CN110031004B - Static and dynamic path planning method for unmanned aerial vehicle based on digital map - Google Patents
Static and dynamic path planning method for unmanned aerial vehicle based on digital map Download PDFInfo
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Abstract
The invention provides a static and dynamic path planning method for an unmanned aerial vehicle based on a digital map, and relates to the technical field of unmanned aerial vehicle path planning. The method comprises the following steps: constructing an unmanned aerial vehicle flight three-dimensional digital map; setting coordinates of starting and ending positions of the unmanned aerial vehicle flight, and initializing a path planning constraint condition of the unmanned aerial vehicle; updating the speed and the position of the particles according to a hybrid particle swarm simulated annealing algorithm; drawing a global static optimal flight path of the unmanned aerial vehicle; judging whether the unmanned aerial vehicle meets sudden threats or not, and if so, replanning the static flight path according to a hybrid particle swarm simulated annealing algorithm; and drawing a global dynamic optimal flight path of the unmanned aerial vehicle. Under the static condition, the method can quickly plan an accurate global navigation track; when encountering sudden threats in the flight process, the method can quickly avoid the threats and effectively plan the path again to ensure the flight safety of the unmanned aerial vehicle.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle path planning, in particular to an unmanned aerial vehicle static and dynamic path planning method based on a digital map.
Background
Unmanned Aerial Vehicles (UAVs) are known as drones, which fly by receiving radio commands or by program control themselves. In the 20 th century, the first unmanned aerial vehicle is successfully developed, then the unmanned aerial vehicle in the form of a target drone is successfully developed, and as time goes on, the reconnaissance unmanned aerial vehicle controlled by a multi-instruction program and the unmanned aerial vehicles in various complex control forms are successively developed, and the unmanned aerial vehicles play respective roles in different fields. The rapid development of the science and technology in the modern society, especially the unmanned military equipment in the modern battlefield environment, has become an obvious trend. With the continuous improvement of aviation flight technology, the military value of the unmanned aerial vehicle with the autonomous control function on a battlefield is more and more obvious, so that the unmanned aerial vehicle can be an important component of military fighting strength of each country on a future complex battlefield.
The unmanned aerial vehicle needs to finish autonomous combat flight, path planning is one of key technologies which need to be solved firstly, a route which is manually planned in the past is rough, and a practical and effective penetration effect cannot be achieved, so that fine planning of the unmanned aerial vehicle path is needed to be achieved through an algorithm with excellent computing performance. The unmanned aerial vehicle path planning is to find an optimal path between start and stop positions, and the following aspects are mainly considered in the path planning process: flight path length, terrain, natural weather environment, threat factors, etc [6] And a reasonable route is planned on the premise of integrating the factors to ensure that the task is safe and smooth in the process of executing the task. In general, a plurality of uncertain factors exist in the flight environment of the unmanned aerial vehicle, so that exact global flight environment information cannot be obtained at the initial stage, and therefore, the conventional static global path planning of the unmanned aerial vehicle cannot meet the real-time combat requirement of the existing unmanned aerial vehicle. At present, when a threat is encountered, the unmanned aerial vehicle dynamic path planning generally selects to bypass a sudden threat and then returns to an original static planning route, and the processing mode not only can increase the path re-planning time, but also can increase the path cost, further increases the oil consumption and has poor maneuvering performance.
In recent years, the research of path planning is mainly focused on an unmanned aerial vehicle path planning search algorithm based on geometry, and the unmanned aerial vehicle path planning search algorithm is mainly divided into a random search algorithm and a deterministic search algorithm, wherein the deterministic search algorithm comprises a dynamic planning algorithm, a D algorithm, an A algorithm and a Dijkstra algorithm, the algorithms have predictability for path search, the requirement for establishing a model is relatively simple, the problem solving is convenient to realize, but the path planning of the unmanned aerial vehicle is increased to a three-dimensional space finally, the data calculation amount is large, the combined explosion situation can occur by using the deterministic algorithm, the path planning problem is not easy to solve, the random search algorithm has better global optimization performance in the complex problem space, and the robustness and the parallelism of the random search algorithm have obvious advantages compared with the deterministic search algorithm. However, at present, the flight environment is more and more complex, and the flight requirement for the unmanned aerial vehicle is also more and more high, so that further research on achieving a more excellent unmanned aerial vehicle path planning algorithm to complete fast and accurate unmanned aerial vehicle path planning still remains a challenging subject.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a static and dynamic path planning method of an unmanned aerial vehicle based on a digital map, which can quickly plan an accurate global navigation track under a static condition; when encountering sudden threats in the flight process, the method can quickly avoid the threats and effectively plan the path again to ensure the flight safety of the unmanned aerial vehicle.
In order to achieve the purpose, the static and dynamic path planning method of the unmanned aerial vehicle based on the digital map comprises the following specific steps:
step 1: the method adopts a digital map technology to construct the unmanned aerial vehicle flight three-dimensional digital map, and comprises the following specific processes:
step 1.1: constructing a reference topographic map according to a topographic elevation value corresponding to the projection coordinates (x, y) of any point on the three-dimensional digital terrain on a horizontal plane;
the formula for constructing the reference terrain is as follows:
wherein z is 1 (x, y) is the terrain elevation value corresponding to the projection coordinate (x, y) of any point on the three-dimensional digital terrain on the horizontal plane, a 1 ～a 7 Are constant coefficients used for controlling the undulation state of the reference terrain;
step 1.2: constructing a mountain peak topographic map according to the elevation value of the midpoint (x, y) of the digital map;
the formula for constructing the mountain peak terrain is as follows:
