CN111427378B - Method for planning preferential rescue path of unmanned aerial vehicle in mountainous region - Google Patents
Method for planning preferential rescue path of unmanned aerial vehicle in mountainous region Download PDFInfo
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Abstract
The invention discloses a method for planning a preferential rescue path of an unmanned aerial vehicle in mountainous regions, which comprises the following steps: screening out edges L of the Thiessen polygons meeting the safety degree constraint condition from feasible solutions of the target position ij (ii) a Acquiring the type of emergency needing rescue, including the number g of people at a target rescue point and the mountain danger degree h of the target rescue point; construction and threat degree u n Associated target priority coefficient T, at edge L ij And then, taking the target point with the highest target priority coefficient T as a starting point, taking the starting point as the starting point of the ant colony, solving the moving path of a second target point with the highest target priority coefficient T through an improved ant colony algorithm, then taking the second target point as a new starting point, repeatedly operating the improved ant colony algorithm to solve the moving path of a third target point with the highest target priority coefficient T until all the target points are passed through, and obtaining a total moving path as a priority rescue path. The path of the invention can quickly find out the critical target with high threat degree for first rescue.
Description
Technical Field
The invention relates to the field of unmanned aerial vehicle application. More specifically, the invention relates to a method for planning a preferential rescue path of an unmanned aerial vehicle in a mountain land.
Background
In recent years, an unmanned aerial vehicle is taken as a novel application platform, the unmanned aerial vehicle is widely applied to military and civil fields such as reconnaissance and environment due to the characteristics of small size, strong maneuverability, flexible operation, low cost and the like, and the unmanned aerial vehicle has the greatest advantages of greatly prolonging the sailing time, executing more dangerous tasks, reducing the danger coefficient of personnel and saving the cost on the premise of driving for a longer voyage and having enough power; therefore, the unmanned aerial vehicle has a very wide application prospect in the search and rescue task of the personnel.
The existing unmanned aerial vehicle faces a plurality of problems to be solved urgently in executing search and rescue tasks of personnel, and the problem of path planning is the first direct rushing. Aiming at the problem of unmanned aerial vehicle path planning, in the prior art, tang Li and the like disclose a mountain unmanned aerial vehicle path planning method based on an improved ant colony algorithm in the 19 th volume of traffic transportation system engineering and information, no. 1, and the feasible path formed by Thiessen polygons is used as an initial solution of the ant colony algorithm, so that the computation amount is greatly reduced, the algorithm efficiency is improved, and the rescue efficiency of the unmanned aerial vehicle is improved.
Although the unmanned aerial vehicle path planning method of Tang Li and the like can limit the path and direction of ants searching along the edges of the Thiessen polygon by using a feasible path formed by the Thiessen polygon as an initial solution of the ant colony algorithm, and ant k determines the next target point according to the cost of the path and the pheromone concentration, the method has the advantages of reducing the computation workload and improving the convergence speed of the algorithm, but the ant colony leaves pheromone lack of guidance, so that the convergence direction of the algorithm cannot be converged according to the required direction, rescue can only be searched according to the direction of the shortest path, and no rescue expert analyzes the dangerous situation, and thus preferential rescue cannot be performed on the dangerous rescue target.
Therefore, people now devote to research and develop a method to enable the unmanned aerial vehicle to have the analysis level of rescue experts, when rescue data is faced, people do not need to operate and control personnel, and the unmanned aerial vehicle has certain strain capacity.
Disclosure of Invention
The invention aims to provide a mountain area priority rescue path planning method for an unmanned aerial vehicle, which aims at the problem that the unmanned aerial vehicle path planning in the prior art cannot execute priority rescue, and provides a method for searching a next target point with the highest target priority coefficient T by taking a target point with the highest target priority coefficient T as a starting point, so that a rescue path can more quickly find emergency target rescue with high threat degree from the target with the highest priority coefficient T through improved ant colony algorithm search. Meanwhile, the optimal comprehensive index is used as the pheromone concentration increment and movement guide of the ant colony algorithm, so that the ant colony converges towards the direction of the optimal comprehensive index, and the ant colony is guided to select towards the path of the optimal comprehensive index, and therefore the rescue efficiency and accuracy of the target with high threat degree can be improved.
