CN112432649A - Heuristic unmanned aerial vehicle swarm flight path planning method introducing threat factors - Google Patents
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Abstract
The invention discloses a heuristic unmanned aerial vehicle swarm flight path planning method introducing threat factors, which is used for evaluating and quantizing various obstacles in a passing flight area and the severity of enemy threats after setting a starting position and a target position on the premise of acquiring information such as terrain, altitude, path obstacles, meteorological information data, enemy regional air defense deployment and the like, and taking the evaluation as important parameters for calculating a flight path, and applying the evaluation to an improved heuristic A algorithm to finally obtain a flight path result which most accords with enemy and air condition. The beneficial technical effects of the invention are as follows: the method is characterized in that quantitative analysis is carried out on barriers and air defense threats between flying route points of unmanned aerial vehicle swarm formation, the barriers and the air defense threats are used as important reference factors in a flight path searching algorithm, an obtained flight path result not only guarantees the shortest flying path in space, but also can be far away from the threats, benefits are approached, the hazards are avoided, the flying safety rate of the swarm formation is improved, and the probability that the swarm formation is detected by radar or is attacked by firepower is reduced.
Description
Technical Field
The invention relates to the field of unmanned aerial vehicle flight mission planning, in particular to an optimal path calculation planning and generation method under the condition of a known track situation.
Background
The unmanned platform technology is increasingly widely applied nowadays, and the civil field and the military field have urgent application requirements on the unmanned platform. Unmanned platforms represented by unmanned aerial vehicles are gradually entering the field of view of military requirements, undertaking combat missions under scenes where manpower is difficult to reach, serving as important supplements of humanized weapon platforms, and becoming one of the developing directions of emerging military equipment.
Unmanned aerial vehicle has advantages such as with costization, mobility are strong, casualties are little, intelligent autonomy in the aspect of military operation demand, especially in the middle of participating in the joint operation action to the formation is constituteed to unmanned aerial vehicle bee colony, can accomplish the stronger task of multiple cooperativity with high efficiency. Task planning control is one of the core technologies of an unmanned aerial vehicle, and comprises pre-planning and setting before the unmanned aerial vehicle executes various tasks. The flight path of the unmanned aerial vehicle is an important aspect of mission planning, and the calculation and selection of the flight path are called unmanned aerial vehicle track planning.
The flight path planning technology is a technology for planning a flight path and a flight route of an unmanned aerial vehicle in advance by adopting a corresponding calculation method and synthesizing various constraint conditions after acquiring the information of the position of an enemy target and the general deployment of the air defense force in advance through reconnaissance in the known longitude, latitude and altitude of a flight area. A calculation algorithm applied to the flight path planning technology is called a flight path planning algorithm, and a common flight path planning algorithm includes: voronoi algorithm, Dijkstra algorithm, a-x algorithm, ant colony algorithm, particle algorithm. In which heuristic factors are designed during the iterative computation of the a-algorithm, and therefore this type of algorithm is also referred to as a heuristic algorithm. The idea of the heuristic algorithm is to deduce the path in a stepping mode, divide the path by discrete coordinate points, calculate the estimated cost value of the distance from the destination point at the stepping point in each direction, add the estimated cost value to the historical cost, and then select the direction with the minimum value to evolve until the destination point is reached. The heuristic algorithm is to calculate by taking the shortest path as a target, and each step of discrete coordinate point is the shortest path from the destination point within the reachable range. A heuristic algorithm is a typical method for calculating a planned flight path by combining historical data and future estimation values. The algorithm can find the shortest path relatively quickly, but the optimal solution of the flight path cannot be obtained necessarily. Due to the change and randomness of battlefield situations, the threat condition of a target area needs to be considered in the calculation of the flight path, and the threat condition needs to be considered as an important factor in the calculation and planning process, so that the flight path planning algorithm based on the target threat factor is provided as a starting point to obtain the flight path which is more accurate and enables the survival rate of the unmanned aerial vehicle swarm formation to be higher.
Disclosure of Invention
The invention discloses a heuristic unmanned aerial vehicle swarm flight path planning method introducing threat factors, which is used for evaluating and quantizing various obstacles in a passing flight area and the severity of enemy threats after setting a starting position and a target position on the premise of acquiring information such as terrain, altitude, path obstacles, meteorological information data, enemy regional air defense deployment and the like, and taking the evaluation as important parameters for calculating a flight path, and applying the evaluation to an improved heuristic A algorithm to finally obtain a flight path result which most accords with enemy and air condition.
