CN108444482A - A kind of autonomous pathfinding barrier-avoiding method of unmanned plane and system - Google Patents
A kind of autonomous pathfinding barrier-avoiding method of unmanned plane and system Download PDFInfo
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- G—PHYSICS
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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Abstract
A kind of autonomous pathfinding barrier-avoiding method of unmanned plane of present invention offer and system.The method of the present invention, including：Acquire the location information of barrier；Three-dimensional environment modeling is carried out by Grid Method, and it is divided into several grids, the processing for carrying out different colours to the grid comprising barrier and the grid not comprising barrier respectively, the Judge plane by crossing starting point and target endpoint divide grid, obtain two-dimensional grid model；Based on A* algorithms global static path planning is made on the two-dimensional grid model；After global static path planning, local dynamic station path planning is carried out；Track following, the smoothing processing of complete paired pathways track are carried out to the field location cooked up by Bezier；The present invention passes through global static path planning and local active path planning so that unmanned plane can find the change for the Obstacle Position that the dynamic change of environment is brought in time, and calculating process is simple and efficient, solve the problems, such as that conventional flight circuit is easy to touch barrier.
Description
Technical field
The present invention relates to unmanned plane path planning and track following field more particularly to a kind of autonomous pathfinding avoidances of unmanned plane
Method and system.
Background technology
Unmanned plane can carry out the task of various demands under complex environment, due to the property complicated and changeable of environment, lead to nothing
It is man-machine to encounter various problem in the task of execution.Wherein most important problem is precisely due to caused by changeable environment
Unmanned plane peripheral obstacle increases so that unmanned plane correctly can not execute task by cut-through object.Direct straight line cut-through
Object may also will produce the accident of collision paddle.Due to the property complicated and changeable of environment, when static avoidance pathfinding cannot meet
The complex environment of variation is carved, one kind must be used to detect environmental change, a kind of dynamic rule of real-time alternative routing in real time thus
The method of drawing.
In path planning and track following field, various algorithms emerge one after another.Existing related algorithm is all due to majority
Static map and environment are handled, although the performance under static environment is all very outstanding, in having for dynamic environment field performance
Shortcoming.
Invention content
According to technical problem set forth above, and one kind is provided after global path planning, carry out local dynamic station planning, more
The autonomous pathfinding barrier-avoiding method of unmanned plane and system of environmental change are handled well.The technological means that the present invention uses is as follows：
A kind of autonomous pathfinding barrier-avoiding method of unmanned plane, includes the following steps：
S1, the location information for acquiring barrier；
S2, three-dimensional environment modeling is carried out by Grid Method, and is divided into several grids, respectively to including barrier
Grid and grid not comprising barrier carry out the processing of different colours, pass through the Judge plane for crossing starting point and target endpoint
Divide grid, obtains two-dimensional grid model；
S3, global static path planning is made on the two-dimensional grid model based on A* algorithms, for finding starting point
Optimal global reference path between maximal end point；
S4, after global static path planning, local dynamic station path planning is carried out, in the dynamic ring for having handled burst
After the variation of border, it is returned to original path planned as early as possible；
Further, after the local dynamic station planning, including：S5, the circuit cooked up is clicked through by Bezier
Row track following, the smoothing processing of complete paired pathways track.
Further, the step S1 is specifically included：
S101, location information of the barrier under sensor coordinate system is obtained by sensor；
S102, the location information under the sensor coordinate system is transformed under unmanned plane coordinate system by coordinate transform；
S103, the location information under unmanned plane coordinate system is imported under inertial coodinate system by navigation calculation, obtains dynamic
Obstacle position information.
