CN108932876B - Express unmanned aerial vehicle flight path planning method introducing black area A and ant colony hybrid algorithm - Google Patents
Express unmanned aerial vehicle flight path planning method introducing black area A and ant colony hybrid algorithm Download PDFInfo
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Abstract
The invention discloses an express unmanned aerial vehicle flight path planning method introducing a mixed algorithm of A x and ant colony in a black area, firstly determining obstacle coordinates and dot express cabinet coordinates, neglecting a no-fly area and obstacles, and making a track by using an ant colony algorithm; confirming a starting point, a passing point and a terminal point of the route, and determining an initial route; establishing grid points, setting the length and width of each grid point to be S meters, establishing margins of at least 3 grid points along the edge of the obstacle or the no-fly area, and defining the grids of the obstacle and the margins around the obstacle as black areas; and judging whether the line segment of each section of route passes through the barrier or not, if the route passes through the black area, starting the judgment from the starting point, and sequentially using the A-star algorithm until all routes bypass all barriers or the no-fly area, thereby finishing the route planning of the express unmanned aerial vehicle. The invention introduces the black area, integrates the advantages of mixed use of A and ant colony hybrid algorithm, and makes the planned unmanned aerial vehicle flight path more reasonable, the path shorter and the unmanned aerial vehicle energy consumption less.
Description
Technical Field
The invention belongs to the field of unmanned aerial vehicle path planning, relates to unmanned aerial vehicle track planning, and particularly relates to an express unmanned aerial vehicle track planning method introducing an A-star and ant colony mixed algorithm of a black area.
Background
Nowadays, unmanned aerial vehicles are applied to many fields, and along with the rapid development of express delivery industry, express delivery also gradually becomes the rigid demand in people's lives. However, as the competition of large express companies is intensified, the requirements of people on the time consumption and cost of express transportation become higher and higher. However, due to the limitation of factors such as traffic on land, the express delivery time of land transportation is difficult to break through greatly. Therefore, the transportation of the express by using the unmanned aerial vehicle becomes a key point concerned by each large express company. The express in China has been developed at a high speed all the time in the last 10 years. The annual composite growth rate is as high as 40%, and the traffic volume is increased from 30 hundred million pieces in 2006 to 400 hundred million pieces in 2017. And express traffic has increased tremendously over the last 3 years. All large express companies at home and abroad rapidly develop unmanned aerial vehicle express. Therefore, the fact that the unmanned aerial vehicle express delivery trend is popularized only a time problem in the future can be expected. The unmanned aerial vehicle express delivery is based on the advantages of low cost, high efficiency, rapidity and the like, so that the popularization of the unmanned aerial vehicle is on the future. However, a stable intelligent unmanned aerial vehicle must need a smart cruise system with high sensitivity and high automation degree.
In the prior art, the unmanned aerial vehicle track planning is generally the planning of a star and ant colony mixed algorithm, but each algorithm has respective advantages and defects, and the unmanned aerial vehicle track planning is also independently used in the prior art, and has certain difficulty due to different principles when being used together.
Disclosure of Invention
The invention aims to provide an express unmanned aerial vehicle flight path planning method introducing an A-star and ant colony mixed algorithm of a black area, overcomes respective defects of the A-star and ant colony mixed algorithm, integrates the advantages of the A-star and ant colony mixed algorithm, and provides an algorithm for planning a flight path of an unmanned aerial vehicle to be more optimal.
In order to solve the technical problems, the invention adopts the technical scheme that:
an express unmanned aerial vehicle flight path planning method introducing a mixed algorithm of A and ant colony in a black area is characterized by comprising the following steps:
firstly, determining obstacle coordinates and dot express cabinet coordinates, ignoring no-fly zones and obstacles, and making a track by using an ant colony algorithm;
confirming a starting point, a passing point and a terminal point of the route, connecting express cabinets of all network points, and determining an initial route;
establishing grid points, setting the length and width of each grid to be S meters, establishing margins of at least 3 grid points along the edges of the obstacles or the no-fly zone, defining the grids of the obstacles and the margins around the obstacles as black zones, and establishing the black zones for each obstacle or the no-fly zone according to the method;
step four, judging whether the line segment of each section of route passes through the obstacle or not, wherein the judgment method is to select whether the points (x, y) on the route segment meet the requirement in the set of the black area edge vertex every L meters;
and step five, if the route passes through the black area, confirming the starting point, the passing point and the end point of the route, starting judgment from the starting point, sequentially using an A-star algorithm until the route bypasses all barriers or no-fly areas, and finishing the route planning of the express unmanned aerial vehicle.