wherein z is 2 (x, y) is the elevation value of the midpoint (x, y) of the digital map, n is the number of peaks in the digital map, h m Height of the mth simulated peak, (x) m ，y m ) Is the center coordinate of the mth simulated peak, x sm The attenuation amount of the mth simulated peak in the x-axis direction, y sm The attenuation of the mth simulation peak in the y-axis direction is obtained;
step 1.3: the radar working area is equivalent to a hemispherical terrain by adopting the idea of information fusion, and a radar threat model is established;
the formula for establishing the radar threat model is as follows:
wherein z is 3 (x, y) is the radar threat equivalent elevation value at the point (x, y) in the radar threat model, (x 3 ，y 3 ，z 3 ) Is the position coordinate of any point on the three-dimensional digital terrain, r 3 Radius of radar threat range;
step 1.4: simulating an urban building by using a cube, and establishing an urban environment model;
the formula of the urban environment model is as follows:
wherein z is 4 (x, y) is the elevation at the midpoint (x, y) of the city building model, (x 4 ，y 4 ) The central coordinate of the urban environment model, length is the transverse length of the building in the urban environment model, width is the longitudinal width of the building in the urban environment model, and h is the height of the building in the urban environment model;
step 1.5: simulating a weather threat by using a cylinder, and establishing a weather threat model;
the formula of the weather threat model is as follows:
wherein z is 5 (x, y) is the point (x, y) in the weather threat modelElevation value of r 5 Radius of the weather threat range, (x) 5 ，y 5 ) As the coordinates of the meteorological threat center, h w Height of the weather threat model;
step 1.6: converting the established reference topographic map, the peak topographic map, the radar threat model, the urban environment model and the weather threat model into data which can be processed by a computer, and fusing by combining the information fusion principle to form a comprehensive digital topographic information equivalent three-dimensional digital map;
the expression of the integrated digital terrain information equivalent three-dimensional digital map is as follows:
z(x，y)＝max[z 1 (x，y)+z 2 (x，y)+z 3 (x，y)+z 4 (x，y)+z 5 (x，y)]；
wherein z (x, y) is an elevation value at the midpoint (x, y) of the equivalent three-dimensional digital map of the comprehensive digital terrain information;
step 2: importing an equivalent three-dimensional digital map, setting coordinates of starting and ending positions of unmanned aerial vehicle flight, initializing unmanned aerial vehicle path planning constraint conditions, and performing global static path planning;
the unmanned aerial vehicle path planning constraint condition comprises a minimum flight step length l min (ii) a Maximum turning angle theta, maximum climbing angle beta and minimum flying height H min ；
And step 3: initializing parameters of a mixed particle swarm simulated annealing algorithm and setting the position x of the ith particle i And velocity v i Carrying out random initialization to update the velocity v of the ith particle i And position x i ；
Step 3.1: initializing parameters of the hybrid particle swarm simulated annealing algorithm, including maximum iteration number t max1 Population size N, higher initial temperature T, attenuation factor alpha, learning factor c 1 And c 2 And for the position x of the ith particle i And velocity v i Carrying out random initialization;
step 3.2: calculating the fitness value f (x) of the ith particle according to the unmanned aerial vehicle comprehensive path evaluation function i ) And determining the individual extreme value p of the particle through the minimum fitness value of the particle best And global extreme g best ；
The formula of the unmanned aerial vehicle comprehensive path evaluation function is as follows:
the method comprises the following steps that C is a path planning target function, l is a path length, sigma delta w(s) is the comprehensive cost of the unmanned aerial vehicle, and the comprehensive cost sigma delta w(s) of the unmanned aerial vehicle is as follows:
∑δw(s)＝δ O w O (s)+δ T w T (s)+δ R w R (s)+δ D w D (s)；
wherein w O (s) is the fuel cost of the drone, w T (s) cost of natural weather threat in flight area, w R (s) radar threat cost, w, for the drone D (s) a terrain threat cost, δ O To the cost of fuel consumption weight coefficient, δ T Is a weather threat cost weighting coefficient, δ R As a radar threat cost weighting coefficient, δ D A cost weighting factor for the terrain threat, and δ O +δ T +δ R +δ D ＝1；
Step 3.3: velocity v of the ith particle by means of band compression factor i And position x i Updating to obtain the updated velocity v of the ith particle i ' and position x i ′；
The updated velocity v of the ith particle i ' and position x i The formula of' is as follows:
v i ′＝χ[v i +c 1 r 1 (p best -x i )+c 2 r 2 (g best -x i )]；
x i ′＝x i +v i ′；
wherein r is 1 And r 2 Are all in [0,1 ]]The random number of (2) is greater than,
step 3.4: according to the updated velocity v of the ith particle i ' and position x i ', calculate the updated fitness value f (x) of the ith particle i ') and calculating a variable delta f of the ith particle fitness value before and after updating;
step 3.5: judging whether the variable delta f of the ith particle fitness value is larger than 0, if so, continuing the step 3.6, and if not, receiving the updated speed v of the ith particle i ' and position x i ', continue step 4;
step 3.6: judging whether exp (-delta f/T) is larger than random number rand (0, 1), if yes, receiving updated speed v of ith particle i ' and position x i ', and the updated fitness value f (x) according to the ith particle i ') update particle individual extremum p best And global extreme g best Continuing to the step 4, if not, not updating the speed v of the ith particle i And position x i Continuing to step 4;
and 4, step 4: performing an annealing operation with a temperature T = at, where a is a decay factor;
and 5: judging whether the current iteration times of the hybrid particle swarm simulated annealing algorithm is larger than the maximum iteration times t max1 If yes, drawing a global static optimal flight path of the unmanned aerial vehicle after spline function interpolation processing, and if not, outputting a particle individual extreme value p best And global extreme g best And returning to the step 3.2;
step 6: initializing parameters of the artificial fish swarm algorithm of the mixed particle swarm, including the minimum value omega of the linearly decreasing inertia weight min And maximum value ω max Particle group N, particle view visual, state change step, particle swarm crowding factor delta, random repeat try number, maximum iteration number t max2 ；
And 7: judging whether the unmanned aerial vehicle meets the sudden threat or not, if so, updating the next generation of particles through bulletin board updating information, and re-planning the flight path of the unmanned aerial vehicle, otherwise, flying according to the global static optimal flight path of the unmanned aerial vehicle;
and 8: judging whether the current iteration times of the artificial fish swarm algorithm of the mixed particle swarm is greater than the maximum iteration time t max2 If yes, drawing a global dynamic optimal flight path of the unmanned aerial vehicle through spline function interpolation processing; if not, returning to the step 7.
