CN102901500A - Aircraft optimal path determination method based on mixed probability A star and agent - Google Patents
Aircraft optimal path determination method based on mixed probability A star and agent Download PDFInfo
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Abstract
The invention discloses an aircraft optimal path determination method based on mixed probability A star and agent. The method is mainly used to solve problems of too long path length and too large thread value of obstacles to the aircraft. The method comprises steps of: (1) generating a topographical map of the obstacles; (2) arranging a start point SP and a stop point EP of the aircraft in the topographical map of the obstacles; (3) planning a global path of the aircraft by using a probability A star algorithm in accordance with the positions of the start point SP and the stop point EP of the aircraft; and (4) locally optimizing the planned path by using an agent algorithm. The method has advantages of short path length and low thread value, and can be used for determining the optimal path of the aircraft.
Description
Technical field
The invention belongs to the spatial data processing technology field, the particularly path optimization of aircraft, the path planning in the field such as the method can be used for carrying out intelligent navigation, and is unmanned.
Background technology
Aircraft is to rely on terrain information and enemy's situation information to carry out path planning, and namely under certain constraint condition, aircraft is from the starting point to the terminating point, and the flight path of specific purpose optimum can be preferentially satisfied in search, and wherein flight path represents with a series of track points.
Path planning is wanted the neutrodyne circuit electrical path length and to the threat value of aircraft, so path planning in artificial intelligence, is made overall planning, is played a part very important in machine learning and the spatial database technology in seeking the process of optimal path.
Along with the development of military technology, actual war demand, the path planning modeling of aircraft is conformed to the principle of simplicity only one System Development to system complicated and changeable.The path planning problem of aircraft presents real-time, multi-dimensional nature, the complex characteristic such as uncertain and non-linear.Be based upon the model under the legacy system, still get used to the angle known and that can not change from the barrier terrain information, with static, be idealized as the modeling problem that main mode is come exploratory flight device path planning, ignored from the angle of reality and treated whole flight course.The most paths planning method is relatively poor for the barrier landform scene effect of pressing close to true environment of complexity.This is to be simple static planing method because traditional algorithm adopts, namely before flight, carry out path planning according to barrier landform scene at the practical flight device, immutable and the barrier in path meets specific rule in flight course, and the gap of this and real scene is larger.Obvious this planing method has been carried out Utopian processing with barrier landform scene, and in fact the terrain information of flying scene is irregular random.
Present the mainly containing three types of technology and can solve this path planning problem of path planning: 1. traditional algorithm, such as Grid Method, Voronoi figure method; 2. intelligent optimization algorithm is such as genetic algorithm; 3. other algorithms are such as dynamic programming algorithm.
Traditional algorithm is that the mathematical equation according to classics calculates, more desirable barrier map is more excellent on the overall situation with the path that this type of algorithm calculates, but the requirement to barrier is comparatively idealized, largely is subject to the impact of algorithm itself and the idealized degree of barrier for the planning effect of actual landform map.In general, real map scene information content is comparatively complicated, comprise a large amount of mountain ranges, the thunderstorm zone, and the factors such as air defense position, in addition the thunderstorm zone be movably and the air defense position may occur suddenly the increase and decrease, this has all proposed very high requirement to the real-time of path planning.Barrier landform for regular shape adopts traditional algorithm such as Voronoi figure method can obtain comparatively desirable result, but if there is erose barrier landform, in the situation of the overlapping or dynamic map of barrier, Voronoi figure method just can't obtain effective path.
Intelligent optimization algorithm, to be proposed by John Holland in 1975 such as genetic algorithm, it is the evolution laws that a class is used for reference organic sphere, it is the survival of the fittest, survival of the fittest genetic mechanism develops and next randomization searching method, its principal feature is directly structure objects to be operated, and does not have the restriction of differentiate and continuous; Have inherent Implicit Parallelism and better global optimizing ability; Adopt the optimization method of randomization, the search volume that energy automatic acquisition and guidance are optimized is adjusted the direction of search adaptively, does not need the rule of determining.On robustness, be significantly improved than traditional algorithm, the Utopian degree of dependence of barrier is lowered, but the computing time of intelligent optimization algorithm is generally long, the barrier that is drawn by radar equation is larger to the threat value of aircraft, and can't adapt to movingly, variable more carry out path planning near the map of real situation.