Further, the Judge plane by crossing starting point and target endpoint divides grid, obtains two-dimensional grid mould
Type specifically includes：
S201, starting point and target endpoint are crossed, generates the Judge plane perpendicular to yz planes；
S202, the two-dimensional grid of x/y plane is mapped into the Judge plane,
When the level height of starting point is higher than the level height of target endpoint：
When the level height of starting point is higher than the level height of target endpoint：
When the level height of starting point is consistent higher than the level height of target endpoint, Judge plane is parallel to x/y plane,
Pz=Ez=Sz (3)
Wherein, P1(Py,Pz) indicate the coordinate of any point P on Judge plane in the subpoint of yz planes, S1(Sy,Sz) indicate
Starting point S is in the subpoint of yz planes, E1(Ey,Ez) indicate target endpoint S yz planes subpoint,
The coordinate of any point in Judge plane is obtained in conjunction with formula (1) (2) (3),
S203, certain point coordinates P in Judge plane is setx=Bx, Py=By, the obstacle in two-dimensional grid is judged by formula (4)
Object grid and free grid, wherein：
If Pz＞ Bz, then with point P (P on plane Fx,Py,Pz) centered on grid be free grid,
If Pz＜ Bz, then with point P (P on plane Fx,Py,Pz) centered on grid be barrier grid,
B(Bx,By,Bz) indicate three-dimensional barrier grid coordinate.
Further, the step S3 specifically comprises the following steps：
S301, the two-dimensional grid model of foundation is mapped in two-dimensional array, by the barrier grid in the array
Corresponding element is assigned 1, and corresponding element of the free grid in array is assigned 0；
S302, starting point is added and is opened in collection OpenList, and the address assignment of start point information structure is worked as
Front nodal point parent pointer,
Wherein, the unlatching collection is for storing grid node information to be selected；
S303, present node is put into closing collection CloseList, the closing collection was selected for storing to have searched for
, the grid node information that cannot be searched again for；
S304, in two-dimensional array search for present node neighborhood in all free each nodes of raster symbol-base cost function
Value, and be compared with the node concentrated is opened, if opening collection has existed the node, more respective g (k) value, such as
G (k) the value smallers of the fruit node will then be opened g (k) and the f (k) concentrated and be updated, and will open to concentrate and store the node
Parent pointer is directed toward present node, is concentrated without the node if opening, which is added and opens collection, if target endpoint is added
Enter to open and concentrate, thens follow the steps S306, it is no to then follow the steps S305,
Wherein, g (k), path cost of the starting point to the k of node to be selected；F (k), the total cost value of node k to be selected；
The cost function f (n) is stated by following expression：
F (n)=g (n)+h (n),
G (n) indicates starting point to the path cost of node n, and g (n) is using a kind of formula based on Minkowski Distance
It is calculated, the cost for calculating all directions is different so that path cost is different, and h (n) is indicated from node to target point
Heuristic function；What h (n) was indicated is manhatton distance | x1-x2|+|y1-y2|；
S305, it is concentrated from unlatching and chooses the node with minimum f (k), present node pointer is directed toward present node, execution
S303；
S306, since destination node, determine final path by tracing father node pointer, global path planning is completed,
Outgoing route result.
Further, the step S4 specifically comprises the following steps：
S401, read path point simultaneously judge whether to reach target endpoint, if so, terminating flow, if it is not, then executing
S402；
S402, judge whether environment changes in present node field, if so, carrying out local dynamic station path planning, execute
S403 continues path point tracking if it is not, then returning to S401,
Wherein, the active path planning passes through shown in following algorithm：
F (n)=g (n)+h (n)+x (n)
G (n) indicates starting point to the path cost of node n, and g (n) is using a kind of formula based on Minkowski Distance
It is calculated, the cost for calculating all directions is different so that path cost is different, and h (n) is indicated from node to target point
Heuristic function；What h (n) was indicated is manhatton distance | x1-x2|+|y1-y2|；What x (n) was indicated is the node newly planned to former
Carry out the Euclidean distance in the path of static programming；
After S403, active path planning, judges whether to return global path, if so, returning to S401, continue path
Point tracking, if it is not, then continuing active path planning, until reverting in global path；
After path point judges point by point, target endpoint is reached, completes local dynamic station path planning in global path.