The algorithm adopted by the invention comprises the following steps:
1) firstly, acquiring geographic information of a formation flight area of an unmanned aerial vehicle swarm by utilizing reconnaissance platforms such as a radar, an early warning machine and a sensor, and mainly marking approximate position coordinates of mountains, meteorological clouds and enemy air defense force around a starting and stopping point of a flight line;
2) setting the position coordinates as elements in a close table according to the obtained coordinate positions of the obstacles and the threat area, and indicating that the coordinate point is a track unreachable point;
3) designing a path cost function f (x) = g (x) + h (x), wherein f (x) represents a path cost estimated value finally reaching an end point through a stepping track point x, and the path cost estimated value is related to path length, a heuristic function h (x) and an obstacle threat factor; g (x) represents the historical cost paid from the initial path point to the current pass-through point x; h (x) is a heuristic function, represents minimum path cost estimation from a current track point x to a target point position, and the design and selection of h (x) function determine the efficiency of A-heuristic algorithm for searching a path and the degree of approaching to an optimal path;
4) taking the starting position of the bee colony as an algorithm starting point, searching a cost function of a next-step carry position track point according to the azimuth, selecting a point with the minimum cost as a next path point, placing the point in an open list, and simultaneously taking the current node as a father node of the next path point and placing the father node in a route list; if the search node of the next hop enters an obstacle or threat control area, placing the node on a close list and showing the node as an unreachable node;
5) selecting the next path point in the open list as the current node, repeating the azimuth search, if the searched node exists in the open list, comparing the f (x) function obtained by the latest calculation with the f (x) function before the node, and if the cost is smaller, not processing; if the new f (x) cost is smaller, updating the cost value, setting the previous path point as a new father node, and placing the new father node in a route list;
6) repeatedly executing the step 4) and the step 5) until the search path point reaches the target point;
7) and reversely backtracking all the father node coordinates from the target point to the starting point to form a complete track path.
Drawings
FIG. 1 is a schematic diagram of a simulation of a flight path scene of a swarm of unmanned aerial vehicles;
FIG. 2 is a schematic diagram of a track search simulation based on a shortest path;
FIG. 3 is a schematic diagram of a flight path search simulation based on threat factors.
Detailed Description
The invention provides an unmanned aerial vehicle swarm flight path planning method based on a heuristic algorithm, which is different from other flight path planning methods and is characterized in that barriers or enemy threats in a formation flight area of an unmanned aerial vehicle swarm are deployed and introduced into a cost algorithm in a quantitative mode, so that the optimal balance of a flight path searched by the method is obtained in a constraint condition of minimum flight cost and maximum flight safety.
The embodiments of the present invention will now be described in further detail. The method mainly comprises the following steps:
1) before implementing the flight path planning, determining the longitude and latitude coordinates of the positions of a starting point and a flight destination of the swarm formation of the unmanned aerial vehicle, and acquiring the positions of obstacles and enemy threat force in a flight path through reconnaissance and terrain observation, wherein a scene schematic diagram is shown in fig. 1;
2) drawing a flight track map by taking longitude and latitude as a coordinate system according to the acquired coordinates of the barrier and the threat area, and setting the barrier and the threat area as track unreachable points;
3) generating three lists, namely an open list, a close list and a route list, for storing longitude and latitude coordinates of the searched route points in a classified manner, wherein the open list is used for storing the minimum cost value corresponding to the coordinates of the reachable route reference point and the coordinates of the reachable route reference point after traversing search is finished, the close list is used for storing the nodes of the searched historical route, each node in the list is the optimal route point coordinate selected after each search and the corresponding cost function value, and the route list represents the father node of each selected optimal route point;
4) designing a heuristic A arithmetic operator, firstly establishing h (x) by using a Manhattan distance or a Euclidean distance, representing the estimated distance from the x point to the target point, and g (x) setting a path cost function value reaching the x point through a previous path point, namely a parent node of the x, wherein the f function value is expressed as the sum of the f function value of the parent node and the geometric distance from the parent node to the x point, so that f (x) = g (x) + h (x) of the x point is obtained and represents the estimated distance cost value from the x point to the target point. On the basis of the operator, by applying to A-star algorithm iteration, a track search result based on the shortest path condition can be calculated, and a simulation result is shown in FIG. 2;
5) weighting by using threat factors on the basis of A-x algorithm, wherein the threat factors are defined as quantifying the threat degree of formation of a swarm in the control range of an obstacle or enemy air defense radar system, setting the weight t (x) after the addition of the distance between the x point and the corresponding threat source and the threat degree, and weighting f (x) by using t (x) value to obtain the actual path cost value f based on the threat factorst(x)；
6) Step 4) and step 5) are executed in a circulating way, and f in the same group of stepping nodes is selected after each searcht(x) Taking the minimum as a next step-in node, recording a father node of the point into a route list, and gradually searching until the current node x is completely coincided with the position of the target point;
7) and sorting the father node sequences in the finished route list route, reversely tracing according to the longitude and latitude coordinates, and tracing a route calculated by an algorithm, namely the optimal route planning route calculated by the method, wherein the simulation result is shown in fig. 3.