To achieve these objects and other advantages in accordance with the purpose of the invention, there is provided an unmanned aerial vehicle mountain first rescue path planning method, including:
acquiring a target position to be rescued, and generating a line segment of a Thiessen polygon in a target position range based on obstacle points to serve as an initial flight path feasible solution;
constructing a path safety degree constraint condition, and screening out the side L of the Thiessen polygon meeting the safety degree constraint condition from feasible solutions ij Edge L ij The edge between adjacent vertexes i and j of the Thiessen polygon is shown, wherein i and j are two position points respectively;
further comprising:
acquiring the type of emergency needing rescue, wherein the type of emergency comprises the number g of people at a target rescue point and the mountain danger degree h of the target rescue point;
construction and threat degree u n Associated target priority coefficient T, at edge L ij Taking the target point with the highest target priority coefficient T as a starting point, taking the starting point as the starting point of the ant colony, solving the moving path of a second target point with the highest target priority coefficient T through an improved ant colony algorithm, then taking the second target point as a new starting point, repeatedly operating the improved ant colony algorithm to solve the moving path of a third target point with the highest target priority coefficient T until all the target points are passed through, and obtaining a total moving path as a priority rescue path;
wherein the degree of threat u n The threat degree u is related to the number g of people at the target rescue point and the mountain danger degree h of the target rescue point n Represents the threat degree of the target point n, the threat degree u n The value is positive, the larger the value is, the more priority is given to rescue plans, the number g of people at a target rescue point is an integer greater than or equal to 0, and the value of the mountain danger degree h is between 0 and 1.
Preferably, the construction target priority coefficient is:
T＝W g ×g+W h ×h；
the threat degree u n The relation function with the target priority coefficient T is constructed as follows:
F(u n )＝u n 2 [(W g +W h )|T-1| q ] 2/q +(1-u n ) 2 [(W g +W h )T q ] 2/q
in that
When, is greater or less>
Wherein, W g A specific gravity, W, representing the obstacle avoidance distance from the feasible point to be selected h A specific gravity representing the required ability to reach the feasible point to be selected, q is a distance parameter, the value is a positive number, W g The value of (3) is between 0 and 1, the larger the specific gravity value is, the better the obstacle avoidance effect is, W h The value of (2) is between 0 and 1, and the larger the specific gravity value is, the more dangerous the mountain is; the target priority coefficient T is greater than 0, and the larger the value is, the more priority the rescue plan is to consider;
preferably, the method further comprises the following steps:
acquiring the killing probability p of the disaster m to the target point n mn Parameter x of damage to target point n caused by disaster m mn And threat degree u of target point n n ；
Construction of a comprehensive index s within the target Point Range mn Comprises the following steps:
s mn ＝u n p mn x mn ；
the optimal comprehensive indexes in the target range are as follows:
wherein s is mn The comprehensive index of the target point n is obtained from the disaster m and the current situation of the unmanned aerial vehicle; the target point n corresponds to the disaster m one by one, the values of r and b do not represent the sizes of the target point n and the disaster m and only represent one label, m and n are respectively valued from 1, r and b are the final values of m and n, and p mn 、x mn The values are all between 0 and 1;
in the improved ant colony algorithm, an optimal comprehensive index S between the distances from a position point i to a position point j at a time t is obtained ij For comparing the probability of transition from location point i to location point j at time t with the current optimal composite indicator S ij Correlation, and the kth ant in the ant colony left at the edge L during the search ij Concentration of the above pheromone and the current optimal comprehensive index S ij Is in positive correlation.
Preferably, the probability of transitioning from location point i to location point j at time t is:
wherein, alpha is an information heuristic factor and represents the importance degree of the pheromone; tau is ij (t) is the edge L at time y ij Beta is an expected heuristic factor representing the relative importance of the visibility heuristic, eta ij Is a heuristic function of visibility, representing the visibility of the path (i, j) ij ＝1/d ij Wherein d is ij Is the distance from point i to point j;
as time t progresses, edge L ij The pheromones in (A) are as follows:
τ ij (t+1)＝(1-ρ)τ ij (t)+Δτ ij
wherein t +1 represents that the time advances to the next iteration, and the physical quantity at the moment t +1 is represented by the sum of the moment t and the time consumed by the previous iteration; rho is the evaporation coefficient of pheromone concentration, and the value interval of rho is as follows: rho epsilon (0,1); 1-rho is a pheromone concentration residual coefficient; delta tau ij The increase of the pheromone concentration is calculated by the following formula:
wherein K is the number of ants (unmanned aerial vehicles), delta tau ij k For the kth ant (unmanned aerial vehicle) to stay at the side L during searching ij The pheromone concentration in the above step is calculated in the following way:
wherein l km Labeled as the path length traveled by ant k in the search of the mth iteration.