Further, the step of updating the next generation particles by the bulletin board update information in the step 7 is as follows:
step S1: for the velocity v of the ith particle i And position x i Updating and calculating the fitness value f (x) of the ith particle after updating i ') the state of the particles with the minimum fitness and the value thereof are given to the bulletin board;
step S2: respectively carrying out foraging, rear-end collision, clustering and random behaviors on the particles;
step S2.1: setting the current state of the ith particle to
The next position which is searched randomly within its field of view is @>
The optimal central status of the population is->
Optimum particle status is>
Step S2.2: judging the comprehensive path evaluation function value of the ith particle in the current state of the t generation
Whether or not it is greater than the integrated path merit function ≥ from which the next position is randomly searched for in its field of view>
If yes, then based on the next state/value searched randomly in the visual field range of the tth generation of the ith particle>
And a global extremum point in the tth generation>
Updating the ith particle to obtain the t +1 th generation speed V of the ith particle t+1 iD And State X t+1 iD Continuing to step S3, if not, not updating the ith particle, and continuing to step S2.3;
the method is based on the next position randomly searched in the visual field range of the t generation of the ith particle
And a global extreme point in the tth generation->
Updating the ith particle to obtain the speed of the ith particle in the t +1 th generation>
And status->
The formula of (1) is as follows:
wherein the content of the first and second substances,
the speed of the ith particle in the t +1 th generation under the foraging behavior, t is the current iteration number, t max2 Is the maximum number of iterations, r 1 And r 2 Are all in [0,1 ]]On a random number->
For the current status of the ith particle in the t +1 th passage under foraging behavior, it is selected>
Is a random behavior;
step S2.3: judging whether the random search times in the visual field range of the tth generation of the ith particle are larger than the random repeated try times try _ number, if so, randomly flying the particle in the visual field range of the ith particle, otherwise, randomly searching the next position in the visual field range of the tth generation of the ith particle, and returning to the step S2.2;
step S2.4: judging whether the population state meets the crowding condition, if so, returning to the step S2.2, and if not, searching the optimal central position of the population in the visual field range of the tth generation of the ith particle
And a global extreme point in the tth generation->
Updating the ith particle to obtain the speed of the ith particle in the t +1 th generation>
And status->
Continuing to step S3;
according to the optimal center position of the population searched in the visual field range of the t generation of the ith particle
And a global extreme point in the tth generation->
The speed at which the t +1 th generation of the i-th particle is updated is taken into account>
And status->
The formula of (1) is as follows:
wherein the content of the first and second substances,
for the speed of the t +1 th generation of the i-th particle under clustering behavior, <' > H>
Is the current state of the t +1 th generation of the ith particle under clustering behavior, n f The number of fish groups of the rest artificial fishes in the visual field range of the t generation of the ith particle is shown;
step S2.5: judging the comprehensive path evaluation function value of the ith particle in the current state
If it is less than the integrated path evaluation function value for the optimum particle state ≥ r>
If so, returning to the step S2.2, otherwise, based on the searched optimal particle status->
And a global extremum point in the current iteration state->
Updating the ith particle to obtain the t +1 th generation speed V of the ith particle t+1 iD And State X t+1 iD Continuing to step S3;
the optimal particle state according to the search
And a global extremum point in the current iteration state->
Updating the t +1 th generation speed V of the ith particle t+1 iD And State X t+1 iD The formula of (1) is as follows:
wherein the content of the first and second substances,
for the speed of the t +1 th generation of the i-th particle in rear-end action, be->
The current state of the t +1 th generation of the ith particle under the rear-end collision behavior;
and step S3: calculating the fitness of the ith particle in the t +1 th generation according to the updating process of the ith particle from the t generation to the t +1 th generation, and selecting the speed V of the ith particle in the t +1 th generation corresponding to the minimum value of the fitness in the individual behaviors t+1 iD And state X t+1 iD Updating the current state of the ith particle;
and step S4: and judging whether the fitness of the ith particle after updating is smaller than that of the particles in the bulletin board, if so, updating the particle state information in the bulletin board, and if not, not updating the particle state information in the bulletin board.
The invention has the beneficial effects that:
the invention provides an unmanned aerial vehicle static and dynamic path planning method based on a digital map, which adopts a hybrid particle swarm simulated annealing algorithm (PSO-SA algorithm) under a static condition, optimizes the particle swarm algorithm by using a simulated annealing snap probability strategy, can overcome the premature (local extreme value) problem, and can rapidly plan a precise and effective unmanned aerial vehicle route. Under the dynamic condition, a dynamic path re-planning strategy avoiding threat is adopted, re-path planning is carried out from a threat point, in order to improve the calculation efficiency of the algorithm under the three-dimensional emergency, a hybrid particle swarm artificial fish swarm algorithm (PSO-AFSA algorithm) is adopted, the algorithm introduces strategies of foraging, rear-end collision, clustering and the like of the artificial fish swarm to make particles make better selection, the algorithm has better global convergence performance, can avoid falling into a local extreme value, quickly avoids threat avoidance, carries out accurate path re-planning, and reduces the total cost of unmanned aerial vehicle flight.
Drawings
Fig. 1 is a flow chart of a method for planning static and dynamic paths of an unmanned aerial vehicle based on a digital map according to an embodiment of the present invention;
FIG. 2 is a digital map reference terrain map in accordance with an embodiment of the present invention;
FIG. 3 is a mountain peak topographic map of the digital map according to the embodiment of the present invention;
FIG. 4 is a diagram of a digital map radar model according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a digital map city model according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a digital map weather threat model in an embodiment of the invention;
FIG. 7 is an equivalent three-dimensional digital map of integrated digital terrain information in an embodiment of the present invention;
FIG. 8 is a routing diagram using the method of the present invention under a three-dimensional digital map in an embodiment of the present invention;
FIG. 9 is a top view of path planning of different algorithms in the same three-dimensional digital map environment according to the embodiment of the present invention;
wherein, (a) is the path planning top view of the algorithm proposed by the invention; (b) a path planning top view of the AFSA algorithm; and (c) planning a top view of the path of the PSO algorithm.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clear, the present invention is further described in detail with reference to the accompanying drawings and specific embodiments. The specific embodiments described herein are merely illustrative of the invention and do not delimit the invention.
On the basis of a three-dimensional digital map, the embodiment provides a static and dynamic path planning method for an unmanned aerial vehicle based on the digital map, under the static condition, firstly, a three-dimensional environment for the unmanned aerial vehicle to fly is constructed through the digital map technology, a flight starting position is set, then, path planning is carried out by using a PSO-SA algorithm introducing an SA (synthetic Aperture Radar) sudden-jump probability strategy, the flight cost is determined through the flight constraint condition of the unmanned aerial vehicle, and a global static flight route of the unmanned aerial vehicle is drawn on the basis of meeting the algorithm ending condition; and then setting dynamic threats on a digital map, and performing accurate path re-planning from the positions of the threat points by using a PSO-AFSA algorithm to reduce the total cost of the unmanned aerial vehicle flying. The specific process is shown in fig. 1, and comprises the following steps:
step 1: the method adopts a digital map technology to construct the unmanned aerial vehicle flight three-dimensional digital map, and comprises the following specific processes:
step 1.1: and constructing a reference topographic map according to the topographic elevation value corresponding to the projection coordinate (x, y) of any point on the three-dimensional digital terrain on the horizontal plane.
The formula for constructing the reference terrain is shown as formula (1):
wherein z is 1 (x, y) is the terrain elevation value corresponding to the projection coordinate (x, y) of any point on the three-dimensional digital terrain on the horizontal plane, a 1 ～a 7 Are constant coefficients for controlling the heave state of the reference terrain.
In this example, the reference topography is constructed as shown in FIG. 2, where a is shown in FIG. 2 1 To a 7 The reference terrain condition is taken as 10, 0.2, 0.1, 0.6, 1, 0.1 and 0.1 in sequence.
Step 1.2: and constructing a mountain terrain map according to the elevation values at the points (x, y) in the digital map.
The formula for constructing the mountain peak terrain is shown as formula (2):
wherein z is 2 (x, y) is the elevation value of the midpoint (x, y) of the digital map, n is the number of peaks in the digital map,h m height of the mth simulated peak, (x) m ，y m ) Is the center coordinate of the mth simulated peak, x sm Is the attenuation of the mth simulated peak in the x-axis direction, y sm The attenuation of the mth simulated peak in the y-axis direction is shown.
In this embodiment, x may be passed sm And y sm To change the slope of the peak surface. The constructed mountain peak topography is shown in fig. 3, and when n =4, h = [20, 35, 25, 20 ] is shown in fig. 3]，x m ＝[20，70，20，80]，y m ＝[30，20，70，80]，x sm ＝[10，15，10，15]；y sm ＝[10，15，10，10]Peak model of time.