A kind of autonomous pathfinding obstacle avoidance system of unmanned plane, including：
Collecting unit, the location information for acquiring path obstructions；
Pretreatment unit carries out three-dimensional environment modeling for the information to acquisition and dimensionality reduction is at two-dimensional grid model；
Path planning unit, including
Wherein, Global motion planning module, it is optimal between two-dimensional grid generation starting point and maximal end point using A* algorithms
Global reference path；
Sector planning module, using improving and the A* algorithms with memoryless recurrence carry out local path optimum programming；
Post-processing module carries out track following to the field location cooked up using Bezier, makes smooth trajectory.
The present invention carries out three-dimensional environment modeling to the environment of unmanned plane, carries out drop mould to it by Judge plane and obtains two dimension
Raster Data Model makes global path planning based on A* algorithms on two-dimensional grid model, then by local dynamic station planning to burst
Dynamic environment variation planned again, then the field location cooked up smoothly is planned by Bezier, thus
Allow the change of Obstacle Position that unmanned plane finds that the dynamic change of environment is brought in time, local dynamic station Planning response speed
Degree is fast, and real-time is good, and calculating process is simple and efficient, solves the problems, such as that conventional flight circuit is easy to touch barrier, based on above-mentioned
The reason present invention can be widely popularized in unmanned plane path planning and track following field.
Description of the drawings
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technology description to do simply to introduce, it should be apparent that, the accompanying drawings in the following description is this hair
Some bright embodiments for those of ordinary skill in the art without having to pay creative labor, can be with
Obtain other attached drawings according to these attached drawings.
Fig. 1 is the autonomous pathfinding barrier-avoiding method flow chart of unmanned plane of the present invention；
Fig. 2 is the autonomous pathfinding obstacle avoidance apparatus figure of unmanned plane of the present invention；
Fig. 3 is three-dimensional modeling schematic diagram of the present invention, wherein (a) is the schematic diagram that starting point is higher than in the case of target endpoint,
(b) schematic diagram being less than for starting point in the case of target endpoint；
Fig. 4 is that Judge plane of the present invention cuts grid schematic diagram, wherein (a) is starting point higher than in the case of target endpoint
Schematic diagram, be (b) starting point less than the schematic diagram in the case of target endpoint；
Fig. 5 is two-dimensional grid model schematic of the present invention, wherein (a) is starting point higher than showing in the case of target endpoint
It is intended to, is (b) schematic diagram that starting point is less than in the case of target endpoint；
Fig. 6 is the path search process schematic diagram of the global static path planning of the present invention；
Fig. 7 is the path planning process figure of local dynamic station path planning of the present invention；
Fig. 8 is that the present invention smoothly plans schematic diagram by Bezier to field location.
Specific implementation mode
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
The every other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.
As shown in Figure 1, a kind of autonomous pathfinding barrier-avoiding method of unmanned plane, includes the following steps：
S2, three-dimensional environment modeling is carried out by Grid Method, and is divided into several grids, respectively to including barrier
Grid and grid not comprising barrier carry out the processing of different colours, pass through the Judge plane for crossing starting point and target endpoint
Divide grid, obtains two-dimensional grid model；
S3, global static path planning is made on the two-dimensional grid model based on A* algorithms, for finding starting point
Optimal global reference path between maximal end point；
S4, after global static path planning, local dynamic station path planning is carried out, in the dynamic ring for having handled burst
After the variation of border, it is returned to original path planned as early as possible；
S5, track following, the smooth place of complete paired pathways track are carried out to the field location cooked up by Bezier
Reason.
As shown in Fig. 2, a kind of autonomous pathfinding obstacle avoidance system of unmanned plane, including：
Collecting unit, the location information for acquiring path obstructions；
Pretreatment unit carries out three-dimensional environment modeling for the information to acquisition and dimensionality reduction is at two-dimensional grid model；
Path planning unit, including
Wherein, Global motion planning module, it is optimal between two-dimensional grid generation starting point and maximal end point using A* algorithms
Global reference path；
Sector planning module, using improving and the A* algorithms with memoryless recurrence carry out local path optimum programming；
Post-processing module carries out track following to the field location cooked up using Bezier, makes smooth trajectory.
