement method
Because the ant colony algorithm is a constructive random search algorithm, the running time of the algorithm is found to be long in simulation. For remote services, planning needs of multiple unmanned aerial vehicle users may be met at the same time, and operation efficiency is very important.
For this purpose, a two-stage search method may be used for planning. Specifically, the original number of m x n grids in the horizontal plane is compressed into m 1 *n 1 (m 1 <m，n 1 <n) of nodes, or dividing the original m x n nodes into m 1 *n 1 And selecting the lower elevation limit of each node after compression as the maximum value of the lower elevation limit of the original node in each area. Specifically, three types are included, as shown in fig. 12, where the boxes and small circles of e, d, and c respectively represent the regions and the nodes after compression. Only m is needed for the primary search 1 *n 1 Searching by taking the horizontal coordinate of each node as an independent variable, as shown in the left side of fig. 14, according to the departure point a and the target point b, a corresponding primary path can be obtained through searching; then, if necessary, a second search is performed on each of the neighboring primary paths, as shown by the area between d and e in the right diagram of fig. 13. Whether the second-level search is needed depends on the perception capability of the unmanned aerial vehicle such as the visual field and the like.
For the example herein, the original 256 × 256 trellis number may be compressed to 18 × 18 trellis number, with the reference compression code being
for i＝1：18
for j＝1：18
vaiable18(i，j)＝vaiable256(15*i-14，15*j-14)；
end
end
Fig. 14 and 15 show the path planning and its fitness result by the primary search only, respectively. Since the compressed mesh size is less than 250m × 200m, it is sufficient for the visual capabilities of many drones.
Some unmanned vehicles are sensitive to meteorological environments, and the tracks of the unmanned vehicles need to be predicted by offline path planning analysis combined with meteorological data, and the predicted tracks serve as necessary bases for declaring airspace plans. For example, for high-altitude balloons, wind fields are the absolute factor affecting trajectory, and offline planning essentially embodies trajectory prediction based on historical meteorological data. The service requirements of two high-altitude balloon users in a certain place are explained, and a diagram of expected flight modes of the high-altitude balloon users is shown in FIG. 16.
Because the wind field brings great uncertainty to the flight track of the high-altitude balloon, the flight scheme hopes that the wind speed is very low around 19 km above sea level and can be fully utilized. FIG. 17 is a graph of the height distribution of the upper and lower zero-windage limits of ECMWF (European Central for Medium-Range Weather means mid-European Weather forecast center) historical data for the first five months of the year.
To be more reliable, it should be possible to make offline planning predictions from multiple data sources, the remote planning service center may obtain Global weather data from multiple data sources such as europe, north american GFS (Global forecasting System in the united states), and chinese GRAPES (Global/regional assessment and Prediction Enhanced System). Fig. 18 shows the track of the high-altitude balloon B released at a certain time in 8 months of the year by forecasting through three different data sources.
Because a satisfactory predicted trajectory can be obtained according to various data sources, the offline planned path can be delivered to an aircraft user, and the user can declare a formal flight plan to a flight control department. The solid black area in fig. 19 is a spatial map suggesting the two high-altitude balloon user applications.
(5) In-flight on-line path planning
In the dynamic flight process, the user of the unmanned aerial vehicle reports dynamic information to the remote planning comprehensive supervision platform in real time according to the regulations, and the remote planning center timely performs necessary correction on the dynamic path of the unmanned aerial vehicle. On the basis of the off-line planning, other dynamic targets in the air and on the ground around the aircraft are avoided as much as possible, and some disclosed dynamic planning algorithms can be used for reference. The actual trajectory is compared with the predicted trajectory, and the result of the subsequent dynamic planning can be optimized.