Step 1.3: and (3) equating a radar working area to be a hemispherical terrain by adopting an information fusion idea, and establishing a radar threat model.
The formula for establishing the radar threat model is shown as formula (3):
wherein z is 3 (x, y) is the radar threat equivalent elevation value at the point (x, y) in the radar threat model, (x 3 ，y 3 ，z 3 ) Is the position coordinate of any point on the three-dimensional digital terrain, r 3 Is the radius of the radar threat range.
In this embodiment, the established radar threat model is shown in fig. 4.
Step 1.4: and (3) simulating the urban building by using the cube, and establishing an urban environment model.
The formula of the urban environment model is shown as formula (4):
wherein z is 4 (x, y) is the elevation at the midpoint (x, y) of the city building model, (x 4 ，y 4 ) Is the central coordinate of the urban environment model, and length is the transverse direction of the building in the urban environment modelAnd the length, width, is the longitudinal width of the building in the urban environment model, and h is the height of the building in the urban environment model.
In this embodiment, the established urban environment model is as shown in fig. 5, and the unmanned aerial vehicle can select to laterally bypass or longitudinally cross the urban environment model according to the position in flight.
Step 1.5: and (4) simulating the weather threat by using a cylinder, and establishing a weather threat model.
The formula of the weather threat model is shown as formula (5):
wherein z is 5 (x, y) is the elevation at point (x, y) in the weather threat model, r 5 Radius of the weather threat range, i.e. the distance of the drone from the center of the weather threat, (x) 5 ，y 5 ) As the weather threat center coordinate, h w Is the height of the weather threat model.
In this embodiment, the weather threat refers to a severe weather phenomenon existing in nature, such as tornado, sand storm, and the like, the established weather threat model is as shown in fig. 6, and the unmanned aerial vehicle generally selects to bypass the weather threat model according to the position in flight.
Step 1.6: and converting the established reference topographic map, the peak topographic map, the radar threat model, the urban environment model and the weather threat model into data which can be processed by a computer, and fusing by combining an information fusion principle to form the integrated digital topographic information equivalent three-dimensional digital map.
The expression of the integrated digital terrain information equivalent three-dimensional digital map is shown as the formula (6):
z(x，y)＝max[z 1 (x，y)+z 2 (x，y)+z 3 (x，y)+z 4 (x，y)+z 5 (x，y)] (6)
wherein z (x, y) is an elevation value at the midpoint (x, y) of the equivalent three-dimensional digital map of the comprehensive digital terrain information.
In this embodiment, the formed equivalent three-dimensional digital map of the integrated digital terrain information is shown in fig. 7, in which the number of peaks n =8, and the coordinates of the center of the peaks are (5, 85), (20, 180), (25, 25), (45, 165), (50, 85), (65, 65), (85, 145), (130, 50); the number of the radars is 1, the center coordinates of the radars are (180, 140), and the radius is 40; 1 city model, wherein the center coordinates of the city model are (60, 60); 1 weather threat model, wherein the center coordinates of the model are (120, 30), and the radius of the model is 30; the mission planning area is 200km × 200km × 0.4km, and the planning horizontal area is converted into a 200 × 200 coordinate area in a ratio of 1000: 1, wherein the vertical direction adopts a ratio of 1: 1, that is, the maximum coordinate value in the vertical direction represents 400m.
And 2, step: and importing an equivalent three-dimensional digital map, setting coordinates of the starting position and the ending position of the unmanned aerial vehicle, initializing a path planning constraint condition of the unmanned aerial vehicle, and performing global static path planning.
The unmanned aerial vehicle path planning constraint condition comprises a minimum flight step length l min (ii) a Maximum turning angle theta, maximum climbing angle beta, minimum flying height H min 。
In this embodiment, the unmanned aerial vehicle path planning constraint is initialized: minimum flight step length l min =1, maximum turning angle θ =45 °, maximum climbing angle β =30 °, minimum flying height H min ＝1。
And step 3: initializing parameters of a mixed particle swarm simulated annealing algorithm and setting the position x of the ith particle i And velocity v i Carrying out random initialization to update the velocity v of the ith particle i And position x i 。
Step 3.1: initializing parameters of the hybrid particle swarm simulated annealing algorithm, including maximum iteration times t max1 Group size N, higher initial temperature T, attenuation factor alpha and learning factor c 1 And c 2 And for the position x of the ith particle i And velocity v i Random initialization is performed.
In this example, the population scale of the initialized PSO-SA algorithm is 100, the annealing parameter is α =0.98, the initial temperature is 10000, and the learning factor is set to c 1 ＝c 2 ＝[1.8，2.2](ii) a Maximum number of iterationst max1 ＝100。
Step 3.2: calculating the fitness value f (x) of the ith particle according to the unmanned aerial vehicle comprehensive path evaluation function i ) And determining individual extreme value p of the particles through the minimum fitness value of the particles best And global extreme g best ；
The formula of the unmanned aerial vehicle comprehensive path evaluation function is shown as formula (7):
wherein, C is a path planning objective function, l is a path length, Σ δ w(s) is a synthetic cost of unmanned aerial vehicle flight, and the formula of the synthetic cost Σ δ w(s) of unmanned aerial vehicle flight is as follows:
∑δw(s)＝δ O w O (s)+δ T w T (s)+δ R w R (s)+δ D w D (s)；
wherein, w O (s) is the fuel consumption penalty, w, of the drone T (s) cost of natural weather threat in flight area, w R (s) radar threat cost, w, for the drone D (s) a terrain threat cost, δ O To be the fuel consumption cost weight coefficient, δ T Cost weighting factor, δ, for weather threats R As a radar threat cost weighting coefficient, δ D A cost weighting factor for the terrain threat, and δ O +δ T +δ R +δ D ＝1。
In this embodiment, unmanned aerial vehicle flight in-process oil consumption cost w O (s) mainly depends on the flying distance and the flying speed, and if n nodes are arranged on the flight path of the unmanned aerial vehicle in total, the flying range distance of the unmanned aerial vehicle is shown as the formula (8):
wherein l i The distance between the ith node and the (i + 1) th node of the unmanned aerial vehicle path is represented, and the oil consumption of the unmanned aerial vehicle is ensured to be constant on the assumption that the flight speed of the unmanned aerial vehicle is kept constantThe cost is shown in equation (9):
wherein o represents the oil consumption proportional coefficient per unit length.
In this embodiment, path segment l is calculated conveniently i The threat cost of (1) is obtained by dividing the path segment into three equal parts, i1, i2, i3, wherein alpha * Alpha representing a threat * A threat, e.g. alpha T Represents the alpha th T A natural weather threat point, α R Represents alpha R A radar threat, α D Represents alpha D A point of threat to the terrain is identified,
representing the distance of the drone from the threat point.