As an improvement, in the first step, the ant colony algorithm comprises the following steps:
step 1.1, firstly, initializing, randomly placing m ants in n cities, adding the city where the current ant is located into a taboo list of the ant, wherein the pheromone content on each side is equal, and setting tauij(0) C (C is a constant);
step 1.2t moment, the transfer probability of ants from city i to city j
Selecting a path, and adding the city j into a taboo table;
step 1.3, judging whether the taboo list of the ant k contains all cities, if so, completing one round trip by the ant k, and enabling the path taken by the ant k to be a feasible solution; otherwise, returning to the step 1.2;
step 1.4, after the moment of n, when the ants finish one round trip, the pheromone content of each path is adjusted according to the following formula:
in the formula
The probability of the ant k transferring from the point i to the point j; a indicates the relative importance of the trajectory,β represents the relative importance of visibility; tau isij(t) indicates the pheromone content of the path ij at time t; etaij(t) represents the visibility of path ij at time t, typically taken as ηij＝1/dij，dijRepresents the distance from city i to city j; allowedkRepresents a city available for ant k to select; tau isis(t) pheromone content from a path point i to a point s at time t, ηis(t) represents visibility from a path point i to a point s at the time t;
step 1.5, judging whether the maximum iteration frequency is reached, if so, calculating the minimum value of the peripheral travel of the ants, and taking the corresponding path as the shortest path; otherwise, go back to step 1.1.
As an improvement, in the fifth step, the flow of the A-star algorithm is
Step 2.1, setting basic parameters, inputting a matrix which comprises a starting point, a terminal point and an obstacle point, and setting an optional path table open table and a passed path table close table;
2.2, putting the starting point into a close table, and putting 4 nodes, which are up, down, left and right, of the starting point into an open table;
step 2.3, calculating the path cost F of 4 points in an open table according to the following formula, and selecting a node A with the minimum cost;
F＝G+H
in the formula, G represents the Euclidean distance from the current square to the starting square, and H represents the path consumption from the current square to the end square;
step 2.4, putting the point A into a close table, and emptying an open table;
step 2.5, judging whether a terminal node exists in the current close table, and if so, ending the program; if not, go to step 2.6;
step 2.6, putting 4 nodes of the upper, lower, left and right sides of the current point into an open table, calculating the node with the minimum path cost in the open table, storing the node into a close table and emptying the open table;
step 2.7, judging whether close contains an end point, if yes, ending the program; otherwise, go back to step 2.6.
As an improvement, in the third step, the grid length and width S is set to be in the range of 1-4 m.
As an improvement, in step three, the margin of grid points used for establishing the black area is 3-6.
The invention has the beneficial effects that:
the invention introduces the black area to mix the A and the ant colony hybrid algorithm for use, overcomes the respective disadvantages of the A and the ant colony hybrid algorithm, integrates the advantages of the A and the ant colony hybrid algorithm, and provides an algorithm for planning the flight path of the unmanned aerial vehicle to be more optimal, so that the planned flight path of the unmanned aerial vehicle is more reasonable, the path is shorter, and the energy consumption of the unmanned aerial vehicle is less.
Drawings
FIG. 1 shows two obstacle crossing modes of an unmanned aerial vehicle for an obstacle;
wherein the obstacle a in fig. 1 is higher and detours around the obstacle, and the obstacle b in fig. 1 is not high and passes over from the top of the obstacle.
Fig. 2 is a diagram comparing the expected route and the actual route of the unmanned aerial vehicle.
Fig. 3 is a planning diagram of the error range of the no-fly zone.
Fig. 4 is a diagram of the algorithm a.
Fig. 5 is a value representation of the a-algorithm F, G, H.
Fig. 6 is a graph showing the algorithm row path.
Fig. 7 is a schematic diagram of the algorithm planning.
Fig. 8 is a flowchart of the algorithm a for planning the flight path.
