CN106441303B - It is a kind of based on the paths planning method that can search for continuous neighborhood A* algorithm - Google Patents
It is a kind of based on the paths planning method that can search for continuous neighborhood A* algorithm Download PDFInfo
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Abstract
The present invention is to provide a kind of based on the paths planning method that can search for continuous neighborhood A* algorithm.The present invention is established the environmental model of environmental model acquisition using Grid Method, UUV is thought of as particle according to existing barrier geometric dimension, using the longest width of barrier as diameter, is handled by the round barrier of origin of the center of gravity of barrier；According to the information of barrier, grid size l is obtained；According to established grid map, the evaluation function f (x) of A* algorithm is determined；According to can neighborhood the characteristics of in conjunction with A* algorithm, determine the appraisal cost h (y) of arbitrary point y；The evaluation function minimum f of adjacent domains is found according to the evaluation function that can search for continuous neighborhood A* algorithmminNode UUV routeing is done step-by-step as next way point.The present invention solves the paths planning method of existing UUV in global context, there are problems that the smoothness difference in path and non-shortest path.
Description
Technical field
The present invention relates to a kind of UUV global path planning methods.
Background technique
UAV navigation (Unmanned underwater vehicle, UUV) is used as a kind of high-tech means, In
It is played a crucial role in the following valuable development space of this block of ocean, and path planning is autonomous underwater navigation
One of key technology of device is that Autonomous Underwater Vehicle can safely, effectively travel in locating environment, smoothly complete to
Determine the important guarantee of task.
UUV global path planning essence is under the conditions of given barrier and constraint, to find one in planning region
Optimal or feasible path from starting point to target point.Traditional path planning algorithm, as Artificial Potential Field Method, graph search method,
Particle swarm algorithm etc. has some shortcomings mostly, for example, be easily trapped into local minimum and it is computationally intensive the problems such as.With grid
On the basis of method environmental modeling, UUV global path planning is carried out using the A* algorithm that can search for continuous field, can effectively be solved
Certainly tradition UUV path planning algorithm be easily trapped into local best points, it is computationally intensive the problems such as.
Summary of the invention
The purpose of the present invention is to provide it is a kind of can be shortened the path UUV, save the time, improve efficiency based on the company of can search for
The paths planning method of continuous neighborhood A* algorithm.
The object of the present invention is achieved like this:
Step 1: establishing the environmental model of environmental model acquisition using Grid Method according to existing barrier, it is to UUV
Particle, barrier longest width be diameter, barrier center of gravity be the round barrier of origin, utilize formulaObtain the area S of each round barrieri, wherein liFor the maximum gauge of the i-th barrier；
Step 2: the area of each round barrier obtained according to step 1, determines grid size l；
Wherein: S is expressed as the gross area of environment, lmaxIt is barrier maximal side, lmin=min (lminobs,dr) it is obstacle
The minimum side length of object, drIt is UUV diameter, l is finally determining grid side length；
Step 3: the evaluation function of A* algorithm are as follows:
F (x)=g (x)+h (x)
Wherein, f (x) is the evaluation function from initial point via x node to target point, and evaluation function f, g (x) are in shape
From initial point to the actual cost of x node in state space, actual cost g, h (x) are from x node to target point optimal path
Estimate cost, appraisal cost are h；
Step 4: the evaluation function of the A* algorithm obtained according to step 3, the sideline of each grid is defined as may have access to
Node, the appraisal cost that can search for field A* algorithm is
H (y)=d × h (X2)+(l-d)×h(X1) d∈[0 l]
Wherein, h (X1) it is nodes X1Estimate cost, h (X2) it is nodes X2Estimate cost, y is point X2X1On line segment
Any point, d are point y to X1Distance, X1,X2...X8It is the node of the adjacent eight sideline intersection point of present node X；
Step 5: being sequentially found excellent according to the g value, f value and h value that can search for continuous field A* algorithm that step 4 obtains
The node of change, until target point, this paths is shortest path.
The present invention is that there are smoothness difference and path are non-most in path planning in the global context of ocean in order to solve UUV
Short problem and propose.
Beneficial effects of the present invention:
By calculating area and entire environment area shared by barrier, determines the grid size of grid environment, both avoided
The problem of Computer real-time processing and the data of storage increase, also improves the accuracy of UUV path planning.