Equivalently treating a cylinder with a certain radius and height as thunderstorm weather, assuming d Tmax Maximum radius representing the area of thunderstorm impact, d Tmin The distance between the unmanned aerial vehicle and the thunderstorm threat center is d T Probability p of damage caused by thunderstorm weather to unmanned aerial vehicle T (d T ) As shown in equation (10):
the natural weather threat point alpha can be known from the formula (10) * For path segment l i The threat cost of (c) is shown in equation (11):
suppose for path segment l i Presence of n T A natural weather threat point, the section is subjected to the total weather threat
As shown in equation (12):
then the natural weather threat cost for the entire drone path is as shown in equation (13):
in this embodiment, the unmanned aerial vehicle receives the terrain threat cost w in flight D (s) threat costs generated by some peaks, highland and the like are mainly included, a stereoscopic cone is used for approximate representation, the cross section of the peak at the corresponding height of the unmanned aerial vehicle in the flight process is a circle, and the radius of the corresponding circle is set as R D The ground clearance of the unmanned aerial vehicle is H, the maximum height of the peak is H, the gradient of the peak is theta, and the distance between the unmanned aerial vehicle and the symmetry axis of the peak is d D When H > H, no consideration of the terrain threat is required. Let d Dmin In order to allow the nearest distance between the unmanned aerial vehicle and the mountain peak terrain, when the distance between the unmanned aerial vehicle and the mountain peak is less than d Dmin When the unmanned aerial vehicle is damaged, the probability of the unmanned aerial vehicle being damaged by collision is 1, correspondingly, when the distance between the unmanned aerial vehicle and a peak is greater than d Dmax And the unmanned aerial vehicle has no collision threat. The formula for calculating the elevation angle (gradient) θ of the terrain is shown in equation (13):
the formula for calculating the radius of the corresponding circumference of the cross section of the peak of the unmanned aerial vehicle in the flying process is shown as the formula (14):
R D (h)＝(H-h)/tanθ (14)
through the analysis, the collision probability of the unmanned aerial vehicle is shown as the formula (15):
the terrain threat α is known from equation (15) T For path segment l i The threat cost of (c) is shown in equation (16):
suppose that in path segment l i Above is present with n D Individual terrain threat point, the total cost of the terrain threat in the section
As shown in equation (17):
then the terrain of the entire drone path is as shown in equation (18):
the radar is a relatively common detection type threat in a real environment, has the functions of remotely detecting, identifying and tracking a target, and analyzes by receiving the electromagnetic wave echo emitted by the radar, so that information about the position, the direction and the like of the detected target is obtained, and therefore the radar threat cost received in the flight process of the unmanned aerial vehicle is considered in the embodiment. The radar is a heavy-weight device in the air defense field, and the electromagnetic wave signal of the radar meets the following signal-to-noise ratio formula (19):
wherein, P t For radar transmission power, G r For receiving antenna gain, G t For the gain of the transmitting antenna, sigma is the effective detection cross-sectional area, lambda is the signal operating wavelength, K is the Boltzmann constant, and L m As an energy loss factor, B n Matching the bandwidth, T, of the filter s Is an absolute temperature value, d R For the length of the drone from the center of the radar, in general G r ＝G t . For convenience of analysis, formula (19) is collatedObtainable formula (20):
wherein, sigma and R are variables, and the values of other system parameters are brought into formula (20) to be arranged into formula (21):
in a real environment, the scattering cross-sectional area for a given drone is fixed, i.e. σ is a constant value, then equation (21) can be further simplified to equation (22):
wherein k is r Is a constant value. In this embodiment, it is assumed that the scanning range of the radar is 360 °, the working range of the radar can be equivalent to a hemisphere, and d is set Rmax Maximum area radius detectable for radar, d Rmin For the area that the radar will detect certainly, it can be known from the equation (22) that when the distance between the unmanned plane and the radar center O is d R In time, the detection threat probability P that the unmanned aerial vehicle receives R (d R ) Can be approximated by formula (23):
then determining the individual extreme value p of the particle through the minimum fitness value of the particle best And global extreme g best 。
Step 3.3: using a mode with compression factor to adjust the speed v of the ith particle i And position x i Updating to obtain the updated speed v of the ith particle i ' and position x i ′。
The updated velocity v of the ith particle i ' and position x i The formula of' is shown in formula (24) and formula (25):
v i ′＝χ[v i +c 1 r 1 (p best -x i )+c 2 r 2 (g best -x i )] (24)
x i ′＝x i +v i ′ (25)
wherein r is 1 And r 2 Are all in [0,1 ]]The random number of (2) is greater than,
step 3.4: according to the updated speed v of the ith particle i ' and position x i ', calculate the updated fitness value f (x) of the ith particle i ') and calculates a variable deltaf for the ith particle fitness value before and after the update.
Step 3.5: judging whether the variable delta f of the ith particle fitness value is larger than 0, if so, continuing the step 3.6, and if not, receiving the updated speed v of the ith particle i ' and position x i ', continue with step 4.
Step 3.6: judging whether exp (-delta f/T) is larger than random number rand (0, 1), if yes, receiving updated speed v of ith particle i ' and position x i ', and updating the fitness value f (x) according to the ith particle i ') update the individual extremum p of the particle best And global extremum g best Continuing to the step 4, if not, not updating the speed v of the ith particle i And position x i And continuing to step 4.
And 4, step 4: an annealing operation is performed with a temperature T = at, where a is the decay factor.
And 5: judging whether the current iteration times of the hybrid particle swarm simulated annealing algorithm is larger than the maximum iteration times t max1 If yes, drawing a global static optimal flight path of the unmanned aerial vehicle after spline function interpolation processing, and if not, outputting a particle individual extreme value p best And global extreme g best And returning to the step 3.2.
And 6: initializing parameters of the artificial fish swarm algorithm of the mixed particle swarm, including linear decrementMinimum value ω of inertial weight min And maximum value ω max Particle group N, particle view visual, state change step, particle swarm crowding factor delta, random repeat try number, maximum iteration number t max2 。
In the embodiment, the PSO-AFSA algorithm parameters are initialized, the algorithm group scale is 100, and the maximum iteration times t max2 100，c 1 ＝c 2 ＝2，ω＝[0.4，0.9]，try_number＝50，visual＝20，δ＝0.618，step＝5。
And 7: and judging whether the unmanned aerial vehicle encounters sudden threat, if so, updating the next generation of particles through bulletin board updating information, and re-planning the flight path of the unmanned aerial vehicle, otherwise, flying the global static optimal flight path of the unmanned aerial vehicle.
In this embodiment, dynamic emergency threat path re-planning sets emergency threats on a digital map, the emergency threats being weather threats with center coordinates (160, 130, 0), which are detected by the drone when flying near the location.
The step of updating the next generation of particles through the notice board updating information is as follows:
step S1: for the velocity v of the ith particle i And position x i Updating and calculating the fitness value f (x) of the ith particle after updating i ') the state of the least fitness particle and its value are assigned to the bulletin board.
Step S2: the particles are respectively subjected to foraging, rear-end collision, clustering and random behaviors.