Fig. 9 is a flowchart of the ant colony algorithm.
Fig. 10 is a schematic diagram of edge mesh establishment.
Fig. 11 is a flowchart of a process combining the a-algorithm and the ant colony algorithm.
Fig. 12 is a point diagram for simulating a dot express cabinet.
Fig. 13 is a schematic view of point selection coordinates of a network point express delivery cabinet.
Fig. 14 is a graph of the shortest path that is calculated and returned in the program written in MATLAB by using the ant colony algorithm in fig. 13 and is sent to each node express cabinet.
Fig. 15 is a trajectory planning diagram of the a-algorithm and the ant colony algorithm.
The numbers in the squares in fig. 4 to 7 indicate the numbers of the squares.
Detailed Description
The present invention will now be described, by way of example, with reference to the accompanying drawings, in which some, but not all of the details of the prior art are known.
1 black region
In order to ensure the rapidity of the unmanned aerial vehicle, the invention divides the modes of the unmanned aerial vehicle flying over the barrier into 2 types, the current flying height is h, and the height of the barrier high-rise is hBuildingThe set flight mode conversion threshold value is hs。
1: when the difference between the height of the obstacle and the flying height of the unmanned aerial vehicle is lower than a flying mode change threshold value, namely hBuilding-h≤hsThen the drone chooses to fly directly from the top as shown in fig. 1 (b).
2: when the height of the obstacle is above the flight mode switching threshold, i.e. hBuilding-h＞hsThen the drone chooses to fly around from the side of the obstacle as shown in fig. 1 (a).
The first flying mode (the top is crossed) can enable the unmanned aerial vehicle to be always at the maximum speed, and the electric energy and the time consumption caused by the acceleration and deceleration process are reduced. But the disadvantage is that the unmanned plane needs to overcome the gravity to do work, which brings certain energy loss. In the three-dimensional space, the first flight mode and the second flight mode (bypassing) can be reasonably used according to specific practical conditions.
The unmanned aerial vehicle can receive the influence of environmental factor among the flight process, leads to among the in-process unmanned aerial vehicle of flying can not follow predetermined flight path completely and fly. But the unmanned aerial vehicle can adjust the flight state of the unmanned aerial vehicle in real time. The drone is caused to generally follow the flight trajectory as shown in fig. 2. Meanwhile, the no-fly zone is in an irregular shape in practical conditions.
In order to prevent the unmanned aerial vehicle from mistakenly entering the no-fly zone. We then artificially enlarge the no-fly zone to a rectangle ABCD and call this rectangle a black zone. As shown in FIG. 3, wherein hminThe distance of the flight error of the unmanned aerial vehicle can be set according to the specific conditions of the unmanned aerial vehicle which are not used. The main function of the black area is to outline the no-fly zone in advance. When the unmanned plane is forbiddenWhen flying, the unmanned aerial vehicle can directly fly along each side of the surface of no-fly zone. Instead of encountering a no-fly zone, the fly-around is passively selected. And when the unmanned aerial vehicle meets complex buildings such as a high building, a real-time route is not required to be found again, and only a black area needs to be drawn. The mode of actively processing the no-fly zone can enable the unmanned aerial vehicle to avoid the obstacle with shortest reaction time and fastest speed when meeting the obstacle.
Algorithm
The A-star algorithm is very suitable for obstacle avoidance and path exploration of a two-dimensional plane path. It can find the shortest path with the least cost.
As shown in fig. 4, one plane is divided into 5 × 7 squares, and the numbers 1 to 35 are used as the numbers of the squares for the sake of convenience in the following description, and the numbers in fig. 5 to 7 have the same meaning. In the figure, the area consisting of 11, 18, 25 represents the obstacle area, the area of 16 represents the start point, and the area of 20 represents the end point. Each square is a square, the side length is defined as 10, and only the transverse and longitudinal movement of the unmanned aerial vehicle is considered. Let F be the path cost.
F ═ G + H formula (1)
In formula (1), G represents the euclidean distance of the current cell from the starting cell, and H represents the path cost of the current cell from the ending cell. Let the start point be n (x, y) and the end point coordinate be coarse (x, y).