In the step 1), the purpose of implementing the flight path planning is to calculate an optimal flight path for a flight mission executed by the formation of the swarm of the unmanned aerial vehicles or other combat missions, so that reconnaissance information and terrain information are provided for other combat forces needing to be coordinated, and the reconnaissance information and the terrain information are used as accurate data support for modeling of a flight path planning scene.
In the step 2), the map modeling takes two-dimensional coordinates of geographical longitude and latitude as a scale, the smaller the interval of sampling points is, the higher the searching precision is, the clearer the control range of the edge of the obstacle and the threat area is, and the higher the precision of the planned track route is.
In the step 3), before the search path starts, the storage list is initialized, and the storage list is classified into an open list, a close list and a route list according to the attributes obtained in the process of searching the pixel coordinates of the map, wherein each time a coordinate point is searched, which list the point belongs to can be obtained, and one coordinate can only be uniquely subordinate to one list. If the search node coincides with an obstacle or threat zone, it is classified in a close list as an unreachable node; otherwise, the list is classified into an open list, and the next hop is regarded as reachable; after the next hop path node is selected, the current node coordinates are attributed to the route list, considered as the parent node.
In the step 4), the calculation of h (x) may be performed by manhattan distance measurement or euclidean distance. The Manhattan distance between two places is the sum of absolute values of the difference between longitude distances and the difference between latitude distances, and is expressed by formula
The Euclidean distance is the straight-line distance between two places, and the formula is expressed as
One of the two distance estimation algorithms can be selected optionally, and multiplication weighting can be performed according to the calculation quantification requirement. The next hop node of the current node is determined by eight orientations in two-dimensional coordinates, and g (x) is calculated from the actual distance between two points.
In the step 5), after the point x is searched and f (x) is calculated, the threat state of the point x is further calculated, specifically, the shortest distance from the point x to the obstacle and the threat target near the flight path is calculated, the threat coefficients are divided by the distance and then accumulated respectively to obtain the threat factor t (x) of the point x, and f is enabled to be calculatedt(x)=f(x)*t(x)。
In the step 6), each search is performed by taking the current node as the center, diverging the current node to 8 points around the 3 × 3 matrix pixel, calculating the a × function value of the 8 points, and selecting the smallest one as the next track jump point. Before threat factors are introduced, the result of the algorithm is a simple physical shortest path, the difference and the division of obstacles and threat strength in flying passing points are relatively deficient, and the track result is not completely optimal when the method is applied to the actual battlefield environment. Therefore, the method carries out threat quantification on various barriers and air defense deployments, obtains the threat coefficient of each search node after weighting, and finally selects ft(x) The point of the minimum value is the next trace point.
In the step 7), after the track path is obtained through searching, an interpolation smoothing method is adopted for processing, and a track route suitable for the flight curvature of the unmanned aerial vehicle is obtained.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (8)
1. A heuristic unmanned plane swarm flight path planning method introducing threat factors is characterized by comprising the following steps:
1) firstly, acquiring geographic information of a formation flight area of an unmanned aerial vehicle swarm by utilizing reconnaissance platforms such as a radar, an early warning machine and a sensor, and mainly marking approximate position coordinates of mountains, meteorological clouds and enemy air defense force around a starting and stopping point of a flight line;
2) setting the position coordinates as elements in a close table according to the obtained coordinate positions of the obstacles and the threat area, and indicating that the coordinate point is a track unreachable point;
3) designing a path cost function f (x) = g (x) + h (x), wherein f (x) represents a path cost estimated value finally reaching an end point through a stepping track point x, and the path cost estimated value is related to path length, a heuristic function h (x) and an obstacle threat factor; g (x) represents the historical cost paid from the initial path point to the current pass-through point x; h (x) is a heuristic function, represents minimum path cost estimation from a current track point x to a target point position, and the design and selection of h (x) function determine the efficiency of A-heuristic algorithm for searching a path and the degree of approaching to an optimal path;
4) taking the starting position of the bee colony as an algorithm starting point, searching a cost function of a next-step carry position track point according to the azimuth, selecting a point with the minimum cost as a next path point, placing the point in an open list, and simultaneously taking the current node as a father node of the next path point and placing the father node in a route list; if the search node of the next hop enters an obstacle or threat control area, placing the node on a close list and showing the node as an unreachable node;
5) selecting the next path point in the open list as the current node, repeating the azimuth search, if the searched node exists in the open list, comparing the f (x) function obtained by the latest calculation with the f (x) function before the node, and if the cost is smaller, not processing; if the new f (x) cost is smaller, updating the cost value, setting the previous path point as a new father node, and placing the new father node in a route list;
6) repeatedly executing the step 4) and the step 5) until the search path point reaches the target point;
7) and reversely backtracking all the father node coordinates from the target point to the starting point to form a complete track path.