G, h, q, W in the invention g 、W h 、p mn 、x mn The parameters are specifically set according to actual conditions, and can be generally set according to expert experience.
The invention at least comprises the following beneficial effects:
1. according to the invention, the target point with the highest target priority coefficient T is used as the starting point, the target point with the highest target priority coefficient T is searched for the next target, so that the rescue path starts from the target with the highest priority coefficient T, and the improved ant colony algorithm is used for searching, so that the emergency target with high threat degree can be found out for first rescue more quickly.
2. According to the method, the optimal comprehensive index is used as the guidance of pheromone concentration increment and movement of the ant colony algorithm, so that the ant colony converges towards the direction of the optimal comprehensive index, and the ant colony is guided to select towards the path of the optimal comprehensive index, and therefore the rescue efficiency and accuracy of the target with high threat degree can be improved.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.
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FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
It will be understood that terms such as "having," "including," and "comprising," as used herein, do not preclude the presence or addition of one or more other elements or groups thereof.
In one technical scheme, the method for planning the preferential rescue path of the unmanned aerial vehicle in the mountainous region comprises the following steps:
acquiring a target position to be rescued, and generating a line segment of a Thiessen polygon in a target position range based on obstacle points to serve as an initial flight path feasible solution;
constructing a path safety degree constraint condition, and screening the edges L of the Thiessen polygons meeting the safety degree constraint condition for feasible solutions ij Edge L ij The edge between adjacent vertexes i and j of the Thiessen polygon is shown, wherein i and j are two position points respectively;
acquiring an emergency type needing rescue, wherein the emergency type comprises the number g of people at a target rescue point and the mountain danger degree h of the target rescue point;
construction and threat degree u n The associated target priority coefficient T is given to,
the target priority coefficient T is:
T＝W g ×g+W h ×h；
the degree of threat u n The relation function with the target priority coefficient T is constructed as follows:
F(u n )＝u n 2 [(W g +W h )|T-1| q ] 2/q +(1-u n ) 2 [(W g +W h )T q ] 2/q
in that
When, is greater or less>
At the edge L ij Taking the target point with the highest target priority coefficient T as a starting point, taking the starting point as the starting point of the ant colony, solving the moving path of a second target point with the highest target priority coefficient T through an improved ant colony algorithm, then taking the second target point as a new starting point, repeatedly operating the improved ant colony algorithm to solve the moving path of a third target point with the highest target priority coefficient T until all the target points are passed through, and obtaining a total moving path as a priority rescue path;
wherein the degree of threat u n The threat degree u is related to the number g of people at the target rescue point and the mountain danger degree h of the target rescue point n Represents the threat degree of the target point n, the threat degree u n Taking the value as a positive numberThe larger the distance is, the more priority is given to the rescue plan, the number g of people at the target rescue point is an integer greater than or equal to 0, the mountain danger degree h is between 0 and 1, and W is g A specific gravity, W, representing the obstacle avoidance distance from the feasible point to be selected h A specific gravity representing the required ability to reach the feasible point to be selected, q is a distance parameter, the value is a positive number, W g The value of (A) is between 0 and 1, the larger the specific gravity value is, the better the obstacle avoidance effect is, W h The value of (2) is between 0 and 1, and the larger the specific gravity value is, the more dangerous the mountain is; the target priority coefficient T is larger than 0, and the larger the value is, the more priority the rescue plan is.