In the above formula, n.x and n.y respectively represent the horizontal and vertical coordinates of the start point, and good.x and good.y respectively represent the horizontal and vertical coordinates of the end point.
In fig. 5, the upper left corner of the square box is the starting point to the path cost F, and the smaller the value of F, the smaller the corresponding cost. The a-algorithm introduces an open table and a close table, and the open table contains the current alternative paths. The close table contains the paths that have already been traversed. At this point, the open table contains squares 9, 15, 17, 23, while the close table contains square 16. Since the cost of the grid 17 is minimal at this point, the drone proceeds to the grid 17 next. At the same time, the square 17 is removed in the open table and the square 17 is added to the close table.
When the cost of the panel 10 and 24 is the same after the drone has walked from the panel 16 to the panel 17, as shown in figure 6, a path is randomly selected for the drone.
This is repeated until the end point squares are incorporated into the close table, similarly. That is, as shown in fig. 7, the route of the drone can be obtained.
The flow of the A algorithm is
(1) And setting basic parameters. A matrix is input, wherein a starting point, an end point and an obstacle point are contained, and an alternative path table open table and a path table close table which is already passed are set.
(2) The start point is put into the close table and the 4 nodes above, below, left and right of the start point are put into the open table.
(3) And (3) calculating the path cost F of 4 points in the open table according to the formula (1), and selecting the node A with the minimum cost.
(4) Point a was placed in the close table and the open table was emptied.
(5) Judging whether a terminal node exists in the current close table or not, and if so, ending the program; if not, go to step (6).
(6) The upper, lower, left and right 4 nodes of the current point are put into the open table. And calculating the node with the minimum path cost in the open table, storing the node into the close table and emptying the open table.
(7) And (4) judging whether close contains an end point, if so, ending the program, otherwise, returning to the step (6), and the whole flow is shown as the figure 8.
Ant colony algorithm
The ant colony algorithm is a bionic random optimization algorithm derived from organisms. The shortest path can be found by utilizing the cooperative characteristic among ants.
Pheromone: the information is the communication mode between ants. The ant will select the path according to the strength of the path pheromone in the searching process.
Tabu table: in the algorithm, every time an ant passes through a path, the path is added into a taboo table of the ant, and then the path is not searched.
Let the pheromone content of each path be the same at the initial time, an ant k ( k 1,2, 3.. said., m) determines the next target according to the pheromone content on each path during the movement, and the probability that the ant k transfers from the position i to the position j at the time t is:
in the formula
The probability of the ant k transferring from the point i to the point j; α represents the relative importance of the trajectory, β represents the relative importance of visibility; tau isij(t) indicates the pheromone content of the path ij at time t; etaij(t) represents the visibility of path ij at time t, typically taken as ηij＝1/dij，dijRepresents the distance from city i to city j; allowedkRepresenting the city available for ant k to select.
The meaning of this expression is that the judgment basis of the ant in selecting the path is the pheromone left on the path, and the probability of selecting the path is high if the pheromone content of the path is high. In addition to preventing the formation of locally optimal solutions. After n times, the ants complete one cycle, and the pheromone content on the path needs to be adjusted as follows.
In the formula (5), ρ represents the persistence of the pheromone on the path, and the information left before is gradually lost due to the attenuation degree of the track along with the time, and 1- ρ represents the disappearance degree of the information.
Indicates that the kth ant leaves the pheromone on the path ij in the current cycle, delta tauijIndicates the pheromone increment, L, on path ij in the present cyclekRepresents the path length of the kth ant circulating for one circle, and Q is a constant.
The ant colony algorithm comprises the following steps:
(1) firstly, initializing, randomly putting m ants in n cities, and adding the city where the current ant is located into a taboo list of the ant. At this time, the pheromone content on each side is equal, and τ is setij(0) C (C is a constant);
(2) at time t, the transition probability of ants from city i to city j
Selecting a path, and adding the city j into a taboo table;
(3) judging whether the taboo list of the ant k contains all cities, if so, completing one round trip by the ant k, and enabling the path taken by the ant k to be a feasible solution; otherwise, returning to the step (2).
(4) After n moments, when the ants finish one round trip, the pheromone content of each path is adjusted according to the formula (4):
(5) and judging whether the maximum iteration frequency is reached, if so, calculating the minimum value of the ant's circumambulation, and the corresponding path is the shortest path. Otherwise, go back to step (1), and the whole flow is as shown in fig. 9.