By combining traditional A* algorithm with can search for continuous field, the path UUV is shortened, the time has been saved, is mentioned
High efficiency.
Detailed description of the invention
Fig. 1 is grid environmental modeling schematic diagram in the present invention；
Fig. 2 is that can search for the schematic diagram that field node is sideline intersection point in the present invention；
Fig. 3 is that can search for field node in the present invention be the adjacent sideline schematic diagram vertical with current sideline；
Fig. 4 is that can search for field node in the present invention be the adjacent sideline schematic diagram parallel with current sideline；
Fig. 5 is UUV Path Planning Simulation figure in the present invention.
Specific embodiment
It illustrates below and the present invention is described in more detail.
In conjunction with Fig. 1, Fig. 2, Fig. 3, Fig. 4 and Fig. 5, one kind described in present embodiment is based on can search for continuous neighborhood A* algorithm
Paths planning method, the specific steps of this method are as follows:
Step 1: the environmental model that environmental model obtains is established using Grid Method according to there are barriers, is selected on map
Any one barrier is taken, and judges whether it is round；If it is not, then being selected in all vertex of i-th of barrier
The maximum value of coordinate and minimum value x under environmental map coordinate systemmax,ymax,xmin,ymin, then compare | | xmax-xmin| | with | |
ymax-ymin| | maximum gauge is obtained, finally forms round barrier by origin of center of gravity.Utilize formula:
Obtain the area S of each round barrieri, liFor the maximum gauge of the i-th barrier.
Step 2: the size of computation grid is
Wherein, S is expressed as the gross area of environment, lmaxIt is barrier maximal side, lmin=min (lminobs,dr) it is obstacle
The minimum side length of object, drIt is the diameter of mobile robot, l is finally determining grid side length.
Working environment is indicated that wherein the east-west direction of working environment is by the elevation information for not considering UUV with two-dimensional surface
X-axis, North and South direction are Y-axis, establish rectangular coordinate system XOY, are sat if the horizontal and vertical maximum value of two-dimensional surface is corresponding in plane
It is X in mark systemmaxAnd Ymax, due to definition grid side length be l square.
Step 3: according to established grid map, the evaluation function of A* algorithm are as follows:
F (x)=g (x)+h (x) (4)
Wherein: x is node, and f (x) is the evaluation function from initial point via node to target point, and g (x) is in state sky
Between in actual cost from initial point to node, h (x) is the estimate cost from node to target point optimal path.When h (x) estimates
When counting actual distance value of the cost no more than node to target point, the points of search are more, low efficiency, but can obtain optimal solution；
When h (x) is greater than actual distance value of the node to target point, the points of search are few, high-efficient, but cannot be guaranteed to obtain optimal
Solution；For the Euclidean distance linear distance between two nodes in actual environment, can be chosen) be used as estimate cost, i.e.,
Wherein (xG,yG) be target point G coordinate, (xi,yi) it is arbitrary node XiCoordinate.
Step 4: according to the evaluation function for the A* algorithm that step 3 obtains, A* algorithm is mutually tied with can search for continuous neighborhood
It closes, so that the sideline of each grid is defined as addressable node, the estimate cost value h (y) of the arbitrary point y on one section of sideline is it
The linear combination of two-end-point assessment values, i.e.,
H (y)=d × h (X2)+(l-d)×h(X1) d∈[0 l] (6)
Wherein, h (X1) it is nodes X1Estimate cost, h (X2) it is nodes X2Estimate cost, y is point X2X1On line segment
Any point, d are point y to X1Distance, X1,X2...X8It is the node of the adjacent eight sideline intersection point of present node X.For
Sideline XXiOn cost be min (a, b).Its child node can be any one in these adjacent nodes, then move side
It also just expands to angle for continuous any direction.It is different with h value for g value, the f value of different nodes of locations y.
When nodes X being sideline intersection point the case where, nodes X to sideline X2X1The cost of upper arbitrary node y can be expressed as
And
Wherein, g (X) be in state space from initial point to the actual cost of X node, g (y) be in state space from
Actual cost of the initial point to y node, a, b are sideline XXiOn cost.