2. The heuristic unmanned aerial vehicle swarm flight path planning method for introducing threat factors according to claim 1, wherein the step 1) comprises: the purpose of implementing the track planning is to calculate the optimal flight path for the flight mission executed by the formation of the unmanned aerial vehicle swarm or other combat missions, so that reconnaissance information and terrain information are provided for other combat forces in cooperation as accurate data support for modeling of a track planning scene.
3. The heuristic unmanned aerial vehicle swarm flight path planning method for introducing threat factors according to claim 1, wherein the step 2) comprises: the map modeling takes two-dimensional coordinates of geographical longitude and latitude as a scale, the smaller the interval of sampling points is, the higher the searching precision is, the clearer the control range of the edge of the barrier and the threat area is, and the higher the precision of the planned track route is.
4. The heuristic unmanned aerial vehicle swarm flight path planning method for introducing threat factors according to claim 1, wherein the step 3) comprises: before a search path is started, initializing a storage list, classifying the storage list into an open list, a close list and a route list according to the calculated attributes in the process of searching the pixel coordinates of the map, wherein each time a coordinate point is searched, which list the point belongs to can be obtained, and one coordinate can only be uniquely subordinate to one list; if the search node coincides with an obstacle or threat zone, it is classified in a close list as an unreachable node; otherwise, the list is classified into an open list, and the next hop is regarded as reachable; after the next hop path node is selected, the current node coordinates are attributed to the route list, considered as the parent node.
5. The heuristic unmanned aerial vehicle swarm flight path planning method for introducing threat factors according to claim 1, wherein the step 4) comprises: the calculation of h (x) may select manhattan distance measurement or euclidean distance; the manhattan distance between two places refers to the sum of absolute values of the difference between longitude distances and the difference between latitude distances of the two places; and euclidean distance refers to the straight line distance between two places; one of the two distance estimation algorithms can be selected optionally, multiplication weighting can be carried out according to the calculation quantification requirement, the next hop node of the current node is determined by eight directions of two-dimensional coordinates, and g (x) is calculated according to the actual distance between the two points.
6. The heuristic drone swarm flight path planning method introducing threat factors according to claim 1, characterized in that the step 5) comprises: after searching to point x and calculating f (x), further calculating the threat state of point x, specifically calculating the respective shortest distances from point x to obstacles and threat targets near the flight path, dividing the threat coefficients by the distances, accumulating the distances to obtain the threat factor t (x) of point x, and making ft(x)=f(x)*t(x)。
7. The heuristic drone swarm flight path planning method introducing threat factors according to claim 1, characterized in that the step 6) comprises: in each search, the current node is used as the center, divergence is respectively carried out on 8 points around the 3 x 3 matrix pixel, A x function values of the 8 points are calculated, and the smallest one is selected as a next track jumping point; before threat factors are introduced, the result of the algorithm is that the shortest path can be reached through a pure physical mode, and the difference division of obstacles and threat force in flying passing points is relatively highThe method is not sufficient, and the track result is not completely optimal when the method is applied to the actual battlefield environment; therefore, the method carries out threat quantification on various barriers and air defense deployments, obtains the threat coefficient of each search node after weighting, and finally selects ft(x) The point where the minimum value is located is the next trace point.
8. The heuristic drone swarm flight path planning method introducing threat factors according to claim 1, characterized in that the step 7) comprises: after the track path is obtained through searching, an interpolation smoothing method is adopted for processing, and a track route suitable for the flight curvature of the unmanned aerial vehicle is obtained.
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