Embodiment 1, wherein step S1 includes：Barrier is obtained in sensor coordinates by laser and ultrasonic sensor
System under location information, converted it under unmanned plane coordinate system by changes in coordinates, then by navigation system by it in inertia
Position under coordinate system shows, to obtain the location information of dynamic barrier.
Planning space is divided into the basic unit of several regular shapes, each basic unit using unit decomposition modeling
The node comprising barrier is divided into not comprising the node of barrier, these nodes can intuitively describe environmental information
And stored convenient for computer, while each node facilitates fractionation, and operation is handled convenient for planning algorithm.
Grid Method carries out three-dimensional environment modeling, is that the three-dimensional planning space of limited range such as is divided at the big unit, such as
Shown in Fig. 3 (a) (b), it is divided into n × n × n grid, the present embodiment to use 30 × 30 × 30 grids three dimensions, each
Grid is a cube, a unit of account of each grid center of a lattice as planning algorithm, wherein including the barrier of barrier
Hinder object grid to be assigned a value of 1, is used in combination dark processing, the free grid not comprising barrier to be assigned a value of 0, is handled with light color, Mei Gedan
Position grid center of a lattice is by the coordinate as the grid.
As shown in Fig. 4 (a) (b), starting point and target endpoint are crossed, generates the Judge plane F perpendicular to yz planes, is judged flat
Face F includes starting point S and target endpoint E, and cuts through the barrier grid that height is higher than Judge plane F；
As shown in Fig. 5 (a) (b), the two-dimensional grid of x/y plane is mapped into the Judge plane F, the unit of Judge plane F
Grid size is determined by the elementary cell size of the angle and x/y plane of Judge plane F and x/y plane.Figure includes Judge plane
F and sharp angle α formed by x/y plane, starting point and the extended line of target endpoint line and the intersection point C (C of y-axisy, 0),
In Fig. 3,4,5 shown in (a) figure, when the level height of starting point is higher than the level height of target endpoint：
In Fig. 3,4,5 shown in (b) figure, when the level height of starting point is higher than the level height of target endpoint：
When the level height of starting point is consistent higher than the level height of target endpoint, Judge plane is parallel to x/y plane,
Pz=Ez=Sz (3)
Wherein, P1(Py,Pz) indicate the coordinate of any point P on Judge plane in the subpoint of yz planes, S1(Sy,Sz) indicate
Starting point S is in the subpoint of yz planes, E1(Ey,Ez) indicate target endpoint S yz planes subpoint,
The coordinate of any point in Judge plane is obtained in conjunction with formula (1) (2) (3),
S203, certain point coordinates P in Judge plane is setx=Bx, Py=By, the obstacle in two-dimensional grid is judged by formula (4)
Object grid and free grid, wherein：
If Pz＞ Bz, then with point P (P on plane Fx,Py,Pz) centered on grid be free grid,
If Pz＜ Bz, then with point P (P on plane Fx,Py,Pz) centered on grid be barrier grid,
B(Bx,By,Bz) indicate three-dimensional barrier grid coordinate.
The essence of A* algorithms is the combination of greedy algorithm and heuristic search algorithm, so A* algorithms combine greedy algorithm
And the advantages of heuristic search algorithm, inherit the characteristic of the two.A* algorithms are advised compared with greedy algorithm in state space
In the case of mould is huge, search efficiency is significantly increased, and compared with best-first search algorithm, A* algorithms are because combine greed
" optimizing " property of algorithm, gradually tends to optimally scan for, overcome best-first search algorithm heuristic information it is single lack
Point can theoretically search optimal solution in state space.
The core of A* algorithms is the design of cost function, shown in the general expression such as formula (5) of cost function.