When nodes X is not sideline intersection point but vertical with this section of sideline situation, sideline nodes X to sideline X2X1Take up an official post
The cost of meaning node y can be expressed as
Wherein, t ∈ [0, l] and
When nodes X is not sideline intersection point node but parallel with this section of sideline situation, sideline nodes X to sideline X2X3
The cost of upper arbitrary node y can be expressed as
Wherein [0, l] m ∈, and
By the calculating of the above different situations, can compare to obtain fminAnd g.
Step 5: two tables of creation: open table and closed table, open table be used to save it is all oneself generate and do not investigate
Node, closed table accessed the node for being not required to investigate again for recording oneself.Then it is operated according to following step:
Starting point A is added in open table, and by the grid point X adjacent with A point1,X2...X8And appointing on sideline
The point y that anticipates adds open into table.Starting point A is moved on in closed table from open table, and be defined as the grid of its adjacent extension
Father node.G value, f value and the h value of grid point in open table are solved by formula (7,8,9,10,11,12).It is selected from open table
The smallest grid point of f value is selected, is denoted as present node X, and it is moved on in closed table from open list.It investigates respectively and works as prosthomere
Point X all adjacent nodes (be denoted as S, if fruit dot is neither in closed table nor in open table, added open into
Table, solves its g value, f value and h value, and juxtaposition present node X is its father node；If oneself is present in open table S point, compare
Compared with the size of g (X)+c (X, S) (wherein, c (X, S) is the cost from X point to S point) and g (S), as g (X)+c (X, S) < g (S)
When, g (S)=g (X)+c (X, S) is enabled, and accordingly update the f value of point S, and setting point X is otherwise its father node does not do any place then
Reason；If S is already present in closed table, also without any processing.It repeats above-mentioned until present node X is that target point G is
Only.Since target point G, its father node is sequentially found, until starting point A, this paths is that the algorithm solves most
Short path.
Specific example:
UUV arrives terminal (80,80) from starting point (10,10) navigation, 45 ° of original heading, devises multiple and barrier O1,
O2... is obtained the area of barrier using formula (1), obtains the size of grid using formula (2), obtained using formula (3)
In evaluation function.Then, being found using the evaluation function that can search for continuous neighborhood A* algorithm can search for continuous neighborhood evaluation function
UUV routeing is done step-by-step as next way point in the smallest node.Simulation track is as shown in Figure 5.
Claims (5)
1. it is a kind of based on the paths planning method that can search for continuous neighborhood A* algorithm, it is characterized in that:
Step 1: establishing the environmental model of environmental model acquisition using Grid Method according to existing barrier, chosen on map
Any one barrier, and judge whether it is round；If it is not, then being selected in all vertex of i-th of barrier
The maximum value of coordinate and minimum value x under environmental map coordinate systemmax,ymax,xmin,ymin, then compare | | xmax-xmin| | with | |
ymax-ymin| | maximum gauge is obtained, finally round barrier is formed by origin of center of gravity, utilizes formulaIt obtains
Obtain the area S of each round barrieri, wherein liFor the maximum gauge of the i-th barrier；
Step 2: the area of each round barrier obtained according to step 1, determines grid size l；
Working environment is indicated that wherein the east-west direction of working environment is X-axis by the elevation information for not considering UUV with two-dimensional surface,
North and South direction is Y-axis, establishes rectangular coordinate system XOY, if the horizontal and vertical maximum value of two-dimensional surface is corresponding in plane coordinate system
In be XmaxAnd Ymax, the grid side length of definition is the square of l；
Wherein: S is expressed as the gross area of environment, lmaxIt is barrier maximal side, lmin=min (lminobs,dr) it is barrier
Minimum side length, lminobsIt is the minimum side length of barrier itself, drIt is the diameter of UUV, l is finally determining grid side length；
Step 3: the evaluation function of A* algorithm are as follows:
F (x)=g (x)+h (x)
Wherein, f (x) is the evaluation function from initial point via x node to target point, and evaluation function f, g (x) are in state sky
Between in from initial point to the actual cost of x node, actual cost g, h (x) are the estimations from x node to target point optimal path
Cost, appraisal cost are h；
Step 4: the evaluation function of the A* algorithm obtained according to step 3, is defined as addressable section for the sideline of each grid
Point, the appraisal cost that can search for field A* algorithm are
H (y)=d × h (X2)+(l-d)×h(X1) d∈[0 l]
Wherein, h (X1) it is nodes X1Estimate cost, h (X2) it is nodes X2Estimate cost, y is point X2X1It is any on line segment
A bit, d is point y to X1Distance, X1,X2...X8It is the node of the adjacent eight sideline intersection point of present node X；
Step 5: sequentially finding optimization according to the g value, f value and h value that can search for continuous field A* algorithm that step 4 obtains
Node, until target point, this paths is shortest path.