In another technical scheme, referring to a flow diagram of fig. 1, the method for planning the preferential rescue path of the unmanned aerial vehicle in the mountainous region comprises the following steps:
acquiring a target position to be rescued according to the distributed rescue task, and generating a Thiessen polygon segment in the target position range based on the obstacle point to serve as an initial flight path feasible solution;
constructing a path safety degree constraint condition, and screening the edges L of the Thiessen polygons meeting the safety degree constraint condition for feasible solutions ij Edge L ij The edge between adjacent vertexes i and j of the Thiessen polygon is shown, wherein i and j are two position points respectively;
the method for establishing the path security degree constraint condition specifically comprises the following steps: assume that the total path consists of N line segments, each line segment L ij The shortest distance to the center of the obstacle point is d ij Solving the total safety degree of the total path by a safety degree constraint condition formula to obtain the total safety degree Q of the total path; the safety degree constraint condition formula is as follows:
acquiring an emergency type needing rescue, wherein the emergency type comprises the number g of people at a target rescue point and the mountain danger degree h of the target rescue point;
construction and threat degree u n The associated target priority coefficient T is given to,
the target priority coefficient T is:
T＝W g ×g+W h ×h；
the threat degree u n The relation function with the target priority coefficient T is constructed as follows:
F(u n )＝u n 2 [(W g +W h )|T-1| q ] 2/q +(1-u n ) 2 [(W g +W h )T q ] 2/9
in that
When, is greater or less>
Acquiring the killing probability p of the disaster m to the target point n mn Parameter x of damage to target point n caused by disaster m mn And threat degree u of target point n n ；
Construction of a comprehensive index s within the target Point Range mn Comprises the following steps:
s mn ＝u n p mn x mn ；
the optimal comprehensive indexes in the target range are as follows:
wherein the degree of threat u n The threat degree u is related to the number g of people at the target rescue point and the mountain danger degree h of the target rescue point n Representing the degree of threat of target point n, degree of threat u n The value is positive, the larger the value is, the rescue plan is considered preferentially, the number g of people at the target rescue point is an integer larger than or equal to 0, the mountain danger degree h is between 0 and 1, and W is g A specific gravity, W, representing the obstacle avoidance distance from the feasible point to be selected h A specific gravity representing the required ability to reach the feasible point to be selected, q is a distance parameter, the value is a positive number, W g The value of (A) is between 0 and 1, the larger the specific gravity value is, the better the obstacle avoidance effect is, W h The value of (2) is between 0 and 1, and the larger the specific gravity value is, the more dangerous the mountain is; the target priority coefficient T is largeAt 0, the larger the value is, the more priority is given to the rescue plan; s mn The comprehensive index of the target point n is obtained from the disaster m and the current situation of the unmanned aerial vehicle; the target point n corresponds to the disaster m one by one, the values of r and b do not represent the sizes of the target point n and the disaster m and only represent one label, m and n are respectively valued from 1, r and b are the final values of m and n, and p mn 、x mn The values are all between 0 and 1.
At the edge L ij Taking the target point with the highest target priority coefficient T as a starting point, taking the starting point as the starting point of the ant colony, and solving the moving path of a second target point with the highest target priority coefficient T through an improved ant colony algorithm; in the improved ant colony algorithm, the solution of the target points meets the optimal comprehensive index S ij The rule of (2): obtaining an optimal synthetic index S between the distances from the position point i to the position point j at the time t ij For comparing the probability of transition from location point i to location point j at time t with the current optimal composite indicator S ij Correlation, and the kth ant in the ant colony left at the edge L during the search ij Concentration of the above pheromone and the current optimal comprehensive index S ij Is in positive correlation;
according to edge L ij Concentration of pheromone τ of ij (t) rules and optimal composite index S ij And (3) taking the second target point as a new starting point, repeatedly operating the improved ant colony algorithm to solve the moving path of a third target point with the highest target priority coefficient T until all the target points are passed through, and obtaining a total moving path as a priority rescue path.
Side L ij Concentration of pheromone τ of ij The rule for (t) is:
the probability of transitioning from point i to point j at time t is:
wherein, alpha is an information heuristic factor and represents the importance degree of the pheromone; tau is ij (t) is the time limit L at time t ij With β being a desired heuristic factorRepresenting the relative importance of the visibility heuristic, η ij Is a heuristic function of visibility, used to represent the visibility of the path (i, j), η ij ＝1/d ij Wherein d is ij Is the distance from point i to point j;
with the lapse of time t, the edge L ij The pheromones above are:
τ ij (t+1)＝(1-ρ)τ ij (y)+Δτ ij
wherein t +1 represents that the time advances to the next iteration, and the physical quantity at the moment t +1 is represented by the sum of the moment t and the time consumed by the previous iteration; rho is the evaporation coefficient of pheromone concentration, and the value interval of rho is as follows: rho epsilon (0,1); 1-rho is a pheromone concentration residual coefficient; delta tau ij The increase of pheromone concentration is calculated by the formula:
wherein K is the number of ants (unmanned aerial vehicles), delta tau ij k For the kth ant (unmanned aerial vehicle) to stay at the edge L during searching ij The pheromone concentration in the above step is calculated in the following way:
wherein l km Labeled as the path length traveled by ant k in the search of the mth iteration.
G, h, q, W in the invention g 、W h 、p mn 、x mn The parameters are specifically set according to actual conditions, and can be generally set according to expert experience.
While embodiments of the invention have been described above, it is not intended to be limited to the details shown, described and illustrated herein, but is to be accorded the widest scope consistent with the principles and novel features herein disclosed, and to such extent that such modifications are readily available to those skilled in the art, and it is not intended to be limited to the details shown and described herein without departing from the general concept as defined by the appended claims and their equivalents.