Step S2.1: setting the current state of the ith particle to
The next position which is searched randomly within its field of view is @>
The optimal central status of the population is->
Optimized particlesStatus is->
Step S2.2: judging the comprehensive path evaluation function value of the ith particle in the current state of the t generation
Whether or not it is greater than the integrated path merit function ≥ from which the next position is randomly searched for in its field of view>
If yes, then based on the next status/status searched randomly in the visual field of the ith generation of the ith particle>
And a global extreme point in the tth generation->
Updating the ith particle to obtain the t +1 th generation speed V of the ith particle t+1 iD And State X t+1 iD Step S3 is continued, if not, the ith particle is not updated, and step S2.3 is continued.
The method is based on the next position randomly searched in the visual field range of the t generation of the ith particle
And a global extreme point in the tth generation->
Updating the speed V of t +1 generation of ith particle t+1 iD And state X t+1 iD Is represented by the formulas (26) and (27):
wherein the content of the first and second substances,
the speed of the ith particle in the t +1 th generation under the foraging behavior, t is the current iteration number, t max2 Is the maximum number of iterations, r 1 And r 2 Are all in [0,1 ]]In a random number on +>
For the current status of the ith particle in the t +1 th passage under foraging behavior, it is selected>
Is a random behavior.
Step S2.3: judging whether the random search times in the visual field range of the ith generation of particles are greater than the random repeated try times try _ number, if so, randomly flying the particles in the visual field range of the ith generation of particles, if not, randomly searching the next position in the visual field range of the ith generation of particles, and returning to the step S2.2;
step S2.4: judging whether the population state meets the congestion condition, if so, returning to the step S2.2, and if not, searching the optimal central position of the population in the visual field range of the ith generation of the ith particle
And a global extremum point in the tth generation>
Updating the ith particle to obtain the t +1 th generation speed V of the ith particle t+1 iD And state X +1t iD And continuing to step S3.
The formula for judging whether the population state meets the congestion condition is shown as formula (28):
wherein n is f The number of fish groups of the rest artificial fishes in the visual field range of the t generation of the ith particle is shown, if the formula (28) is satisfied, the population state is satisfiedAnd (4) congestion conditions are not met otherwise.
According to the optimal center position of the population searched in the visual field range of the t generation of the ith particle
And a global extreme point in the tth generation->
Updating the speed V of t +1 generation of the ith particle t+1 iD And state X +1t iD Is represented by the formulas (29) and (30):
wherein, the first and the second end of the pipe are connected with each other,
for the speed of the t +1 th generation of the i-th particle in clustering behavior>
Is the current state of the t +1 th generation of the ith particle under the clustering behavior.
Step S2.5: judging the comprehensive path evaluation function value of the ith particle current state
Integrated path evaluation function value whether less than the optimal particle state>
If so, returning to the step S2.2, otherwise, based on the searched optimal particle status->
And a global extremum point in the current iteration state->
Updating the ith particle to obtain the t +1 th generation speed V of the ith particle t+1 iD And State X t+1 iD And continues with step S3.
The optimal particle state is searched according to the search
And a global extremum point in the current iteration state->
Updating the speed V of t +1 generation of ith particle t+1 iD And state X t+1 iD Is represented by the formulas (31) and (32):
wherein the content of the first and second substances,
for the speed of the ith particle in the t +1 th generation in rear-end action, based on the speed of the vehicle>
Is the current state of the t +1 th generation of the ith particle under the rear-end collision behavior.
And step S3: calculating the t +1 generation fitness of the ith particle according to the updating process of the ith particle from the t generation to the t +1 generation, and selecting the t +1 generation speed V of the ith particle corresponding to the minimum value of the fitness in individual behaviors t+1 iD And State X t+1 iD And updating the current state of the ith particle.
And step S4: and judging whether the updated fitness of the ith particle is smaller than the fitness of the particles in the bulletin board, if so, updating the particle state information in the bulletin board, and if not, not updating the particle state information in the bulletin board.
And step 8: judging whether the current iteration times of the artificial fish swarm algorithm of the mixed particle swarm is greater than the maximum iteration times t max2 If yes, drawing a global dynamic optimal flight path of the unmanned aerial vehicle through spline function interpolation processing; if not, returning to the step 7.
In this embodiment, a three-dimensional schematic diagram of path planning by using the PSO-SA and PSO-AFSA algorithms is shown in fig. 8. The unmanned aerial vehicle path planning plan adopting the Particle Swarm Optimization (PSO) algorithm, the artificial fish swarm optimization (AFSA) algorithm and the algorithm used by the invention is shown in figure 9, and as is obvious from figure 9, the method not only can complete the path planning task, but also can plan a precise, short and safe path, and the planning effect is obviously superior to the PSO algorithm and the AFSA algorithm.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art; the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit of the invention which is set forth in the appended claims.
Claims (5)
1. An unmanned aerial vehicle static and dynamic path planning method based on a digital map is characterized by comprising the following steps:
step 1: constructing an unmanned aerial vehicle flight three-dimensional digital map by adopting a digital map technology;
and 2, step: importing an equivalent three-dimensional digital map, setting coordinates of starting and ending positions of unmanned aerial vehicle flight, initializing unmanned aerial vehicle path planning constraint conditions, and performing global static path planning;
the unmanned aerial vehicle path planning constraint condition comprises a minimum flight step length l min Maximum turning angle theta, maximum climbing angle beta, minimum flying height H min ；
And step 3: initializing parameters of the mixed particle swarm simulated annealing algorithm and carrying out the simulation on the ith particle swarmPosition x of the particle i And velocity v i Carrying out random initialization and updating the velocity v of the ith particle i And position x i ；
And 4, step 4: performing an annealing operation with a temperature T = at, where a is a decay factor;
and 5: judging whether the current iteration times of the hybrid particle swarm simulated annealing algorithm is larger than the maximum iteration times t max1 If yes, drawing a global static optimal flight path of the unmanned aerial vehicle after spline function interpolation processing, and if not, outputting a particle individual extreme value p best And global extreme g best Returning to step 3, updating the speed v of the ith particle i And position x i ；
And 6: initializing parameters of the artificial fish swarm algorithm of the mixed particle swarm, including the minimum value omega of the linearly decreasing inertia weight min And maximum value ω max Particle group N, particle view visual, state change step, particle swarm crowding factor delta, random repeat try number, maximum iteration number t max2 ；
And 7: judging whether the unmanned aerial vehicle encounters sudden threat, if so, updating the next generation of particles through bulletin board updating information, and re-planning the flight path of the unmanned aerial vehicle, otherwise, flying according to the global static optimal flight path of the unmanned aerial vehicle;
the steps of updating the next generation of particles by the bulletin board update information are as follows:
step S1: for the velocity v of the ith particle i And position x i Updating, and calculating the fitness value f (x) of the ith particle after updating i ') the state of the particles with the minimum fitness and the value thereof are given to the bulletin board;