FIG. 20 is a remote planning integrated supervisory platform real-time monitoring interface. The system can provide path planning implementation service for a plurality of users at the same time. The yellow area in the figure is a flight limiting area, and when the aircraft of the user is about to enter or enters the flight limiting area, the system can display prompt alarm information on an interface in real time to remind the user of paying attention. The system can also display historical tracks, real-time tracks and relative position information between two or more aircrafts in real time, so that a user can know the positions of the aircraft and adjacent aircrafts in time, and the user can make relevant decision arrangement.
The geographic information of the supervision platform system adopts an online map display mode, and supports operations of amplification, reduction and clicking. When the user clicks on the blue box of the aircraft, the coordinates of the aircraft may be displayed on the map in real time.
In the face of pressure brought to low-altitude airspace management and ground safety by rapid development of unmanned aerial vehicles, a basic architecture for performing remote planning service center based on WebGIS is provided, and the purpose is to perform remote planning service for unmanned aerial vehicles on the premise of ensuring ground facility safety; meanwhile, meteorological factors and flight conditions of other unmanned aerial vehicles in the air can be further considered, and enhanced planning service is provided for unmanned aerial vehicle users. Secondly, the ant colony algorithm is adopted to carry out path planning in the three-dimensional safety corridor, a primary improvement method of two-stage searching is provided aiming at the limitation that the basic ant colony algorithm is low in operation efficiency, and the 2 nd-stage searching is carried out only if the grid scale after compression is larger than the field size of the unmanned aerial vehicle. Simulation results show that the path planning requirements of the light unmanned aerial vehicle and the micro unmanned aerial vehicle can be met.
It should be noted that there are many problems to be further investigated in remote planning. The aim of remote planning is to fully utilize constraint information of low-altitude airspace and ground to provide a global safe path, and an appropriate method should be adopted for problem facing. However, the ant colony algorithm is a heuristic algorithm with randomness, and has a series of problems such as being easily limited to local optimum, so that other methods with global optimization need to be continuously explored. Meanwhile, research is required to be closely combined with online flight of the unmanned aerial vehicle, and dynamic planning service is provided by utilizing meteorological information and airspace flight situation information.
An embodiment of the present invention further provides a controller, which communicates with the unmanned aerial vehicle, where the controller includes:
a memory for storing executable instructions;
and the processor is used for executing the remote planning method of the unmanned aerial vehicle air safety corridor path according to the executable instructions stored in the memory.
An embodiment of the present invention further provides an unmanned aerial vehicle, including:
a memory for storing executable instructions;
and the processor is used for executing the remote planning method of the unmanned aerial vehicle air safety corridor path according to the executable instructions stored in the memory.
Due to the controller and the unmanned aerial vehicle, the advantages of the method for remotely planning the air safety corridor path of the unmanned aerial vehicle are the same as those of the method for remotely planning the air safety corridor path of the unmanned aerial vehicle in comparison with the prior art, and the detailed description is omitted. Unless otherwise indicated, the numerical parameters set forth in the specification and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by the present invention. In particular, all numbers expressing quantities of ingredients, reaction conditions, and so forth used in the specification and claims are to be understood as being modified in all instances by the term "about". Generally, the expression is meant to encompass variations of ± 10% in some embodiments, 5% in some embodiments, 1% in some embodiments, 0.5% in some embodiments by the specified amount.
Furthermore, "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements.