F (n)=g (n)+h (n) (5)
Wherein f (n) is the cost function of each node, and g (n) indicates that starting point arrives the cost of node n, and h (n) expressions are from section
Point the problem of for different situations, answers key design heuristic function h (n) to the heuristic function of target point.When h (n) be designed as it is small
In the true minimum cost equal to present node to target endpoint, A* algorithms can find optimal in planning space in theory
The true minimum cost in path, present node to target endpoint is unable to estimate and is calculated in advance, and h (n) is required and true cost
It approaches to the maximum extent, therefore to ensure that the accuracy of A* algorithm pathfindings, h (n) must be well-designed in conjunction with practical problem itself.
As shown in fig. 6, during carrying out path planning using A* algorithms, it is assumed that establish environmental model, A* with Grid Method
Algorithm goes out the cost value of node to be selected in the state space of Raster Data Model by cost function calculation, is deposited into unlatching collection
In.Select the node of Least-cost from opening to concentrate, be deposited into closing collection, continued based on being selected into the node for closing collection into
Row expanded search, progress cost value must update during each expanded search, ensure the father node pointer of each node
The optimal path from starting point to this node, when A* algorithm search to terminal and be deposited into closing collection after, utilize each section
The father node pointer of point is recalled, and the path search process of A* algorithms is completed.
H (n) selects manhatton distance, has：
H (n)=D* (| n.x-goal.x |+| n.y-goal.y |)
A* algorithms really pass through the letter of the two-dimensional grid node after inspection dimensionality reduction during global path planning
Breath, and it is calculated as cost value, during software emulation, the information that each grid node is included will be stored as a structure
Form is called convenient for routine access.Shown in the following code of the structure.
Member variable g, f, h in structure indicate that g (), f (), h (), fatherpoint are node parent pointer respectively,
Recalled by father node pointer after the node searching of A* algorithms the structure address for being directed toward the father node of present node
The father node of each node completes global path planning.
As shown in fig. 7, the global path planning process based on A* algorithms on dimensionality reduction model is as follows：
S301, the two-dimensional grid model of foundation is mapped in two-dimensional array, by the barrier grid in the array
Corresponding element is assigned 1, and corresponding element of the free grid in array is assigned 0；
S302, starting point is added and is opened in collection OpenList, and the address assignment of start point information structure is worked as
Front nodal point parent pointer,
Wherein, the unlatching collection is for storing grid node information to be selected；
S303, present node is put into closing collection CloseList, the closing collection was selected for storing to have searched for
, the grid node information that cannot be searched again for；
S304, in two-dimensional array search for present node neighborhood in all free each nodes of raster symbol-base cost function
Value, and be compared with the node concentrated is opened, if opening collection has existed the node, more respective g (k) value, such as
G (k) the value smallers of the fruit node will then be opened g (k) and the f (k) concentrated and be updated, and will open to concentrate and store the node
Parent pointer is directed toward present node, is concentrated without the node if opening, which is added and opens collection, if target endpoint is added
Enter to open and concentrate, thens follow the steps S306, it is no to then follow the steps S305,
Wherein, g (k), path cost of the starting point to the k of node to be selected；F (k), the total cost value of node k to be selected；
The cost function f (n) is stated by following expression：
F (n)=g (n)+h (n),
G (n) indicates starting point to the path cost of node n, and g (n) is using a kind of formula based on Minkowski Distance
It is calculated, the cost for calculating all directions is different so that path cost is different, and h (n) is indicated from node to target point
Heuristic function；What h (n) was indicated is manhatton distance | x1-x2|+|y1-y2|；
S305, it is concentrated from unlatching and chooses the node with minimum f (k), present node pointer is directed toward present node, execution
S303；
S306, since destination node, determine final path by tracing father node pointer, global path planning is completed,
Outgoing route result.