step S2: respectively carrying out foraging, rear-end collision, clustering and random behaviors on the particles;
step S2.1: setting the current state of the ith particle to
The next position which is searched randomly within its field of view is @>
The optimal central status of the population is->
Optimum particle status is>
Step S2.2: judging the comprehensive path evaluation function value of the ith particle in the current state of the t generation
Evaluation function for a synthetic path greater than a random search for a next position in the field of view->
If yes, then based on the next state/value searched randomly in the visual field range of the tth generation of the ith particle>
And a global extremum point in the tth generation>
Updating the ith particle to obtain the speed of the ith particle in the t +1 th generation>
And status->
Continuing to the step S3, if not, not updating the ith particle, and continuing to the step S2.3;
the method is based on the next position randomly searched in the visual field range of the t generation of the ith particle
And a global extreme point in the tth generation->
Updating the ith particle to obtain the speed of the ith particle in the t +1 th generation>
And status->
The formula of (1) is as follows:
wherein the content of the first and second substances,
the speed of the ith particle in the t +1 th generation under the foraging behavior, t is the current iteration number, t max2 Is the maximum number of iterations, r 1 And r 2 Are all in [0,1 ]]In a random number on +>
For the current status of the ith particle in the t +1 th generation under foraging behavior>
Is a random behavior;
step S2.3: judging whether the random search times in the visual field range of the tth generation of the ith particle are larger than the random repeated try times try _ number, if so, randomly flying the particle in the visual field range of the ith particle, otherwise, randomly searching the next position in the visual field range of the tth generation of the ith particle, and returning to the step S2.2;
step S2.4: judging whether the population state meets the congestion condition, if so, returning to the step S2.2, and if not, searching the optimal central position of the population in the visual field range of the ith generation of the ith particle
And global extreme points in the t generation
Updating the ith particle to obtain the speed of the ith particle in the t +1 th generation>
And status->
Continuing to step S3;
according to the optimal central position of the population searched in the visual field range of the t generation of the ith particle
And a global extremum point in the tth generation>
The speed at which the t +1 th generation of the i-th particle is updated is taken into account>
And status>
The formula (c) is as follows:
wherein, the first and the second end of the pipe are connected with each other,
for the speed of the t +1 th generation of the i-th particle under clustering behavior, <' > H>
Is the current state of the t +1 th generation of the ith particle under clustering behavior, n f The number of fish groups of the rest artificial fishes in the visual field range of the t generation of the ith particle is shown;
step S2.5: judging the comprehensive path evaluation function value of the ith particle in the current state
Integrated path evaluation function value whether less than the optimal particle state>
If so, returning to the step S2.2, otherwise, based on the searched optimal particle status->
And a global extremum point in the current iteration state->
Updating the ith particle to obtain the speed of the ith particle in the t +1 th generation>
And status>
Continuing to step S3;
according to the searchOptimum particle state
And a global extremum point ≧ in the current iteration state>
Updating a speed in the t +1 th generation of the i-th particle>
And status>
The formula (c) is as follows:
wherein, the first and the second end of the pipe are connected with each other,
for the speed of the ith particle in the t +1 th generation in rear-end action, based on the speed of the vehicle>
The current state of the t +1 th generation of the ith particle under the rear-end collision behavior;
and step S3: calculating the fitness of the ith particle in the t +1 th generation according to the updating process of the ith particle from the t generation to the t +1 th generation, and selecting the speed of the ith particle in the t +1 th generation corresponding to the minimum value of the fitness in the individual behaviors
And status>
Updating the current state of the ith particle;
and step S4: judging whether the fitness of the ith particle after updating is smaller than the fitness of the particles in the bulletin board, if so, updating the particle state information in the bulletin board, and if not, not updating the particle state information in the bulletin board;
and step 8: judging whether the current iteration times of the artificial fish swarm algorithm of the mixed particle swarm is greater than the maximum iteration times t max2 If yes, drawing a global dynamic optimal flight path of the unmanned aerial vehicle through spline function interpolation processing; if not, returning to the step 7.
2. The method for static and dynamic path planning for unmanned aerial vehicle based on digital map according to claim 1, wherein the step 1 comprises the following steps:
step 1.1: constructing a reference topographic map according to a topographic elevation value corresponding to the projection coordinates (x, y) of any point on the three-dimensional digital terrain on a horizontal plane;
the formula for constructing the reference terrain is as follows:
wherein z is 1 (x, y) is the terrain elevation value corresponding to the projection coordinate (x, y) of any point on the three-dimensional digital terrain on the horizontal plane, a 1 ～a 7 All the coefficients are constant coefficients and are used for controlling the fluctuation state of the reference terrain;
step 1.2: constructing a mountain peak topographic map according to the elevation value of the midpoint (x, y) of the digital map;
the formula for constructing the mountain terrain is as follows:
wherein z is 2 (x, y) isThe elevation value of the midpoint (x, y) of the digital map, n is the number of the peaks in the digital map, h m Height of the mth simulated peak, (x) m ，y m ) Is the center coordinate of the mth simulated peak, x sm The attenuation amount of the mth simulated peak in the x-axis direction, y sm The attenuation of the mth simulation peak in the y-axis direction is shown;
step 1.3: the radar working area is equivalent to a hemispherical terrain by adopting the idea of information fusion, and a radar threat model is established;
the formula for establishing the radar threat model is as follows:
wherein z is 3 (x, y) is the radar threat equivalent elevation value at the point (x, y) in the radar threat model, (x 3 ，y 3 ，z 3 ) Is the position coordinate of any point on the three-dimensional digital terrain, r 3 Radius of radar threat range;
step 1.4: simulating an urban building by using a cube, and establishing an urban environment model;
the formula of the urban environment model is as follows:
wherein z is 4 (x, y) is the elevation at the midpoint (x, y) of the city building model, (x 4 ，y 4 ) The central coordinate of the urban environment model, length is the transverse length of the building in the urban environment model, width is the longitudinal width of the building in the urban environment model, and h is the height of the building in the urban environment model;
step 1.5: simulating weather threats by using a cylinder, and establishing a weather threat model;
the formula of the weather threat model is as follows:
wherein z is 5 (x, y) is the elevation at point (x, y) in the weather threat model, r 5 Radius of the weather threat range, (x) 5 ，y 5 ) As the weather threat center coordinate, h w Height of the weather threat model;
step 1.6: converting the established reference topographic map, the peak topographic map, the radar threat model, the urban environment model and the weather threat model into data which can be processed by a computer, and fusing by combining the information fusion principle to form a comprehensive digital topographic information equivalent three-dimensional digital map;
the expression of the integrated digital terrain information equivalent three-dimensional digital map is as follows:
z(x，y)＝max[z 1 (x，y)+z 2 (x，y)+z 3 (x，y)+z 4 (x，y)+z 5 (x，y)]；
wherein z (x, y) is an elevation value at the midpoint (x, y) of the equivalent three-dimensional digital map of the comprehensive digital terrain information.