The use of ordinal numbers such as "first," "second," "third," etc., in the specification and claims to modify a corresponding element does not by itself connote any ordinal number of the element or any ordering of one element from another or the order of manufacture, and the use of the ordinal numbers is only used to distinguish one element having a certain name from another element having a same name.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. The remote planning method for the unmanned aerial vehicle aerial safety corridor path comprises the following steps:
receiving a service application of an unmanned aerial vehicle user based on a remote planning service center;
establishing a three-dimensional task space model based on an airspace range according to the service application;
establishing an air safety corridor based on the three-dimensional task space model by combining terrain features, safety constraints of ground facilities and crowds and air traffic management requirements;
according to the attribute and the environmental change of the unmanned aerial vehicle, performing off-line path planning service in the air safety corridor before flight;
in the flight process, according to the state of the aircraft and the change of the surrounding environment, the dynamic path planning service is carried out on line;
the off-line path planning service in the air safety corridor before flight is carried out on the basis of a two-stage ant colony algorithm;
the two-stage ant colony algorithm comprises the following steps:
the method comprises the steps of discretizing the whole search space into a series of three-dimensional discrete points based on three-dimensional path planning of an ant colony algorithm, wherein the discrete points are nodes needing to be searched by the ant colony algorithm;
selecting a forward movement distance
Is two planes
Distance between, when in plane
In the case where the point P is moved 1km in the forward direction, the maximum allowable distance of the lateral movement is set to
The maximum allowable distance of longitudinal movement is
When the ant advances from the point P along the direction of the x axis, a visible area exists for searching the next point;
when the ants move from the current point to the next point, calculating the selection probability of each point in the visible area according to the heuristic function;
based on two-stage search method, the original in the horizontal plane is obtained
Is compressed into
，
The number of grids, or
Division of nodes into
Selecting the elevation lower limit of each node in each area after compression as the maximum value of the elevation lower limit of the original node in the area;
the two-stage search method comprises the following steps: at the time of primary search
Searching by taking the horizontal coordinate of each node as an independent variable to obtain a corresponding primary path; and performing second-stage search on each corresponding primary path as required.
2. The method for remotely planning the path of the safety corridor in the air for an unmanned aerial vehicle as claimed in claim 1, wherein the service application comprises a flight planning request for the unmanned aerial vehicle, and the flight planning request comprises basic attribute parameters, performance parameters, a flight departure place, a destination and a position parameter of a place which must be passed by the unmanned aerial vehicle.
3. The method for remote planning of an aerial safety corridor path of an unmanned aerial vehicle according to claim 1, wherein establishing a three-dimensional task space model based on an airspace range according to the service application comprises:
taking the vertex of the lower left corner of the three-dimensional map as a coordinate origin A of a three-dimensional planning space, and establishing a three-dimensional planning coordinate system A-xyz, wherein the x axis points to the east along the longitude direction, the y axis points to the north along the latitude direction, the A-xy plane coincides with the sea level, and the z axis is vertical to the sea level and faces upwards;
taking a point A as a vertex in a planning coordinate system, taking the maximum warp-wise length AA 'of a planning space along the x-axis direction, taking the maximum weft-wise length AD' of the planning space along the y-axis direction, and taking the maximum altitude AB of the planning space along the z-axis direction to form a cubic area ABCD-A 'B' C 'D', namely a three-dimensional path planning task space;
adopting a method of equally dividing the space, extracting grid points required by path planning from the three-dimensional space, equally dividing the planning space n along the x axis to obtain n +1 planes
(ii) a Equally dividing along the y-axis m; finally, the three-dimensional task space model is determined by equally dividing along the z-axis l.
4. The method for remotely planning the path of an unmanned aerial vehicle aerial safety corridor according to claim 1, wherein the step of establishing the aerial safety corridor based on the three-dimensional task space model by combining terrain features, safety constraints of ground facilities and people, and air traffic management requirements comprises the following steps:
acquiring terrain elevation data;
acquiring elevation data of ground facilities;
acquiring data of an air traffic control flight-limiting area;
acquiring geographic information of people with heavy activities;
and removing terrain elevation data, ground facility elevation data, air traffic control flight limiting area data and geographic information of heavy and movable intensive crowd from the three-dimensional task space model, and determining the aerial safety corridor.
5. The method for remotely planning the path of the unmanned aerial vehicle safety corridor according to claim 1, wherein during the flight, the dynamic path planning service is performed on line according to the change of the state and the surrounding environment of the unmanned aerial vehicle, and the method comprises the following steps:
dynamic information is reported to a remote planning comprehensive supervision platform in real time according to the regulations, and a remote planning center timely performs necessary correction on a dynamic path of the dynamic information;
the actual trajectory is compared with the predicted trajectory, and the result of dynamic planning can be optimized.