Application of A-star and ant colony hybrid algorithm in trajectory planning
The a-algorithm, while it works well at handling obstacles between 2 points, does not solve the cruise problem. However, the ant colony algorithm has an advantage of solving the cruise problem, but the ant colony algorithm is not very effective in the planning of bypassing obstacles. Therefore, the A-star algorithm and the ant colony algorithm are fused to carry out the track planning on the express unmanned aerial vehicle.
The algorithm comprises the following steps:
firstly, determining obstacle coordinates and dot express cabinet coordinates, ignoring no-fly zones and obstacles, and making a track by using an ant colony algorithm;
confirming a starting point, a passing point and a terminal point of the route, connecting express cabinets of all network points, and determining an initial route;
establishing grid points, setting the length and width of each grid to be S meters, establishing margins of at least 3 grid points along the edges of the obstacles or the no-fly zone, defining the grids of the obstacles and the margins around the obstacles as black zones, and establishing the black zones for each obstacle or the no-fly zone according to the method;
step four, judging whether the line segment of each section of route passes through the obstacle or not, wherein the judgment method is to select whether the points (x, y) on the route segment meet the requirement in the set of the black area edge vertex every L meters;
and step five, if the route passes through the black area, confirming the starting point, the passing point and the end point of the route, starting judgment from the starting point, sequentially using an A-star algorithm until the route bypasses all barriers or no-fly areas, and finishing the route planning of the express unmanned aerial vehicle.
The flow is shown in FIG. 11
Application emulation
A school is used as an application place. Suppose that a city district scattered point dispatching center is located in the teaching building No. 1, as shown in FIG. 12, the locations of express delivery cabinets to be delivered are respectively located at points A-L in the drawing, and suppose that the total mass of express items to be delivered by an express unmanned aerial vehicle meets the requirement, in FIG. 12, the upper part is the north direction, and the map scale is about 14 mm: and 50m (meter), and setting the intersection point of the north (longitude) direction where the J is located and the south (latitude) direction of the L as the original point of the map, and respectively setting the coordinates of other selected points as (calculated according to the actual distance, unit: meter).
Table 1 coordinate table of express delivery network
The express delivery network points are marked in a 2-dimensional plane, and the method is shown in fig. 13.
That is, a shortest path from the urban area to the express cabinet of each network point and back needs to be drawn. In this case, it is assumed that there is no interference from high-rise buildings such as no-fly zone. Importing the coordinate data into a program written by MATLAB, setting relevant parameters, and setting the relative importance alpha of the track to be 1; relative importance of visibility β ═ 5; the total number m of ants is 30; the persistence ρ of the trace is 0.8; the number of iterations is 100. The resulting shortest path graph is shown in fig. 14.
2) Establishing grid point coordinates, wherein the side length of each grid is 5 meters, establishing a black area according to the building, and determining the coordinates of each vertex of the black area. According to the simulation model, the range of the graph is set to 1000 × 80 (meters), that is, the number of squares is set to 200 × 160. Black area data, which is processed using the rule shown in fig. 10, is imported, and the start and end point data. And establishing a two-dimensional plane matrix, wherein the starting point in the matrix is represented by 5, the end point in the matrix is represented by 2, the obstacle is represented by 1, the passable point is represented by 0, and the map, namely the type of the two-dimensional array is int type. The black areas are approximated to obtain the vertex coordinates of the black areas as shown in table 2.
TABLE 2 Black area coordinates
(3) And judging whether the flight path passes through the no-fly zone, if so, planning a path around the flight by using an A-star algorithm, and if not, keeping the flight path, wherein a drawn simulation diagram is shown in fig. 15.
In fig. 15, the gray part is indicated as an obstacle, the gray part is an available flight area, the line is a planned flight path, the straight line part is a flight path line judged by the algorithm that the original ant colony algorithm is not reserved by the obstacle, and the broken line is a route planned by the a-x algorithm.