3. The method for static and dynamic path planning for unmanned aerial vehicle based on digital map according to claim 1, wherein the step 3 comprises the following steps:
step 3.1: initializing parameters of the hybrid particle swarm simulated annealing algorithm, including maximum iteration number t max1 Population size N, initial temperature T, attenuation factor alpha and learning factor c 1 And c 2 And for the position x of the ith particle i And velocity v i Carrying out random initialization;
step 3.2: calculating the fitness value f (x) of the ith particle according to the comprehensive path evaluation function of the unmanned aerial vehicle i ) And determining the individual extreme value p of the particle through the minimum fitness value of the particle best And global extreme g best ；
Step 3.3: using a mode with compression factor to adjust the speed v of the ith particle i And position x i Updating is carried out to obtain updated dataVelocity v of the ith particle of (2) i ' and position x i ′；
Step 3.4: according to the updated velocity v of the ith particle i ' and position x i ', calculating the updated fitness value f (x) of the ith particle i ') and calculating a variable delta f of the ith particle fitness value before and after updating;
step 3.5: judging whether the variable delta f of the ith particle fitness value is larger than 0, if so, continuing the step 3.6, and if not, receiving the updated speed v of the ith particle i ' and position x i ', continue step 4;
step 3.6: judging whether exp (-delta f/T) is larger than random number rand (0, 1), if yes, receiving the updated speed v of the ith particle i ' and position x i ', and updating the fitness value f (x) according to the ith particle i ') update the individual extremum p of the particle best And global extreme g best Continuing to the step 4, if not, not updating the speed v of the ith particle i And position x i And continuing to the step 4.
4. The method for static and dynamic path planning for unmanned aerial vehicle based on digital map as claimed in claim 3, wherein the formula of the synthetic path evaluation function of unmanned aerial vehicle in step 3.2 is as follows:
wherein, C is a path planning objective function, l is a path length, Σ δ w(s) is a synthetic cost of unmanned aerial vehicle flight, and the formula of the synthetic cost Σ δ w(s) of unmanned aerial vehicle flight is as follows:
∑δw(s)＝δ o wo(s)+δ t wt(s)+δ R wR(s)+δ D wD(s)；
wherein wo(s) is the oil consumption cost of the unmanned aerial vehicle, wt(s) is the natural weather threat cost of a flight area, wR(s) is the radar threat cost suffered by the unmanned aerial vehicle, wD(s) is the terrain threat cost, and delta o To the cost of oil consumptionCoefficient of weight, δ t Cost weighting factor, δ, for weather threats R As a radar threat cost weighting factor, δ D A cost weighting factor for the terrain threat, and δ o +δ t +δ R +δ D ＝1。
5. Method for static and dynamic path planning for unmanned aerial vehicle based on digital map according to claim 3, wherein the updated velocity v of the ith particle in step 3.3 i ' and position x i The formula of' is as follows:
v i ′＝χ[v i +c 1 r 1 (p best -x i )+c 2 r 2 (g best -x i )]；
x i ′＝x i +v i ′；
wherein r is 1 And r 2 Are all in [0,1 ]]The random number of (2) is greater than,
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| CN112230678B (en) * | 2020-10-29 | 2023-07-14 | 皖江工学院 | Three-dimensional unmanned aerial vehicle path planning method and system based on particle swarm optimization |
| CN113064450B (en) * | 2021-04-01 | 2022-02-08 | 河海大学 | Quantum particle swarm unmanned aerial vehicle path planning method based on annealing algorithm |
| CN113220008B (en) * | 2021-05-21 | 2022-11-25 | 南京航空航天大学 | Collaborative dynamic path planning method for multi-Mars aircraft |
| 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 |
| CN115639828B (en) * | 2022-12-23 | 2023-03-31 | 河北科技大学 | Robot path planning method and device based on improved artificial fish swarm algorithm |
| CN116518979B (en) * | 2023-06-26 | 2023-08-29 | 深圳市森歌数据技术有限公司 | Unmanned plane path planning method, unmanned plane path planning system, electronic equipment and medium |
| CN116753964B (en) * | 2023-08-21 | 2023-11-28 | 中国人民解放军战略支援部队航天工程大学 | Unmanned aerial vehicle path planning method and system based on influence degree |
| CN117727197B (en) * | 2024-02-07 | 2024-05-03 | 南京信息工程大学 | Vehicle alarm control method and device based on hybrid ant colony algorithm |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104574512A (en) * | 2014-12-29 | 2015-04-29 | 董有福 | Multi-scale DEM (digital elevation model) construction method considering topographical semantic information |
| CN107368075A (en) * | 2017-07-28 | 2017-11-21 | 西北工业大学 | Mobile robot global path planning algorithm based on hybrid particle swarm |
| CN107392388A (en) * | 2017-07-31 | 2017-11-24 | 南昌航空大学 | A kind of method for planning no-manned plane three-dimensional flight path using artificial fish-swarm algorithm is improved |
| CN107577241A (en) * | 2017-07-13 | 2018-01-12 | 西北工业大学 | A kind of fire-fighting unmanned aerial vehicle flight path planing method based on obstacle avoidance system |
| CN107883962A (en) * | 2017-11-08 | 2018-04-06 | 南京航空航天大学 | A kind of dynamic Route planner of multi-rotor unmanned aerial vehicle under three-dimensional environment |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9818303B2 (en) * | 2015-06-16 | 2017-11-14 | Verizon Patent And Licensing Inc. | Dynamic navigation of UAVs using three dimensional network coverage information |
-
2019
- 2019-03-06 CN CN201910167983.0A patent/CN110031004B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104574512A (en) * | 2014-12-29 | 2015-04-29 | 董有福 | Multi-scale DEM (digital elevation model) construction method considering topographical semantic information |
| CN107577241A (en) * | 2017-07-13 | 2018-01-12 | 西北工业大学 | A kind of fire-fighting unmanned aerial vehicle flight path planing method based on obstacle avoidance system |
| CN107368075A (en) * | 2017-07-28 | 2017-11-21 | 西北工业大学 | Mobile robot global path planning algorithm based on hybrid particle swarm |
| CN107392388A (en) * | 2017-07-31 | 2017-11-24 | 南昌航空大学 | A kind of method for planning no-manned plane three-dimensional flight path using artificial fish-swarm algorithm is improved |
| CN107883962A (en) * | 2017-11-08 | 2018-04-06 | 南京航空航天大学 | A kind of dynamic Route planner of multi-rotor unmanned aerial vehicle under three-dimensional environment |
Non-Patent Citations (3)
| Title |
|---|
| "Moving-target search: a real-time search for changing goals";Ishida, T 等;《Pattern Analysis and Machine Intelligence》;19951230;第17卷(第6期);正文第609-618页 * |
| "基于混合鱼群算法的移动机器人路径规划研究";彭凯;《中国优秀硕士学位论文全文数据库 信息科技辑》;20180115(第1期);正文第23-24页 * |
| "基于群体智能算法的无人机航迹规划研究";赵毓;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20170215(第2期);正文第11-37页 * |
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