As shown in fig. 7, active path planning flow,
S401, read path point simultaneously judge whether to reach target endpoint, if so, terminating flow, if it is not, then executing
S402；
S402, judge whether environment changes in present node field, if so, carrying out local dynamic station path planning, execute
S403 continues path point tracking if it is not, then returning to S401,
Wherein, the active path planning passes through shown in following algorithm：
F (n)=g (n)+h (n)+x (n)
G (n) indicates starting point to the path cost of node n, and g (n) is using a kind of formula based on Minkowski Distance
It is calculated, the cost for calculating all directions is different so that path cost is different, and h (n) is indicated from node to target point
Heuristic function；What h (n) was indicated is manhatton distance | x1-x2|+|y1-y2|；What x (n) was indicated is the node newly planned to former
Carry out the Euclidean distance in the path of static programming；
After S403, active path planning, judges whether to return global path, if so, returning to S401, continue path
Point tracking, if it is not, then continuing active path planning, until reverting in global path；
After path point judges point by point, target endpoint is reached, completes local dynamic station path planning in global path.
Since traditional avoidance is after detecting barrier, avoidance is directly carried out along straight line, is easy to bump against unmanned plane,
Lead to mission failure, thus this section use the method for planning track based on Bezier, carry out cut-through object track with
Track.
As shown in figure 8, second order Bezier, by bypassing P0, P1, P2One bezier curve of these three point compositions：
B (t)=(1-t)2P0+2t(1-t)P1+t2P2,t∈[0,1] (6)
The general general formula of three rank Beziers：
The field location cooked up to front by way of with Bezier carries out a track following so that track
It is smoother, more controllably, there is fabulous applicability.
Finally it should be noted that：The above embodiments are only used to illustrate the technical solution of the present invention., rather than its limitations；To the greatest extent
Present invention has been described in detail with reference to the aforementioned embodiments for pipe, it will be understood by those of ordinary skill in the art that：Its according to
So can with technical scheme described in the above embodiments is modified, either to which part or all technical features into
Row equivalent replacement；And these modifications or replacements, various embodiments of the present invention technology that it does not separate the essence of the corresponding technical solution
The range of scheme.
Claims (7)
1. a kind of autonomous pathfinding barrier-avoiding method of unmanned plane, which is characterized in that including：Following steps：
S1, the location information for acquiring barrier；
S2, three-dimensional environment modeling is carried out by Grid Method, and is divided into several grids, respectively to the grid comprising barrier
Lattice and grid not comprising barrier carry out the processing of different colours, pass through the Judge plane segmentation for crossing starting point and target endpoint
Grid obtains two-dimensional grid model；
S3, global static path planning is made on the two-dimensional grid model based on A* algorithms, for find starting point with most
Optimal global reference path between terminal；
S4, after global static path planning, carry out local dynamic station path planning, for handle burst dynamic environment change
After change, it is returned to original path planned as early as possible.
2. according to the method described in claim 1, it is characterized in that, after the step S4, including：S5, pass through Bezier
Track following, the smoothing processing of complete paired pathways track are carried out to the field location cooked up.
3. according to the method described in claim 2, it is characterized in that, the step S1 is specifically included：
S101, location information of the barrier under sensor coordinate system is obtained by sensor；
S102, the location information under the sensor coordinate system is transformed under unmanned plane coordinate system by coordinate transform；
S103, the location information under unmanned plane coordinate system is imported under inertial coodinate system by navigation calculation, obtains dynamic barrier
Hinder object location information.
4. according to the method described in claim 3, it is characterized in that, the Judge plane by crossing starting point and target endpoint
Divide grid, obtains two-dimensional grid model and specifically include：
S201, starting point and target endpoint are crossed, generates the Judge plane perpendicular to yz planes；
S202, the two-dimensional grid of x/y plane is mapped into the Judge plane,
When the level height of starting point is higher than the level height of target endpoint：
When the level height of starting point is higher than the level height of target endpoint：
When the level height of starting point is consistent higher than the level height of target endpoint, Judge plane is parallel to x/y plane,
Pz=Ez=Sz (3)
Wherein, P1(Py,Pz) indicate the coordinate of any point P on Judge plane in the subpoint of yz planes, S1(Sy,Sz) indicate starting
Point S is in the subpoint of yz planes, E1(Ey,Ez) indicate target endpoint S yz planes subpoint,
The coordinate of any point in Judge plane is obtained in conjunction with formula (1) (2) (3),
S203, certain point coordinates P in Judge plane is setx=Bx, Py=By, the barrier grid in two-dimensional grid are judged by formula (4)
Lattice and free grid, wherein：
If Pz＞ Bz, then with point P (P on plane Fx,Py,Pz) centered on grid be free grid,
If Pz＜ Bz, then with point P (P on plane Fx,Py,Pz) centered on grid be barrier grid,
B(Bx,By,Bz) indicate three-dimensional barrier grid coordinate.