Claims (3)
1. An express unmanned aerial vehicle flight path planning method introducing a mixed algorithm of A and ant colony in a black area is characterized by comprising the following steps:
firstly, determining obstacle coordinates and dot express cabinet coordinates, ignoring no-fly zones and obstacles, and making a track by using an ant colony algorithm;
confirming a starting point, a passing point and a terminal point of the route, connecting express cabinets of all network points, and determining an initial route;
establishing grid points, setting the length and width of each grid to be S meters, establishing margins of at least 3 grid points along the edges of the obstacles or the no-fly zone, defining the grids of the obstacles and the margins around the obstacles as black zones, and establishing the black zones for each obstacle or the no-fly zone according to the method in the third step;
step four, judging whether the line segment of each section of route passes through the obstacle or not, wherein the judgment method is to select whether the points (x, y) on the route segment meet the requirement in the set of the black area edge vertex every L meters;
step five, if the route passes through the black area, confirming a starting point, a passing point and an end point of the route, starting judgment from the starting point, sequentially using an A-star algorithm until the route bypasses all barriers or no-fly areas, and finishing route planning of the express unmanned aerial vehicle;
in the first step, the ant colony algorithm comprises the following steps:
step 1.1, firstly, initializing, randomly placing m ants in n cities, adding the city where the current ant is located into a taboo list of the ant, wherein the pheromone content on each side is equal, and setting the pheromone content tauij(0) C is a constant;
step 1.2t moment, the transfer probability of ants from city i to city j
Selecting a path, and adding the city j into a taboo table;
step 1.3, judging whether the taboo list of the ant k contains all cities, if so, completing one round trip by the ant k, and enabling the path taken by the ant k to be a feasible solution; otherwise, returning to the step 1.2;
step 1.4, after the moment of n, when the ants finish one round trip, the pheromone content of each path is adjusted according to the following formula:
in the formula
The probability of the ant k transferring from the point i to the point j; α represents the relative importance of the trajectory, β represents the relative importance of visibility; tau isij(t) indicates the pheromone content of the path ij at time t; etaij(t) represents the visibility of path ij at time t, typically taken as ηij＝1/dij，dijRepresents the distance from city i to city j; allowedkRepresents a city available for ant k to select; tau isis(t) pheromone content from a path point i to a point s at time t, ηis(t) represents visibility from a path point i to a point s at the time t;
step 1.5, judging whether the maximum iteration frequency is reached, if so, calculating the minimum value of the peripheral travel of the ants, and taking the corresponding path as the shortest path; if not, returning to the step 1.1;
in the fifth step, the flow of the A-star algorithm is
Step 2.1, setting basic parameters, inputting a matrix which comprises a starting point, a terminal point and an obstacle point, and setting an optional path table open table and a passed path table close table;
2.2, putting the starting point into a close table, and putting 4 nodes, which are up, down, left and right, of the starting point into an open table;
step 2.3, calculating the path cost F of 4 points in an open table according to the following formula, and selecting a node A with the minimum cost;
F＝G+H
in the formula, G represents the Euclidean distance from the current square to the starting square, and H represents the path consumption from the current square to the end square;
step 2.4, putting the point A into a close table, and emptying an open table;
step 2.5, judging whether a terminal node exists in the current close table, and if so, ending the program; if not, go to step 2.6;
step 2.6, putting 4 nodes of the upper, lower, left and right sides of the current point into an open table, calculating the node with the minimum path cost in the open table, storing the node into a close table and emptying the open table;
step 2.7, judging whether close contains an end point, if yes, ending the program; otherwise, go back to step 2.6.
2. The express delivery unmanned aerial vehicle flight path planning method of claim 1, characterized in that: in the third step, the length and width S of the grid are set to be 1-4 m.
3. The express delivery unmanned aerial vehicle flight path planning method of claim 1, characterized in that: in the third step, the margin of grid points used for establishing the black area is 3-6.
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| CN111399543B (en) * | 2020-04-04 | 2022-09-02 | 西安爱生技术集团公司 | Same-region multi-collision-free air route planning method based on A-star algorithm |
| CN112258899B (en) * | 2020-10-21 | 2022-11-29 | 朱杰 | General aircraft longitude and latitude line network construction and operation control method |
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| CN114296474A (en) * | 2021-12-10 | 2022-04-08 | 国网新疆电力有限公司超高压分公司 | Unmanned aerial vehicle path planning method and system based on path time cost |
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