6. A controller in communication with the UAV, the controller comprising:
a memory for storing executable instructions;
a processor for executing the method for remote planning of an unmanned aerial vehicle airborne safety corridor path according to any one of claims 1 to 5, according to executable instructions stored in the memory.
7. An unmanned aerial vehicle, comprising:
a memory for storing executable instructions;
a processor for executing the method for remote planning of an unmanned aerial vehicle airborne safety corridor path according to any one of claims 1 to 5, according to executable instructions stored in the memory.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2018105991403 | 2018-06-11 | ||
| CN201810599140 | 2018-06-11 |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110597276A CN110597276A (en) | 2019-12-20 |
| CN110597276B true CN110597276B (en) | 2022-10-28 |
Family
ID=68852579
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910504188.6A Active CN110597276B (en) | 2018-06-11 | 2019-06-11 | Remote planning method for unmanned aerial vehicle aerial safety corridor path |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110597276B (en) |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113227710A (en) * | 2019-12-31 | 2021-08-06 | 深圳市大疆创新科技有限公司 | Flight control method, device, system and computer readable medium for unmanned aerial vehicle |
| EP3901859A1 (en) * | 2020-04-22 | 2021-10-27 | Volocopter GmbH | Method of and system for operating an aircraft for assessing operational risk |
| CN111724631B (en) * | 2020-05-29 | 2021-09-24 | 北京三快在线科技有限公司 | Unmanned aerial vehicle service management system, method, readable storage medium and electronic device |
| CN112363408A (en) * | 2020-08-28 | 2021-02-12 | 西安羚控电子科技有限公司 | Method for constructing unmanned aerial vehicle air route virtual simulation model |
| JP7424267B2 (en) * | 2020-10-02 | 2024-01-30 | トヨタ自動車株式会社 | Control devices, systems, aircraft, and transportation methods |
| CN113253761B (en) * | 2021-06-08 | 2021-11-09 | 北京远度互联科技有限公司 | Real-time path planning method and device, movable carrier and storage medium |
| CN113433974B (en) * | 2021-07-22 | 2022-07-05 | 河南大学 | Aircraft safety track planning method under strong convection weather |
| CN113848869B (en) * | 2021-10-20 | 2023-03-07 | 北京三快在线科技有限公司 | Unmanned equipment control method and device, storage medium and electronic equipment |
| CN114518772B (en) * | 2022-02-25 | 2023-02-28 | 中发国研信息技术研究院(北京)有限公司 | Unmanned aerial vehicle swarm self-organization method in rejection environment |
| CN114580168A (en) * | 2022-03-03 | 2022-06-03 | 山东瑞智飞控科技有限公司 | Method for fusion display of unmanned aerial vehicle monitoring atmospheric pollution data and three-dimensional GIS |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8543265B2 (en) * | 2008-10-20 | 2013-09-24 | Honeywell International Inc. | Systems and methods for unmanned aerial vehicle navigation |
| CN102929285A (en) * | 2012-11-16 | 2013-02-13 | 中国民用航空飞行学院 | Multi-target distribution and flight path planning method for multiple rescue helicopters |
| CN103295080A (en) * | 2013-06-14 | 2013-09-11 | 西安工业大学 | Three-dimensional path programming method based on elevation diagram and ant colony foraging |
| CN104406580B (en) * | 2014-11-21 | 2018-08-28 | 北京科航军威科技有限公司 | A kind of air navigation aid of common aero vehicle, device and system |
| CN104504940B (en) * | 2015-01-07 | 2017-01-04 | 江苏理工学院 | Control method of air traffic control system |
| CN106297417A (en) * | 2016-09-27 | 2017-01-04 | 南京航空航天大学 | A kind of unmanned plane during flying service and supervisory systems and method |
-
2019
- 2019-06-11 CN CN201910504188.6A patent/CN110597276B/en active Active
Non-Patent Citations (1)
| Title |
|---|