5. according to the method described in claim 4, it is characterized in that, the step S3 specifically comprises the following steps：
S301, the two-dimensional grid model of foundation is mapped in two-dimensional array, the barrier grid is corresponding in the array
Element be assigned 1, corresponding element of the free grid in array is assigned 0；
S302, starting point is added and is opened in collection OpenList, and the address assignment of start point information structure is worked as into prosthomere
Point parent pointer,
Wherein, the unlatching collection is for storing grid node information to be selected；
S303, present node is put into closing collection CloseList, closings collection be used to store searched for it is selecting,
The grid node information that cannot be searched again for；
S304, the cost function that all free each nodes of raster symbol-base in present node neighborhood are searched in two-dimensional array
Value, and be compared with the node concentrated is opened, the node is had existed if opening to collect, more respective g (k) value, if
G (k) the value smallers of the node will then be opened g (k) and the f (k) concentrated and be updated, and concentrate the father for storing the node by opening
Pointer is directed toward present node, is concentrated without the node if opening, which is added and opens collection, if target endpoint is added into
It opens and concentrates, then follow the steps S306, it is no to then follow the steps S305,
Wherein, g (k), path cost of the starting point to the k of node to be selected；F (k), the total cost value of node k to be selected；
The cost function f (n) is stated by following expression：
F (n)=g (n)+h (n),
G (n) indicates starting point to the path cost of node n, and g (n) is using a kind of formula progress based on Minkowski Distance
It calculates, the cost for calculating all directions is different so that path cost is different, and h (n) indicates opening from node to target point
It sends a letter number；What h (n) was indicated is manhatton distance | x1-x2|+|y1-y2|；
S305, it is concentrated from unlatching and chooses the node with minimum f (k), present node pointer is directed toward present node, execution
S303；
S306, since destination node, determine final path by tracing father node pointer, global path planning is completed, output
Route result.
6. according to the method described in claim 5, it is characterized in that, the step S4 specifically comprises the following steps：
S401, read path point simultaneously judge whether to reach target endpoint, if so, terminating flow, if it is not, then executing S402；
S402, judge whether environment changes in present node field, if so, carrying out local dynamic station path planning, execute S403,
If it is not, then returning to S401, continue path point tracking,
Wherein, the active path planning passes through shown in following algorithm：
F (n)=g (n)+h (n)+x (n)
G (n) indicates starting point to the path cost of node n, and g (n) is using a kind of formula progress based on Minkowski Distance
It calculates, the cost for calculating all directions is different so that path cost is different, and h (n) indicates opening from node to target point
It sends a letter number；What h (n) was indicated is manhatton distance | x1-x2|+|y1-y2|；What x (n) was indicated is the node newly planned to originally quiet
The Euclidean distance in the path of state planning；
After S403, active path planning, judge whether return global path, if so, return S401, continue path point with
Track, if it is not, then continuing active path planning, until reverting in global path；
After path point judges point by point, target endpoint is reached, completes local dynamic station path planning in global path.