For other algorithms, such as dynamic programming algorithm, because algorithm self restriction can reach optimum and adapt to dynamic map, but can't guarantee that may occur can not calculating overall path, route result is unstable in overall situation planning on local path.
Summary of the invention
The object of the invention is to the deficiency for above-mentioned prior art, propose the aircraft optimal path that a kind of Based on Probability A star mixes with intelligent body and determine method, to improve the correctness in the path of cooking up for multiple different maps, improve simultaneously the stability of carrying out repeatedly the route of path planning for same width of cloth map, strengthen the robustness to different map path plannings.
The technical thought that realizes the object of the invention is: required Various types of data is carried out pre-service, the path that draws its global path planning by probability A star algorithm, to meeting the aircraft of mechanics principle under arranging in flight course, carry out local optimum by the path that the intelligent body algorithm is cooked up probability A star algorithm, until aircraft flies to terminating point, thereby finish the whole process of the path planning in the barrier topographic map, its concrete steps comprise as follows:
1, the aircraft optimal path that mixes with intelligent body of a kind of Based on Probability A star is determined method, comprises the steps:
1) dyspoiesis thing topographic map:
If p
i, q
iThe starting point of each barrier that need walk around for aircraft and the coordinate of terminal point are respectively to p
iPoint and q
iPoint is done the straight line perpendicular to X-axis and Y-axis, and four straight lines surround a rectangle, expression barrier region T
i, wherein i is the barrier region number;
2) starting point SP and the terminating point EP of aircraft are set in the barrier topographic map that step (1) obtains;
3) according to the position of starting point SP and the terminating point EP of aircraft, with probability A star algorithm aircraft is carried out global path planning;
4) local optimum is carried out in the path of with the intelligent body algorithm step (3) being cooked up:
4a) initialization aircraft behaviour decision making weight:
With aircraft to father node D
jThe behaviour decision making weight of flight is denoted as R1; Aircraft is denoted as R2 to the behaviour decision making weight of right-hand flight; Aircraft is denoted as R3 to the behaviour decision making weight that left flies; Keep the behaviour decision making weight of current course flight to be denoted as R4 in aircraft, and the initial value of setting aircraft when the SP point does not begin to fly is: R1=0.5, R2=0.4, R3=0.4, R4=0.3;
4b) aircraft is processed accordingly according to the variation of surrounding environment:
If the equal clear in the place ahead of aircraft and the left and right sides, the size of R1, R2 and R3 numerical value relatively then, aircraft is according to the wherein corresponding direction flight of maximal value;
If aircraft the place ahead and right side clear, there is barrier in the left side, then compares the size of R1 and R2 numerical value, and aircraft is according to the wherein corresponding direction flight of maximal value;
If aircraft the place ahead and left side clear, there is barrier on the right side, then compares the size of R1 and R3 numerical value, and aircraft is according to the wherein corresponding direction flight of maximal value;
If aircraft the place ahead clear, all there is barrier both sides, and then aircraft is according to the corresponding direction flight of R4;
If there is barrier in aircraft the place ahead, the equal clear in both sides, the size of R2 and R3 numerical value relatively then, aircraft is according to the wherein corresponding direction flight of maximal value;
If there is barrier in aircraft the place ahead and left side, then aircraft is according to the corresponding direction flight of R2;
If there is barrier on aircraft the place ahead and right side, then aircraft is according to the corresponding behavior flight of R3;
4c) revise aircraft behaviour decision making weight:
The corresponding behaviour decision making weighted value of direction that aircraft is selected in step (4b) adds 0.03, and other behaviour decision making weighted values subtract 0.01, wherein set the behaviour decision making weighted value on be limited to 0.7, lower be limited to 0.2;
Whether the distance of 4d) judging aircraft and terminating point EP less than 2, if distance then stops local path optimization less than 2, the path that local optimum is crossed is as the optimal path of aircraft; Otherwise return step (4b) and (4c) proceed local path optimization.