Claims (3)
1. An unmanned aerial vehicle mountain region priority rescue path planning method comprises the following steps:
acquiring a target position to be rescued, and generating a line segment of a Thiessen polygon in a target position range based on obstacle points to serve as an initial flight path feasible solution;
constructing a path safety degree constraint condition, and screening the edges L of the Thiessen polygons meeting the safety degree constraint condition for feasible solutions ij Edge L ij The edge between adjacent vertexes i and j of the Thiessen polygon is shown, wherein i and j are two position points respectively;
it is characterized by also comprising:
acquiring the type of emergency needing rescue, wherein the type of emergency comprises the number g of people at a target rescue point and the mountain danger degree h of the target rescue point;
construction and threat degree u n Associated target priority coefficient T, at edge L ij Taking the target point with the highest target priority coefficient T as a starting point, taking the starting point as the starting point of the ant colony, solving the moving path of a second target point with the highest target priority coefficient T through an improved ant colony algorithm, then taking the second target point as a new starting point, repeatedly operating the improved ant colony algorithm to solve the moving path of a third target point with the highest target priority coefficient T until all the target points are passed through, and obtaining a total moving path as a priority rescue path;
wherein the degree of threat u n The threat degree u is related to the number g of people at the target rescue point and the mountain danger degree h of the target rescue point n Representing the degree of threat of target point n, degree of threat u n The value is positive number, the number g of people at the target rescue point is an integer greater than or equal to 0, and the value of the mountain danger degree h is between 0 and 1;
wherein the construction target priority coefficient is:
T＝W g ×g+W h ×h；
the degree of threat u n The relation function with the target priority coefficient T is constructed as follows:
F(u n )＝u n 2 [(W g +W h )|T-1| q ] 2/q +(1-u n ) 2 [(W g +W h )T q ] 2/q
in that
When, is greater or less>
Wherein, W g A specific gravity, W, representing the obstacle avoidance distance from the feasible point to be selected h A specific gravity representing the required ability to reach the feasible point to be selected, q is a distance parameter, the value is a positive number, W g Is between 0 and 1, W h Is between 0 and 1.
2. The unmanned aerial vehicle mountain land priority rescue path planning method as claimed in claim 1, further comprising:
acquiring the killing probability p of the disaster m to the target point n mn Parameter x of damage to target point n caused by disaster m mn And threat degree u of target point n n ；
Construction of a composite index s within the target Point Range mn Comprises the following steps:
s mn ＝u n p mn x mn ；
the optimal comprehensive indexes in the target range are as follows:
wherein s is mn The comprehensive index of the target point n is obtained from the disaster m and the current situation of the unmanned aerial vehicle; the target point n corresponds to the disaster m one to one, the values of r and b do not represent the sizes of the target point n and the disaster m, and only represent one label, m and n respectively begin to be valued from 1, r and b are final values of m and n, and p mn 、x mn The values are all between 0 and 1;
in the improved ant colony algorithm, an optimal comprehensive index S between the distances from a position point i to a position point j at a time t is obtained ij For comparing the probability of transition from location point i to location point j at time t with the current optimal composite indicator S ij Correlation, and the kth ant in the ant colony left at the edge L during the search ij Concentration of pheromone and current optimal comprehensive index S ij Is in positive correlation.
3. The unmanned aerial vehicle mountain land priority rescue path planning method of claim 2, wherein a probability of transition from a location point i to a location point j at a time t is:
wherein, alpha is an information heuristic factor and represents the importance degree of the pheromone; tau is ij (t) is the time limit L at time t ij Beta is an expected heuristic factor representing the relative importance of the visibility heuristic, eta ij Is a heuristic function of visibility, used to represent the visibility of the path (i, j), η ij ＝1/d ij In which d is ij Is the distance from point i to point j;
as time t progresses, edge L ij The pheromones above are:
τ ij (t+1)＝(1-ρ)τ ij (t)+Δτ ij
wherein, t +1 represents that the time advances to the next iteration, and the physical quantity at the moment t +1 represents that the time t is added with the time consumed by the previous iteration; rho is the evaporation coefficient of pheromone concentration, and the value interval of rho is as follows: rho epsilon (0,1); 1-rho is a pheromone concentration residual coefficient; delta tau ij The increase of the pheromone concentration is calculated by the following formula:
wherein K is the number of ants, delta tau ij k For the kth ant to stay at the edge L during searching ij The pheromone concentration in the above step is calculated in the following way:
wherein l km Labeled as the path length traveled by ant k in the search of the mth iteration.
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