2. it is according to claim 1 based on the paths planning method that can search for continuous neighborhood A* algorithm, it is characterized in that estimation
Cost h (x) are as follows:
(xG,yG) be target point G coordinate, (xi,yi) it is arbitrary node XiCoordinate.
3. it is according to claim 1 or 2 based on the paths planning method that can search for continuous neighborhood A* algorithm, it is characterized in that:
When nodes X is sideline intersection point, nodes X to sideline X2X1The cost of upper arbitrary node y is expressed as
And
Wherein, g (X) is from initial point to the actual cost of X node in state space, and g (y) is in state space from initial
Point arrives the actual cost of y node, and a, b are sideline XXiOn cost；
When nodes X is not sideline intersection point but is vertical with this section of sideline, sideline nodes X to sideline X2X1The generation of upper arbitrary node y
Valence is expressed as
Wherein, t ∈ [0, l] and
When nodes X is not sideline intersection point node but is parallel with this section of sideline, sideline nodes X to sideline X2X3Upper arbitrary node y
Cost be expressed as
Wherein [0, l] m ∈, and
4. it is according to claim 1 or 2 based on the paths planning method that can search for continuous neighborhood A* algorithm, it is characterized in that step
Rapid five specifically include: two tables of creation: open table and closed table, and open table is used to save all own sections for generating and not investigating
Point, closed table are used to record the node for having accessed and being not required to investigate again, then be operated according to following step:
Starting point A is added in open table, and by the grid point X adjacent with A point1,X2...X8And the arbitrary point y on sideline
Open is added into table；Starting point A is moved on in closed table from open table, and is defined as father's section of the grid of its adjacent extension
Point solves g value, f value and the h value of grid point in open table, and the smallest grid point of f value is selected from open table, is denoted as and works as prosthomere
Point X, and it is moved on in closed table from open list, all adjacent nodes for investigating present node X respectively are denoted as S, if
Point is added open into table, solves its g value, f value and h value, juxtaposition is current neither in closed table nor in open table
Nodes X is its father node；If S point is already present in open table, compare the size of g (X)+c (X, S) and g (S), c (X,
S g (S)=g (X)+c (X, S)) is enabled as g (X)+c (X, S) < g (S) for the cost from X point to S point, and accordingly updates point S
F value, and set point X be its father node, it is otherwise, then without any processing；If S is already present in closed table, also do not do
Any processing, repetition is above-mentioned until present node X is target point G, since target point G, sequentially finds its father node, directly
Until starting point A, this paths is shortest path.
5. it is according to claim 3 based on the paths planning method that can search for continuous neighborhood A* algorithm, it is characterized in that step
Five specifically include: two tables of creation: open table and closed table, and open table is used to save all own sections for generating and not investigating
Point, closed table are used to record the node for having accessed and being not required to investigate again, then be operated according to following step:
Starting point A is added in open table, and by the grid point X adjacent with A point1,X2...X8And the arbitrary point y on sideline
Open is added into table；Starting point A is moved on in closed table from open table, and is defined as father's section of the grid of its adjacent extension
Point solves g value, f value and the h value of grid point in open table, and the smallest grid point of f value is selected from open table, is denoted as and works as prosthomere
Point X, and it is moved on in closed table from open list, all adjacent nodes for investigating present node X respectively are denoted as S, if
Point is added open into table, solves its g value, f value and h value, juxtaposition is current neither in closed table nor in open table
Nodes X is its father node；If S point is already present in open table, compare the size of g (X)+c (X, S) and g (S), c (X,
S g (S)=g (X)+c (X, S)) is enabled as g (X)+c (X, S) < g (S) for the cost from X point to S point, and accordingly updates point S
F value, and set point X be its father node, it is otherwise, then without any processing；If S is already present in closed table, also do not do
Any processing, repetition is above-mentioned until present node X is target point G, since target point G, sequentially finds its father node, directly
Until starting point A, this paths is shortest path.
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