7. a kind of autonomous pathfinding obstacle avoidance system of unmanned plane, including：
Collecting unit, the location information for acquiring path obstructions；
Pretreatment unit carries out three-dimensional environment modeling for the information to acquisition and dimensionality reduction is at two-dimensional grid model；
Path planning unit, including
Wherein, Global motion planning module generates the optimal overall situation between starting point and maximal end point using A* algorithms in two-dimensional grid
Reference path；
Sector planning module, using improving and the A* algorithms with memoryless recurrence carry out local path optimum programming；
Post-processing module carries out track following to the field location cooked up using Bezier, makes smooth trajectory.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102359784A (en) * | 2011-08-01 | 2012-02-22 | 东北大学 | Autonomous navigation and obstacle avoidance system and method of indoor mobile robot |
| CN103528585A (en) * | 2013-09-26 | 2014-01-22 | 中北大学 | Path planning method of passable area divided at unequal distance |
| CN104075717A (en) * | 2014-01-21 | 2014-10-01 | 武汉吉嘉伟业科技发展有限公司 | Unmanned plane airline routing algorithm based on improved A* algorithm |
| CN106325294A (en) * | 2016-08-22 | 2017-01-11 | 上海交通大学 | Unmanned aerial vehicle trajectory smoothing method based on Bessel curve transition |
| CN106647769A (en) * | 2017-01-19 | 2017-05-10 | 厦门大学 | AGV path tracking and obstacle avoiding coordination method based on A* extraction guide point |
| CN106774310A (en) * | 2016-12-01 | 2017-05-31 | 中科金睛视觉科技（北京）有限公司 | A kind of robot navigation method |
| CN106980320A (en) * | 2017-05-18 | 2017-07-25 | 上海思岚科技有限公司 | Robot charging method and device |
| CN107229998A (en) * | 2017-05-31 | 2017-10-03 | 江苏德胜智业信息技术有限公司 | A kind of autonomous pathfinding strategy process of unmanned plane |
| CN107328418A (en) * | 2017-06-21 | 2017-11-07 | 南华大学 | Nuclear radiation detection autonomous path planning method of the mobile robot under strange indoor scene |
| 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 |
-
2018
- 2018-06-15 CN CN201810619904.0A patent/CN108444482B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102359784A (en) * | 2011-08-01 | 2012-02-22 | 东北大学 | Autonomous navigation and obstacle avoidance system and method of indoor mobile robot |
| CN103528585A (en) * | 2013-09-26 | 2014-01-22 | 中北大学 | Path planning method of passable area divided at unequal distance |
| CN104075717A (en) * | 2014-01-21 | 2014-10-01 | 武汉吉嘉伟业科技发展有限公司 | Unmanned plane airline routing algorithm based on improved A* algorithm |
| CN106325294A (en) * | 2016-08-22 | 2017-01-11 | 上海交通大学 | Unmanned aerial vehicle trajectory smoothing method based on Bessel curve transition |
| CN106774310A (en) * | 2016-12-01 | 2017-05-31 | 中科金睛视觉科技（北京）有限公司 | A kind of robot navigation method |
| CN106647769A (en) * | 2017-01-19 | 2017-05-10 | 厦门大学 | AGV path tracking and obstacle avoiding coordination method based on A* extraction guide point |
| CN106980320A (en) * | 2017-05-18 | 2017-07-25 | 上海思岚科技有限公司 | Robot charging method and device |
| CN107229998A (en) * | 2017-05-31 | 2017-10-03 | 江苏德胜智业信息技术有限公司 | A kind of autonomous pathfinding strategy process of unmanned plane |
| CN107328418A (en) * | 2017-06-21 | 2017-11-07 | 南华大学 | Nuclear radiation detection autonomous path planning method of the mobile robot under strange indoor scene |
| 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 |
|---|
| KANGNING ZHU, ER AL: "Two Layers Solving Strategy for Path Planning of Unmanned Aerial Vehicle", 《2016 8TH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS》 * |
| ZHE LV,ET AL: "3D Environment Modeling with Height Dimension Reduction and Path Planning for UAV", 《THE 9TH INTERNATIONAL CONFERENCE ON MODELLING, IDENTIFICATION AND CONTROL》 * |
| 何雨枫: "室内微小型无人机路径规划算法研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
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