The present invention has the following advantages compared with prior art:
The first, the present invention compares with traditional algorithm, owing to adopt probability A star, aircraft is carried out the drawn path of path planning shorten to some extent on length;
Second, the present invention compares with traditional algorithm, aircraft can be according to the variation of surrounding environment in flight course, utilize behaviour decision making weight mechanism to carry out local path optimization to the global path of having cooked up, shorten path, and in this process, reduced the threat value of barrier to aircraft;
The 3rd, the present invention compares other algorithms, owing to the barrier topographic map is not carried out any idealized processing, has overcome the shortcoming of the robustness deficiency in the path of cooking up for different obstruct thing topographic map.
Description of drawings
Fig. 1 is the process flow diagram of the inventive method;
Fig. 2 is that the present invention utilizes probability A star algorithm to carry out the sub-process figure of global path planning;
Fig. 3 is the sub-process figure that local optimum is carried out in path that the present invention cooks up probability A star;
Fig. 4 is the barrier topographic map that the present invention tests;
Fig. 5 is the path comparison diagram that the present invention and traditional A star algorithm are cooked up.
Embodiment
Be described in further detail below in conjunction with 1 pair of step of the present invention of accompanying drawing.
Step 1. dyspoiesis thing topographic map:
If p
i, q
iThe starting point of each barrier that need walk around for aircraft and the coordinate of terminal point are respectively to p
iPoint and q
iPoint is done the straight line perpendicular to X-axis and Y-axis, and four straight lines surround a rectangle, expression barrier region T
i, wherein i is the barrier region number.
Step 2. arranges starting point SP and the terminating point EP of aircraft in the barrier topographic map.
Step 3. utilizes probability A star algorithm to carry out global path planning according to the position of starting point SP and the terminating point EP of aircraft in the step 2.
With reference to Fig. 2, the implementation of this step is as follows:
3a) the barrier topographic map is divided into low precision grid map:
3a1) generate the high precision grid map contain 100 * 100 little grids, wherein the length of side of each little grid is 1, and the weight of the little grid that barrier is covered is made as 1, and other mesh Weight are made as 0;
3a2) count from the grid in the upper left corner of high precision grid map, per 10 * 10 little grids are merged into a macrolattice, then this high precision grid map is reduced to and contains 10 * 10 macroreticular low precision grid maps, each macroreticular length of side is 10, calculate weight in each macrolattice and be 1 little meshes number W (i), wherein i is macroreticular label, 1≤i≤100, calculate weight in each macrolattice and be 1 the shared ratio R (i) of little grid, wherein R (i)=W (i)/100;
3a3) establish the starting point SP of aircraft and the macrolattice at terminating point EP place and be respectively SG and EG;
3b) according to macrolattice SG and the EG at the starting point and ending point place of aircraft, with probability A star algorithm low precision grid map is carried out path planning:
3b1) making the macrolattice SG at the starting point SP place of aircraft is father node D
j, establish general plan displacement and be: E
J+1=L+E
j, wherein L is father node D
jTo the required path of walking of the macrolattice that is adjacent, E
jFor arriving father node D from SG
jThe distance that moves, j is the label of father node, sets father node initial value when SG to be: j=0, L=0, E
j=0;
3b2) calculate father node D
jTo the required path L that walks of each the macrolattice b that is adjacent
b, L wherein
bBe the length of two adjacent mesh lines of centres, b is adjacent macroreticular label, 1≤b≤8;
3b3) from adjacent macrolattice b, select a macrolattice c, calculating from this grid by first level after vertical translation with first vertical after horizontal translation arrival EG two paths of process the pre-estimation of every paths expend and be H=∑ R (k) * 10, estimate that with wherein pre-the minimum value that expends is designated as HM, k by in the translation process the macrolattice label of process, c is for selecting adjacent macroreticular label, 1≤c≤8;
3b4) to father node D
jEach adjacent macrolattice repeating step (3b2) and (3b3) obtains the minimum value HM that pre-estimation separately expends
b
3b5) calculate and father node D
jEach adjacent macrolattice b is to the total estimates F (b) of the macrolattice EG at terminating point EP place:
F(b)＝G(b)+HM
b；
Wherein G (b) expends G (b)=L for total the movement
b+ E
j
3b6) with the corresponding grid x of the minimum value among the total estimates F (b) as new father node D
J+1, E
J+1=L
x+ E
j, then current plan mobile route is father node D
jWith new father node D
J+1The line at the macrolattice center at place;
3b7) judge new father node D
J+1Whether is the macrolattice EG at terminating point EP place, if satisfy condition, then the path of the macrolattice EG at the macrolattice SG arrival terminating point EP place at the starting point SP place of aircraft in low precision grid map is father node D
tWith D
T+1The line segment that the macrolattice line of centres at place forms, wherein 0≤t≤j, and execution in step 4, otherwise, return step (3b2), until satisfy new father node D
J+1Condition for the macrolattice EG ground at terminating point EP place.
Local optimum is carried out in the path that step 4. is cooked up step 3 with the intelligent body algorithm.
With reference to Fig. 3, the implementation of this step is as follows:
4a) initialization aircraft behaviour decision making weight:
With aircraft to father node D
jThe behaviour decision making weight of flight is denoted as R1; Aircraft is denoted as R2 to the behaviour decision making weight of right-hand flight; Aircraft is denoted as R3 to the behaviour decision making weight that left flies; Keep the behaviour decision making weight of current course flight to be denoted as R4 in aircraft, and the initial value of setting aircraft when the SP point does not begin to fly is: R1=0.5, R2=0.4, R3=0.4, R4=0.3;
4b) aircraft is processed accordingly according to the variation of surrounding environment:
If the equal clear in the place ahead of aircraft and the left and right sides, the size of R1, R2 and R3 numerical value relatively then, aircraft is according to the wherein corresponding direction flight of maximal value;
If aircraft the place ahead and right side clear, there is barrier in the left side, then compares the size of R1 and R2 numerical value, and aircraft is according to the wherein corresponding direction flight of maximal value;
If aircraft the place ahead and left side clear, there is barrier on the right side, then compares the size of R1 and R3 numerical value, and aircraft is according to the wherein corresponding direction flight of maximal value;
If aircraft the place ahead clear, all there is barrier both sides, and then aircraft is according to the corresponding direction flight of R4;
If there is barrier in aircraft the place ahead, the equal clear in both sides, the size of R2 and R3 numerical value relatively then, aircraft is according to the wherein corresponding direction flight of maximal value;
If there is barrier in aircraft the place ahead and left side, then aircraft is according to the corresponding direction flight of R2;
If there is barrier on aircraft the place ahead and right side, then aircraft is according to the corresponding behavior flight of R3;
4c) revise aircraft behaviour decision making weight:
The corresponding behaviour decision making weighted value of direction that aircraft is selected in step (4b) adds 0.03, and other behaviour decision making weighted values subtract 0.01, wherein set the behaviour decision making weighted value on be limited to 0.7, lower be limited to 0.2;
Whether the distance of 4d) judging aircraft and terminating point EP less than 2, if distance then stops local path optimization less than 2, the path that the intelligent body local optimum is crossed is as the optimal path of aircraft; Otherwise return step (4b) and (4c) proceed local path optimization.
Effect of the present invention further specifies by following emulation.
1. simulated conditions:
Hardware platform is: Intel Core2Duo CPU E6550@2.33GHZ, 2GB RAM
Software platform is: VC++6.0
Test employed barrier topographic map as shown in Figure 4, Fig. 4 (a) ~ (f) is respectively 6 kinds of different barrier topographic maps, size is 100 * 100, black region is barrier region among the figure, the annulus in the lower right corner is the starting point SP of aircraft among the figure, the annulus in the upper left corner is the terminating point EP of aircraft among the figure, and other zones are the clear zone.
2. the emulation content results is analyzed:
Fig. 5 (a) ~ (f) is the experimental result picture that Fig. 4 (a) ~ (f) is carried out emulation with algorithm of the present invention and traditional A star algorithm respectively, solid line from starting point SP to terminating point EP among Fig. 5 (a) ~ (f) is the path that traditional A star algorithm is cooked up, and dotted line is the path that algorithm of the present invention is cooked up.
Table 1 for algorithm of the present invention and traditional A star algorithm according to the comparison to the threat value of aircraft of the path of simulation result Fig. 5 (a) ~ (f) cook up and barrier, wherein threat value be barrier region to the threaten degree of aircraft, threat value D is according to radar equation D=1/d
4Calculate, d is that aircraft is apart from the short lines distance of barrier.
Table 1
As can be seen from Table 1, these two indexs of the present invention all are better than traditional A star algorithm.
To sum up, the aircraft optimal path that the Based on Probability A star that the present invention proposes mixes with intelligent body is determined method, the aircraft optimal path that the barrier topographic map is cooked up does not fly in barrier region, and no matter draw the present invention from experimental result contrast be that path or threat value all are better than traditional A star algorithm.Image and to the test of performance index is analyzed comparison to experimental result by experiment, has proved fully superiority of the present invention and robustness.
Claims (2)
1. the aircraft optimal path that mixes with intelligent body of a Based on Probability A star is determined method, comprises the steps:
1) dyspoiesis thing topographic map:
If p
i, q
iThe starting point of each barrier that need walk around for aircraft and the coordinate of terminal point are respectively to p
iPoint and q
iPoint is done the straight line perpendicular to X-axis and Y-axis, and four straight lines surround a rectangle, expression barrier region T
i, wherein i is the barrier region number;
2) starting point SP and the terminating point EP of aircraft are set in the barrier topographic map that step (1) obtains;
3) according to the position of starting point SP and the terminating point EP of aircraft, with probability A star algorithm aircraft is carried out global path planning;
4) local optimum is carried out in the path of with the intelligent body algorithm step (3) being cooked up:
4a) initialization aircraft behaviour decision making weight:
With aircraft to father node D
jThe behaviour decision making weight of flight is denoted as R1; Aircraft is denoted as R2 to the behaviour decision making weight of right-hand flight; Aircraft is denoted as R3 to the behaviour decision making weight that left flies; Keep the behaviour decision making weight of current course flight to be denoted as R4 in aircraft, and the initial value of setting aircraft when the SP point does not begin to fly is: R1=0.5, R2=0.4, R3=0.4, R4=0.3;
4b) aircraft is processed accordingly according to the variation of surrounding environment:
If the equal clear in the place ahead of aircraft and the left and right sides, the size of R1, R2 and R3 numerical value relatively then, aircraft is according to the wherein corresponding direction flight of maximal value;
If aircraft the place ahead and right side clear, there is barrier in the left side, then compares the size of R1 and R2 numerical value, and aircraft is according to the wherein corresponding direction flight of maximal value;
If aircraft the place ahead and left side clear, there is barrier on the right side, then compares the size of R1 and R3 numerical value, and aircraft is according to the wherein corresponding direction flight of maximal value;
If aircraft the place ahead clear, all there is barrier both sides, and then aircraft is according to the corresponding direction flight of R4;
If there is barrier in aircraft the place ahead, the equal clear in both sides, the size of R2 and R3 numerical value relatively then, aircraft is according to the wherein corresponding direction flight of maximal value;
If there is barrier in aircraft the place ahead and left side, then aircraft is according to the corresponding direction flight of R2;
If there is barrier on aircraft the place ahead and right side, then aircraft is according to the corresponding behavior flight of R3;
4c) revise aircraft behaviour decision making weight:
The corresponding behaviour decision making weighted value of direction that aircraft is selected in step (4b) adds 0.03, and other behaviour decision making weighted values subtract 0.01, wherein set the behaviour decision making weighted value on be limited to 0.7, lower be limited to 0.2;
Whether the distance of 4d) judging aircraft and terminating point EP less than 2, if distance then stops local path optimization less than 2, the path that the intelligent body local optimum is crossed is as the optimal path of aircraft; Otherwise return step (4b) and (4c) proceed local path optimization.
2. method according to claim 1, wherein the position of described starting point SP and the terminating point EP according to aircraft of step (3) is carried out global path planning with probability A star algorithm to aircraft, carries out as follows:
3a) the barrier topographic map is divided into grid map:
3a1) generate the high precision grid map contain 100 * 100 little grids, wherein the length of side of each little grid is 1, and the weight of the little grid that barrier is covered is made as 1, and other mesh Weight are made as 0;
3a2) count from the grid in the upper left corner of high precision grid map, per 10 * 10 little grids are merged into a macrolattice, then this high precision grid map is reduced to and contains 10 * 10 macroreticular low precision grid maps, each macroreticular length of side is 10, calculate weight in each macrolattice and be 1 little meshes number W (i), wherein i is macroreticular label, 1≤i≤100, calculate weight in each macrolattice and be 1 the shared ratio R (i) of little grid, wherein R (i)=W (i)/100;
3a3) establish the starting point SP of aircraft and the macrolattice at terminating point EP place and be respectively SG and EG;
3b) according to macrolattice SG and the EG at the starting point and ending point place of aircraft, with described probability A star algorithm low precision grid map is carried out path planning:
3b1) making the macrolattice SG at the starting point SP place of aircraft is father node D
j, establish general plan displacement and be: E
J+1=L+E
j, wherein L is father node D
jTo the required path of walking of the macrolattice that is adjacent, E
jFor arriving father node D from SG
jThe distance that moves, j is the label of father node, sets father node initial value when SG to be: j=0, L=0, E
j=0;
3b2) calculate father node D
jTo the required path L that walks of each the macrolattice b that is adjacent
b, L wherein
bBe the length of two adjacent mesh lines of centres, b is adjacent macroreticular label, 1≤b≤8;
3b3) from adjacent macrolattice b, select a macrolattice c, calculating from this grid by first level after vertical translation with first vertical after horizontal translation arrival EG two paths of process the pre-estimation of every paths expend and be H=∑ R (k) * 10, estimate that with wherein pre-the minimum value that expends is designated as HM, k by in the translation process the macrolattice label of process, c is for selecting adjacent macroreticular label, 1≤c≤8;
3b4) to father node D
jEach adjacent macrolattice repeating step (3b2) and (3b3) obtains the minimum value HM that pre-estimation separately expends
b
3b5) calculate and father node D
jEach adjacent macrolattice b is to the total estimates F (b) of the macrolattice EG at terminating point EP place:
F(b)=G(b)+HM
b；
Wherein G (b) expends G (b)=L for total the movement
b+ E
j,
3b6) with the corresponding grid x of the minimum value among the F (b) as new father node D
J+1, E
J+1=L
x+ E
j, then current plan mobile route is father node D
jWith new father node D
J+1The line at the macrolattice center at place;
3b7) judge new father node D
J+1Whether is the macrolattice EG at terminating point EP place, if satisfy condition, then the path of the macrolattice EG at the macrolattice SG arrival terminating point EP place at the starting point SP place of aircraft in low precision grid map is father node D
tWith D
T+1The line segment that the macrolattice line of centres at place forms, wherein 0≤t≤j otherwise, return step (3b2), until satisfy new father node D
J+1Condition for the macrolattice EG ground at terminating point EP place.
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