| 复杂环境下无人机快速航迹规划研究;杨俊等;《计算机工程与应用》;20120721(第21期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110597276A (en) | 2019-12-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110597276B (en) | Remote planning method for unmanned aerial vehicle aerial safety corridor path | |
| CN109814598B (en) | Unmanned aerial vehicle low-altitude public navigation network design method | |
| CN110728857B (en) | Low-altitude isolation airspace traffic management method based on vertically-taking-off and landing unmanned aerial vehicle | |
| EP3816889A1 (en) | Uav flight management planner | |
| CN113776534B (en) | Unmanned aerial vehicle three-dimensional time-varying airspace navigation method based on three-dimensional subdivision grid | |
| KR102399293B1 (en) | A method on the construction of a drone safety flight map based on spatial information | |
| Krozel et al. | Turn-constrained route planning for avoiding hazardous weather | |
| Xu et al. | Iterative construction of low-altitude UAV air route network in urban areas: Case planning and assessment | |
| US20200066031A1 (en) | Method, apparatus, and computer program product for dynamic flight range visualization | |
| Besada et al. | Drone flight planning for safe urban operations: UTM requirements and tools | |
| KR102422300B1 (en) | Method and system for providing climate information for port selection of urban air mobility | |
| Schopferer et al. | Trajectory risk modelling and planning for unmanned cargo aircraft | |
| Ortlieb et al. | Modular modelling of ground and air risks for unmanned aircraft operations over congested areas | |
| Liao et al. | Enable UAVs safely flight in low-altitude: A Preliminary research of the public air route network of UAVs | |
| US20140067267A1 (en) | Methods for determining suitable waypoint locations | |
| Tan et al. | Enhancing sustainable urban air transportation: Low-noise UAS flight planning using noise assessment simulator | |
| Podolskaia et al. | Using of transport network model to estimate travelling time and distance for ground access a forest fire | |
| Li et al. | Preliminary Research on Remote Planning Methods for Paths of Unmanned Aerial Vehicles in the Safe Aerial Corridor | |
| Zhao et al. | Simulation of forest fire occurrence and spread based on cellular automata model | |
| Neto et al. | A-star path planning simulation for UAS Traffic Management (UTM) application | |
| Liao et al. | Potentials of uav remote sensing data carrier–a case of application in uav low altitude route—planning | |
| Fedorova et al. | Determining the composition of the group of drones and the basing method for oil pipeline monitoring | |
| Seyfi-Shishavan et al. | A Novel Mathematical Model to Design UAV Trajectory for Search and Rescue Operations in Disaster | |
| Hu et al. | Conflict probability estimation using a risk-based dynamic anisotropic operational safety bound for UAS traffic management | |
| Cheaitou et al. | Optimizing and Visualizing Dynamic Service Performance for Airspace Users to Enable Safe and Efficient Urban Air Mobility in Dubai |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20210322 Address after: No. 9 Dengzhuang South Road, Haidian District, Beijing 100094 Applicant after: Aerospace Information Research Institute,Chinese Academy of Sciences Address before: No. 9 Dengzhuang South Road, Haidian District, Beijing 100094 Applicant before: Academy of Opto-Electronics, Chinese Academy of Sciences |
|
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20210325 Address after: 579 qianwangang Road, Huangdao District, Qingdao City, Shandong Province Applicant after: SHANDONG University OF SCIENCE AND TECHNOLOGY Applicant after: Aerospace Information Research Institute,Chinese Academy of Sciences Address before: No. 9 Dengzhuang South Road, Haidian District, Beijing 100094 Applicant before: Aerospace Information Research Institute,Chinese Academy of Sciences |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |