CN106845716A - A kind of unmanned surface vehicle local delamination paths planning method based on navigation error constraint - Google Patents
A kind of unmanned surface vehicle local delamination paths planning method based on navigation error constraint Download PDFInfo
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Abstract
The invention discloses a kind of unmanned surface vehicle local delamination paths planning method based on navigation error constraint, key step includes:The local static path planning of genetic algorithm is constrained and combined based on navigation error;The local dynamic station path planning of fusion maritime affairs rule.Present invention employs layering thought, local paths planning is divided into two aspects of static path planning and active path planning, that is, solves static-obstacle thing evasion, dynamic barrier evasion is solved again;Constrained based on navigation error and the static path planning with reference to genetic algorithm reduces the adverse effect that navigation error is caused to Path selection, improve the security of path planning;The active path planning of fusion maritime affairs rule, it is contemplated that the Dynamic Constraints of unmanned surface vehicle itself, improves the feasibility of path planning.
Description
Technical field
The present invention relates to a kind of unmanned surface vehicle local delamination paths planning method based on navigation error constraint, belong to water
Face unmanned boat Path Planning Technique field.
Background technology
With developing rapidly for science and technology, marine intelligent transportation is mainly used as the important component of China's science and technology development strategy
In the automation and the intellectuality of marine traffic control of realizing ship's navigation.Therefore, unmanned surface vehicle is used as maritime traffic system
Individual ship, the research to its navigation path is necessary.Path planning is an important research of unmanned surface vehicle
Field, is premise that unmanned surface vehicle can safely complete cruise task.In recent years, unmanned surface vehicle path planning has become
Marine intelligentized study hotspot.
The method of path planning has a lot, and a variety of situations are applied to because of the advantage and disadvantage of itself, is broadly divided into
Traditional algorithm, graphic-arts technique, intelligent bionic algorithm and other algorithms etc..Traditional algorithm has Artificial Potential Field Method, fuzzy logic to calculate
Method and simulated annealing etc.;Graphic-arts technique has Visual Graph space law, Grid Method and voronoi figure methods etc.;Intelligent bionic algorithm
There are ant group algorithm, neural network algorithm, genetic algorithm and particle cluster algorithm etc.;Other algorithms have A* algorithms, dijkstra's algorithm
With Floyd algorithms etc..
At present, most path plannings are only divided into two aspects of global path planning and local paths planning, do not examine
The lamination problem of local paths planning is considered, with certain limitation.Additionally, most static path plannings are assumed that
Under the premise of navigation information is accurate, unmanned surface vehicle can accurately track the path planned.But deposited in actual navigation system
In error, it will cause the safety zone between unmanned surface vehicle and practical obstacle thing to be reduced, initial point to impact point can be caused
Free path do not exist.Even if free path is still suffered from, be likely to unmanned surface vehicle bring such as collision can not be pre-
The risk known.Therefore, during path planning, the influence of navigation error is very important problem, to unmanned surface vehicle
Practical engineering application is significant.
Also there is the part research report relevant with the present invention at present, 1, unmanned surface vehicle risk of delamination is evaded under complexity sea situation
Technique study, Harbin Engineering University, 2014, although it is contemplated that three layering aspects in the thesis for the doctorate, but do not consider navigation
Influence of the error to path planning.2nd, a kind of UAV navigation paths planning method based on navigation error space, war industry
Journal, 2014,35 (8):1243-1250, although it is contemplated that navigation error in the document, but is carried out just for submarine navigation device
Global path planning research, and simultaneously it is not associated with genetic algorithm.
The content of the invention
Technical problem:The present invention provide it is a kind of reduce the adverse effect that is caused to Path selection of navigation error based on navigation
Be divided into for local paths planning and missed based on navigation by the unmanned surface vehicle local delamination paths planning method of error constraints, the method
Difference constraint simultaneously combines the local static path planning of genetic algorithm and the local dynamic station path planning two of fusion maritime affairs rule
Aspect, considers the restrictive conditions such as navigation error constraint, the constraint of unmanned surface vehicle own dynamics, can improve path planning
Security and feasibility.
Technical scheme:Unmanned surface vehicle local delamination paths planning method based on navigation error constraint of the invention, bag
Include following steps:
Step 1) the static-obstacle thing information that is obtained according to job task, electronic chart and sensor, using rectangular co-ordinate
System and Grid Method, by the discrete elementary cell grid for turning to two dimension of environment, environmental modeling are carried out by the sign to these grids,
Initialization start position Start and aiming spot Goal;
Step 2) M bar chromosomes are obtained as follows:Randomly selected except starting point on grating map using randomized
Starting point and impact point, are divided into a+1 sections, then using A* by a free grid point outside position Start and aiming spot Goal
Algorithm connection source, impact point and a free grid point, make a feasible initial solution, that is, obtain one effectively
Chromosome;
Using the M bars chromosome as initial population, as first generation population, wherein M is the scale of first generation population;
Step 3) determine every navigation error of each gene point of chromosome, specially:Using kth in every chromosome
Expectation navigation position μ at individual gene pointkWith the expectation navigation position μ at+1 gene point of kthk+1Between distance to go D
(μK,μk+1) and navigation error rate of change Δε, product calculation is carried out, along with k-th navigation error ε of gene pointk, obtain every
+ 1 navigation error ε of gene point of bar chromosome kthk+1;Then according to Beidou navigation error εBDIf kth+1 gene point is led
Boat error εk+1> εBD, then by εBDAssignment εk+1;Otherwise ,+1 navigation error ε of gene point of kthk+1It is initial value;
Step 4) according to the step 3) every navigation error of each gene point of chromosome is obtained, using Gauss model
As navigation error model, the navigation position distribution density function according to the navigation error modelUsing Meng Teka
Lip river sampling method is calculated every path cost L (s of chromosome0, sg), wherein the minimum value in all chromosome path costs
It is the optimal solution of the generation population;
Step 5) there will be the danger zone of barrier as collision area A, according to navigation error model, in collision area A
It is interior that navigation error probability density function is integrated, the collision probability at every each gene point of chromosome is obtained, obtain
Every starting point of chromosome to the gene o'clock of impact point, i.e., the 0th to the full gene point between g-th gene point at collision it is general
Rate set { Pc(X0), Pc(X1)...Pc(Xg)};
Step 6) according to the step 5) the collision probability set of the full gene point of every chromosome that obtains, for every
Bar chromosome, searches the chromosome in collision probability set { Pc(X0), Pc(X1)...Pc(Xg) in collision probability maximum,
Then as the collision probability P of the chromosomec(X);
Step 7) according to the step 4) path cost of every chromosome that obtains and the step 6) obtain every
The collision probability P of chromosomec(X) every fitness of chromosome, is calculated in such a way;
Judge the step 6) the collision probability P of every chromosome that obtainsc(X) whether setting value is less than or equal to, such as
It is that every fitness E of chromosome is then calculated according to following formula:
Every fitness E of chromosome is otherwise calculated according to following formula:
Wherein m is weighted value setting value, m > > L (s0, sg);
The fitness of all chromosomes is added and is averaging, as the average adaptive value of the generation population;
Step 8) the n-th generation population algebraically of hypothesis be x (n), if population algebraically x (n) < setting values nsetting, then into step
9);Otherwise, process as follows:
If in n-th-(nsetting- 1) for the continuous n of population to current n-th generation populationsettingGeneration evolve in, population it is optimal
Xie Jun does not change, and the kind group mean adaptive value change of adjacent generations is not above 1%, then will have in current n-th generation
The chromosome of optimal solution touches path as the optimal nothing for being solved, and according to the coordinate value of the gene point for constituting the chromosome
A series of path node sequences from starting point to impact point are obtained, using the path node sequence as after local air route point sequence,
Into step 10), otherwise into step 9);
Step 9) according to crossing-over rate pc, that is, the probable value of the crossover operation for setting, random selection pc* M bars chromosome is handed over
Fork operation, to intersect individual randomly exchange base, because forming new chromosome, replaces the maximum p of fitness two-by-twoc* M bars
Chromosome, then further according to aberration rate pm, that is, the probable value of the mutation operation for setting, random selection pm* M bars chromosome is become
ETTHER-OR operation, the individual random selection gene point to entering row variation carries out real-valued variation, replaces the maximum p of fitnessm* M bars dyeing
Body;Used as new population, i.e., (n+1 generations) population algebraically of future generation is x (n+1)=x (n) to the population that cross and variation operation is obtained
+ 1, return to step 3);
Step 10) according to the step 8) a series of local air route sequence of points that obtains, first local way point is made
It is Dynamic Programming starting point, using second local way point as Dynamic Programming impact point;
Step 11) according to the dynamic barrier positional information and speed letter during sensor acquisition unmanned surface vehicle navigation
Breath, solves unmanned surface vehicle with the distance to closest point of approach DCPA of barrier and the time TCPA, Ran Hougen of arrival closest point of approach
According to following formula, Risk-Degree of Collision ρ is obtained using DCPA and TCPA weightings:
ρ=(ωDDCPA)2+(ωTTCPA)2
Wherein, ωD、ωTRespectively weighted value;
Step 12) judge the step 11) the Risk-Degree of Collision ρ that obtains whether less than or equal to empirical value, in this way, then root
According to fusion maritime affairs rule Robot dodge strategy and unmanned surface vehicle dynamics constraint condition, adjust unmanned surface vehicle course angle or
Velocity magnitude, otherwise, keeps the original course angle of unmanned surface vehicle and velocity magnitude constant;
Step 13) according to step 12) the course angle knots modification Δ α, the velocity magnitude knots modification Δ V that obtainUSVWith the rule of setting
Time Δ T is drawn, unmanned surface vehicle is calculated and is planned that the current location (X, Y) after a Δ T time is:
In formula, X, Y are respectively after one Δ T time of planning abscissa value of the unmanned surface vehicle in rectangular coordinate system and vertical
Coordinate value, Xprev、YprevAbscissa value and ordinate value of the unmanned surface vehicle in rectangular coordinate system before respectively this time planning,
α、VUSVThe course angle and velocity magnitude of unmanned surface vehicle are represented respectively.
Whether the current location for judging unmanned surface vehicle is current Dynamic Programming impact point, in this way, then into step 14),
Otherwise return to step 11);
Step 14) according to the step 1) the aiming spot Goal that obtains, judging the current location of unmanned surface vehicle is
No is aiming spot Goal, in this way, then terminates this method flow, and otherwise current Dynamic Programming impact point is moved as next
State plans starting point, using next local way point as next Dynamic Programming impact point, return to step 11).
Further, in the inventive method, the step 4) in navigation error model solve every road of chromosome
The specific solution procedure of footpath cost includes:
Navigation error model is:
Xk~N (μk, Σk)
In formula,It is the expectation navigation position of unmanned surface vehicle, Xk=(xk, yk) it is water at k-th gene point
The navigation position of face unmanned boat, ∑kIt is k-th covariance matrix of gene point.
The then navigation position distribution density function of the navigation error modelFor:
The covariance matrix ∑kDetermined according to following formula:
Wherein, εk=3 σk
In formula, εkIt is k-th navigation error of gene point;σkIt is k-th standard deviation of gene point;
Path cost L (the s of every chromosome0, sg) specific calculating process include:
The 3 σ criterions in probabilistic model, the state s at k-th gene pointkNavigation uncertain region in, i.e., with
Expectation navigation position μ at k-th gene pointkFor in the 3 σ confidence regions in the center of circle, sampled N number of sample using Monte Carlo method
Point, obtains the state s at k-th gene pointkNavigation uncertain region in sampling set Θk:
State s at+1 gene point of kthk+1Navigation uncertain region in the N number of sample point of sampling, obtain kth+1
State s at gene pointk+1Navigation uncertain region in sampling set Θk+1:
According to k-th sampling set Θ of gene pointkWith the sampling set Θ of+1 gene point of kthk+1, calculate k-th base
Because point to+1 N of gene point of kth is to the Euclidean distance between sampled point, then N is asked the Euclidean distance between sampled point
Be averaged, obtain k-th gene point to the transfer value L (s of+1 gene point of kthk, sk+1):
State s at initial gene point is calculated according to following formula0State s at target gene pointgEvery chromosome road
Footpath cost L (s0, sg):
Further, in the inventive method, the step 12) in the Robot dodge strategy of fusion maritime affairs rule be:
In formula:ei+fi=1, wherein eiAnd fiBe binary variable, take 0 or 1, ξ be arithmetic number and infinity, when the water surface without
People's ship and barrier right side crossing situation or front meet situation when, avoidance on the right side, now ei=1, fi=0, work as the water surface
When unmanned boat and barrier left side crossing situation or overtaking situation, keep left side avoidance, now ei=0, fi=1;Δ V is water
Velocity V of the face unmanned boat with respect to barrierUO sizes;ΔγUO LWhen being turned left for unmanned surface vehicle, unmanned surface vehicle can
Flee under the premise of collision area, the minimum value of the direction knots modification of relative velocity Δ V;ΔγUO RFor unmanned surface vehicle is turned right
When, unmanned surface vehicle can be fled under the premise of collision area, the maximum of the direction knots modification of relative velocity Δ V;It is the water surface
Velocity V of the unmanned boat with respect to barrierUOTo unmanned surface vehicle velocity VUSVAngle;ΔVUSVIt is unmanned surface vehicle speed
Spend the knots modification of size;Δ α is the knots modification of unmanned surface vehicle course angle;
The step 12) in, according to above-mentioned collision prevention strategy and the Dynamic Constraints of unmanned surface vehicle, using MIXED INTEGER line
Property law of planning, obtains the course angle of unmanned surface vehicle and the adjustment amount of velocity magnitude.
Further, in the inventive method, the step 11) in, for starboard target ωD=5, ωT=0.5, for a left side
Side of a ship target ωD=5, ωT=1.
Further, in the inventive method, the step 12) in, the dynamics constraint condition of unmanned surface vehicle is included most
Big speed, peak acceleration, min. turning radius.In the inventive method, mainly for realizing avoidance, barrier can for path planning
Dynamic and static two kinds of forms are divided, the former (... local static path planning) mainly avoids static-obstacle thing, the latter (... office
Portion's active path planning) main avoiding dynamic barrier.But both are not separate, and the former obtains a series of after static programming
Local way point, can be as the starting point of the latter's Dynamic Programming and impact point.Assuming that static programming obtains a series of comprising starting
The location point S of point Start0With the local air route point sequence S of the location point Sn of impact point Goal0、S1、S2... Sn, then enter action
When state is planned, first with S0As Dynamic Programming starting point, S1As Dynamic Programming impact point, carry out active path planning and reach dynamic
After state object of planning point, then by S1As Dynamic Programming starting point, S2As Dynamic Programming impact point, Dynamic Programming is carried out, constantly
Repeat, stop until reaching impact point Sn.
Beneficial effect:The present invention compared with prior art, with advantages below:
1) compared with prior art, present invention employs local delamination thought, local paths planning is divided into static road
Plan and two aspects of active path planning that static local paths planning can obtain most short with shortest path as optimization aim in footpath
Nothing touch path, dynamic local path planning is optimization aim to flee from the moving region of barrier as early as possible so that the water surface nobody
Security of the ship away from moving obstacle to ensure to navigate by water in real time.Additionally, in algorithm realization, electronic chart and sensor are obtained
Static-obstacle thing information transmission in local static path planning, static state need not be hindered in real time in dynamic local planning
Hindering thing carries out repeating planning, reduces path planning time and EMS memory occupation.
2) compared with prior art, present invention employs the improvement heredity paths planning method constrained based on navigation error,
The navigation information of non-precision is converted into the circumstances not known information in environmental model, is fused in path planning, no longer with accurate
Navigation information is supposed premise, and the influence of navigation error is taken into full account in environmental model, reduces navigation error and path is selected
The adverse effect for causing is selected, the security of path planning is improve.With enumerate, compared with the conventional search methods such as heuristic, change
Entering genetic algorithm has good ability of searching optimum, can go out all satisfaction constraint bars by fast search from path planning space
The nothing of part touches path, without being absorbed in locally optimal solution;The population constituted from all feasible paths is searched for, with potential
Concurrency, can easily carry out Distributed Calculation, accelerate solving speed;When very big for input size, different from tradition side
Method without solution situation, still can effectively solve optimal or approximate optimal solution.
The present invention considers the method local static road of the navigation error influence based on genetic algorithm under VC simulated conditions
Footpath planning carries out emulation experiment:
Genetic algorithm parameter is set:Population Size M=50, crossing-over rate pc=0.8, aberration rate pm=0.1;
Navigation error initial value:ε0=0.1m;
Navigation error rate of change:Δε=0.05;
Obtained using invention methods described, it is as shown in Figure 3 respectively.Result shows under the influence of navigation error is considered, adopts
With the inventive method can during path planning not reselection narrow regions and select to detour to free space, with victim-way
The cost of electrical path length ensures unmanned surface vehicle nevigation safety and feasibility.
Brief description of the drawings
Fig. 1 is FB(flow block) of the invention;
Fig. 2 in the present invention definition of maritime affairs rule conflict scene and difference can meet under scene needed for the measures to keep clear taken
Schematic diagram;
Fig. 3 is unmanned surface vehicle path planning schematic diagram of the present invention based on navigation error;
Fig. 4 is the present invention whether there is the unmanned surface vehicle local static path planning correlation curve for considering navigation error influence
Figure.
Fig. 5 is structured flowchart of the invention.
Specific embodiment
With reference to embodiment and Figure of description, the present invention is further illustrated.
Unmanned surface vehicle local delamination paths planning method based on navigation error constraint of the invention is comprised the following steps:
Step 1) the static-obstacle thing information that is obtained according to job task, electronic chart and sensor, using rectangular co-ordinate
System and Grid Method, by the discrete elementary cell grid for turning to two dimension of environment, environmental modeling are carried out by the sign to these grids,
Initialization start position Start and aiming spot Goal;It is 1 wherein to have the zone marker of barrier, that is, represent obstacle grid,
The zone marker of clear is 0, that is, represent free grid;With starting point as origin, x, y, z has been respectively directed to rectangular coordinate system
The east of initial point, north, day;
Step 2) M bar chromosomes are obtained as follows:Randomly selected except starting point on grating map using randomized
Starting point and impact point, are divided into a+1 sections, then using A* by a free grid point outside position Start and aiming spot Goal
Algorithm connection source, impact point and a free grid point, make a feasible initial solution, that is, obtain one effectively
Chromosome;
Using the M bars chromosome as initial population, as first generation population, wherein M is the scale of first generation population;
Step 3) determine every navigation error of each gene point of chromosome, specially:Using kth in every chromosome
Expectation navigation position μ at individual gene pointkWith the expectation navigation position μ at+1 gene point of kthk+1Between distance to go D
(μk, μk+1) and navigation error rate of change Δε, product calculation is carried out, along with k-th navigation error ε of gene pointk, obtain every
+ 1 navigation error ε of gene point of bar chromosome kthk+1:
fε:εk+1=εk+ΔεD(μk, μk+1)
Then according to Beidou navigation error εBDIf ,+1 navigation error ε of gene point of kthk+1> εBD, then by εBDAssignment
εk+1;Otherwise ,+1 navigation error ε of gene point of kthk+1It is initial value;
Step 4) according to the step 3) every navigation error of each gene point of chromosome is obtained, using Gauss model
As navigation error model, the navigation position distribution density function according to the navigation error modelUsing Meng Teka
Lip river sampling method is calculated every path cost L (s of chromosome0, sg), wherein the minimum value in all chromosome path costs
It is the optimal solution of the generation population;
4.1) navigation error model is:
Xk~N (μk, ∑k)
In formula,It is the expectation navigation position of unmanned surface vehicle, Xk=(xk, yk) it is water at k-th gene point
The navigation position of face unmanned boat, ∑kIt is k-th covariance matrix of gene point.
The then navigation position distribution density function of the navigation error modelFor:
The covariance matrix ∑kDetermined according to following formula:
Wherein, εk=3 σk
In formula, εkIt is k-th navigation error of gene point;σkIt is k-th standard deviation of gene point;
4.2) every path cost L (s of chromosome0, sg) specific calculating process include:
The 3 σ criterions in probabilistic model, the state s at k-th gene pointkNavigation uncertain region in, i.e., with
Expectation navigation position μ at k-th gene pointkFor in the 3 σ confidence regions in the center of circle, sampled N number of sample using Monte Carlo method
Point, obtains the state s at k-th gene pointkNavigation uncertain region in sampling set Θk:
State s at+1 gene point of kthk+1Navigation uncertain region in the N number of sample point of sampling, obtain kth+1
State s at gene pointk+1Navigation uncertain region in sampling set Θk+1:
According to k-th sampling set Θ of gene pointkWith the sampling set Θ of+1 gene point of kthk+1, calculate k-th base
Because point to+1 N of gene point of kth is to the Euclidean distance between sampled point, then N is asked the Euclidean distance between sampled point
Be averaged, obtain k-th gene point to the transfer value L (s of+1 gene point of kthk, sk+1):
State s at initial gene point is calculated according to following formula0State s at target gene pointgEvery chromosome road
Footpath cost L (s0, sg):
Step 5) there will be the danger zone of barrier as collision area A, according to navigation error model, in collision area A
It is interior that navigation error probability density function is integrated, obtain the collision probability at every each gene point of chromosome:
In formula, Pc(Xk) it is collision probability at k-th gene point of every chromosome;
According to above formula obtain every the 0th of chromosome the gene point (i.e. starting point) to g-th gene point (i.e. impact point) it
Between full gene point at collision probability set { Pc(X0), Pc(X1)...Pc(Xg)};
Step 6) according to the step 5) the collision probability set of the full gene point of every chromosome that obtains, for every
Bar chromosome, searches the chromosome in collision probability set { Pc(X0), Pc(X1)...Pc(Xg) in collision probability maximum,
Then as the collision probability P of the chromosomec(X):
Pc(X)=max { Pc(X0), Pc(X1)...Pc(Xg)}
Step 7) according to the step 4) path cost of every chromosome that obtains and the step 6) obtain every
The collision probability P of chromosomec(X) every fitness of chromosome, is calculated in such a way;
Judge the step 6) the collision probability P of every chromosome that obtainsc(X) whether setting value is less than or equal to, such as
It is that every fitness E of chromosome is then calculated according to following formula:
Every fitness E of chromosome is otherwise calculated according to following formula:
Wherein m is weighted value setting value, m > > L (s0, sg);
The fitness of all chromosomes is added and is averaging, as the average adaptive value of the generation population;
Step 8) the n-th generation population algebraically of hypothesis be x (n), if population algebraically x (n) < setting values nsetting, then into step
9);Otherwise, process as follows:
If in n-th-(nsetting- 1) for the continuous n of population to current n-th generation populationsettingGeneration evolve in, population it is optimal
Xie Jun does not change, and the kind group mean adaptive value change of adjacent generations is not above 1%, then will have in current n-th generation
The chromosome of optimal solution touches path as the optimal nothing for being solved, and according to the coordinate value of the gene point for constituting the chromosome
A series of path node sequences from starting point to impact point are obtained, using the path node sequence as after local air route point sequence,
Into step 10), otherwise into step 9);
Step 9) according to crossing-over rate pc, that is, the probable value of the crossover operation for setting, random selection pc* M bars chromosome is handed over
Fork operation, to intersect individual randomly exchange base, because forming new chromosome, replaces the maximum p of fitness two-by-twoc* M bars
Chromosome, then further according to aberration rate pm, that is, the probable value of the mutation operation for setting, random selection pm* M bars chromosome is become
ETTHER-OR operation, the individual random selection gene point to entering row variation carries out real-valued variation, replaces the maximum p of fitnessm* M bars dyeing
Body;Used as new population, i.e., (n+1 generations) population algebraically of future generation is x (n+1)=x (n) to the population that cross and variation operation is obtained
+ 1, return to step 3);
Step 10) according to the step 8) a series of local air route sequence of points that obtains, first local way point is made
It is Dynamic Programming starting point, using second local way point as Dynamic Programming impact point;
Step 11) according to the dynamic barrier positional information and speed letter during sensor acquisition unmanned surface vehicle navigation
Breath, solves unmanned surface vehicle with the distance to closest point of approach DCPA of barrier and the time TCPA, Ran Hougen of arrival closest point of approach
According to following formula, Risk-Degree of Collision ρ is obtained using DCPA and TCPA weightings:
ρ=(ωDDCPA)2+(ωTTCPA)2
Wherein, ωD、ωTRespectively weighted value;Generally, for starboard target ωD=5, ωT=0.5, for a left side
Side of a ship target ωD=5, ωT=1;
Step 12) judge the step 11) the Risk-Degree of Collision ρ that obtains whether less than or equal to empirical value, in this way, then root
According to fusion maritime affairs rule Robot dodge strategy and unmanned surface vehicle dynamics constraint condition, adjust unmanned surface vehicle course angle or
Velocity magnitude, otherwise, keeps the original course angle of unmanned surface vehicle and velocity magnitude constant;
Wherein, according to the Robot dodge strategy of fusion maritime affairs rule, the course angle of unmanned surface vehicle or the tool of velocity magnitude are adjusted
Body process includes:Step 12.1) on the basis of the navigation direction of unmanned surface vehicle, obtain between unmanned surface vehicle and barrier
Heading crossing angle Δ θ, the conflict scene further according to International Maritime rule defines judgement and meets situation type:
Δ θ ∈ if [0 °, 45 °] ∪ [315 °, 360 °), then it is overtaking situation;
If Δ θ ∈ [165 °, 195 °], then for front is met situation;
If Δ θ ∈ (45 °, 165 °), then be right side crossing situation;
If Δ θ ∈ (195 °, 315 °), then be left side crossing situation;
Step 12.2) according to the position vector X of current unmanned surface vehicleUsVWith velocity VUSV, barrier position arrow
Amount XObsWith velocity VObs, it is pole axis direction with north orientation with reference to polar coordinate system, it is counterclockwise just, to be asked by speed avoidance method
The collision prevention model of unmanned surface vehicle is:
In formula, Δ V is velocity V of the unmanned surface vehicle with respect to barrierUOSize;ΔγUO LFor unmanned surface vehicle to the left
When turning, unmanned surface vehicle can be fled under the premise of collision area, the minimum value of the direction knots modification of relative velocity Δ V;ΔγUO R
When being turned right for unmanned surface vehicle, unmanned surface vehicle can be fled under the premise of collision area, the direction knots modification of relative velocity Δ V
Maximum;Velocity V for unmanned surface vehicle with respect to barrierUOTo unmanned surface vehicle velocity VUSVAngle;Δ
VUSVIt is the knots modification of unmanned surface vehicle velocity magnitude;Δ α is the knots modification of unmanned surface vehicle course angle;
Step 12.3) according to the step 12.1) meet situation type and the step 12.3 that obtain) avoidance that obtains
Model, obtaining collision prevention strategy is:
In formula:ei+fi=1, wherein eiAnd fiBe binary variable, take 0 or 1, ξ be arithmetic number and infinity, when the water surface without
People's ship and barrier right side crossing situation or front meet situation when, avoidance on the right side, now ei=1, fi=0, work as the water surface
When unmanned boat and barrier left side crossing situation or overtaking situation, keep left side avoidance, now ei=0, fi=1;
Step 12.4) according to the step 12.3) maximal rate of the collision prevention strategy that obtains and unmanned surface vehicle, most greatly
The Dynamic Constraints such as speed, min. turning radius, using MILP method, obtain unmanned surface vehicle course angle and
The adjustment amount of velocity magnitude.
Step 13) planning time Δ T is voluntarily set according to the performance of unmanned surface vehicle and actual demand, according to step 12)
Course angle knots modification Δ α, the velocity magnitude knots modification Δ V for obtainingUSVWith the planning time Δ T of setting, unmanned surface vehicle rule are calculated
Current location (X, Y) after standardized Δ T time is:
In formula, X, Y are respectively after one Δ T time of planning abscissa value of the unmanned surface vehicle in rectangular coordinate system and vertical
Coordinate value, Xprev、YprevAbscissa value and ordinate value of the unmanned surface vehicle in rectangular coordinate system before respectively this time planning,
α、VUSVThe course angle and velocity magnitude of unmanned surface vehicle are represented respectively.
Whether the current location for judging unmanned surface vehicle is current Dynamic Programming impact point, in this way, then into step 14),
Otherwise return to step 11);
Step 14) according to the step 1) the aiming spot Goal that obtains, judging the current location of unmanned surface vehicle is
No is aiming spot Goal, in this way, then terminates this method flow, and otherwise current Dynamic Programming impact point is moved as next
State plans starting point, using next local way point as next Dynamic Programming impact point, return to step 11).
Above-described embodiment is only the preferred embodiment of the present invention, it should be pointed out that:For the ordinary skill of the art
For personnel, under the premise without departing from the principles of the invention, some improvement and equivalent can also be made, these are to the present invention
Claim be improved with the technical scheme after equivalent, each fall within protection scope of the present invention.
Claims (5)
1. it is a kind of based on navigation error constraint unmanned surface vehicle local delamination paths planning method, it is characterised in that the method
Comprise the following steps:
Step 1) the static-obstacle thing information that is obtained according to job task, electronic chart and sensor, using rectangular coordinate system and
Grid Method, by the discrete elementary cell grid for turning to two dimension of environment, carries out environmental modeling, initially by the sign to these grids
Change start position Start and aiming spot Goal;
Step 2) M bar chromosomes are obtained as follows:Randomly selected except start position on grating map using randomized
Starting point and impact point, are divided into a+1 sections, then using A* algorithms by a free grid point outside Start and aiming spot Goal
Connection source, impact point and a free grid point, make a feasible initial solution, that is, obtain an effective dyeing
Body;
Using the M bars chromosome as initial population, as first generation population, wherein M is the scale of first generation population;
Step 3) determine every navigation error of each gene point of chromosome, specially:Using k-th base in every chromosome
Because at expectation navigation position μkWith the expectation navigation position μ at+1 gene point of kthk+1Between distance to go D (μk,
μk+1) and navigation error rate of change Δε, product calculation is carried out, along with k-th navigation error ε of gene pointk, obtain every dye
+ 1 navigation error ε of gene point of colour solid kthk+1;Then according to Beidou navigation error εBDIf the navigation of+1 gene point of kth is missed
Difference εk+1>εBD, then by εBDAssignment εk+1;Otherwise ,+1 navigation error ε of gene point of kthk+1It is initial value;
Step 4) according to the step 3) every navigation error of each gene point of chromosome is obtained, using Gauss model conduct
Navigation error model, the navigation position distribution density function according to the navigation error modelAdopted using Monte Carlo
Sample method is calculated every path cost L (s of chromosome0,sg), wherein the minimum value in all chromosome path costs is should
For the optimal solution of population;
Step 5) there will be the danger zone of barrier as collision area A, it is right in collision area A according to navigation error model
Navigation error probability density function is integrated, and obtains the collision probability at every each gene point of chromosome, obtains every
The starting point of chromosome to the gene o'clock of impact point, i.e., the 0th to the full gene point between g-th gene point at collision probability collection
Close { Pc(X0),Pc(X1)…Pc(Xg)};
Step 6) according to the step 5) the collision probability set of the full gene point of every chromosome that obtains, for every dye
Colour solid, searches the chromosome in collision probability set { Pc(X0),Pc(X1)…Pc(Xg) in collision probability maximum, then will
Its as the chromosome collision probability Pc(X);
Step 7) according to the step 4) path cost of every chromosome that obtains and the step 6) every dyeing obtaining
The collision probability P of bodyc(X) every fitness of chromosome, is calculated in such a way;
Judge the step 6) the collision probability P of every chromosome that obtainsc(X) whether setting value is less than or equal to, in this way, then
Every fitness E of chromosome is calculated according to following formula:
Every fitness E of chromosome is otherwise calculated according to following formula:
Wherein m is weighted value setting value, m>>L(s0,sg);
The fitness of all chromosomes is added and is averaging, as the average adaptive value of the generation population;
Step 8) the n-th generation population algebraically of hypothesis be x (n), if population algebraically x (n)<Setting value nsetting, then into step 9);It is no
Then, process as follows:
If in n-th-(nsetting- 1) for the continuous n of population to current n-th generation populationsettingDuring generation evolves, the optimal solution of population is equal
Do not change, and the kind group mean adaptive value change of adjacent generations is not above 1%, then will have in current n-th generation optimal
The chromosome of solution touches path as the optimal nothing for being solved, and is worth to according to the coordinate of the gene point for constituting the chromosome
A series of path node sequences from starting point to impact point, using the path node sequence as after local air route point sequence, enter
Step 10), otherwise into step 9);
Step 9) according to crossing-over rate pc, that is, the probable value of the crossover operation for setting, random selection pc* M bars chromosome carries out intersection behaviour
Make, to intersect individual randomly exchange base, because forming new chromosome, replaces the maximum p of fitness two-by-twoc* M bars dyeing
Body, then further according to aberration rate pm, that is, the probable value of the mutation operation for setting, random selection pm* M bars chromosome enters row variation behaviour
Make, the individual random selection gene point to entering row variation carries out real-valued variation, replaces the maximum p of fitnessm* M bars chromosome;Will
Used as new population, i.e., (n+1 generations) population algebraically of future generation is x (n+1)=x (n)+1 to the population that cross and variation operation is obtained, and is returned
Return step 3);
Step 10) according to the step 8) a series of local air route sequence of points that obtains, using first local way point as dynamic
State plans starting point, using second local way point as Dynamic Programming impact point;
Step 11) the dynamic barrier positional information and velocity information during unmanned surface vehicle navigation are obtained according to sensor,
Unmanned surface vehicle and the distance to closest point of approach DCPA of the barrier and time TCPA of arrival closest point of approach are solved, then according under
Formula, Risk-Degree of Collision ρ is obtained using DCPA and TCPA weightings:
ρ=(ωDDCPA)2+(ωTTCPA)2
Wherein, ωD、ωTRespectively weighted value;
Step 12) judge the step 11) whether the Risk-Degree of Collision ρ that obtains, less than or equal to empirical value, in this way, then merges sea
The Robot dodge strategy of thing rule and the dynamics constraint condition of unmanned surface vehicle, the course angle or speed for adjusting unmanned surface vehicle are big
It is small, otherwise, keep the original course angle of unmanned surface vehicle and velocity magnitude constant;
Step 13) according to step 12) the course angle knots modification △ α, the velocity magnitude knots modification △ V that obtainUSVDuring with the planning for setting
Between △ T, calculate unmanned surface vehicle plan a △ T time after current location (X, Y) be:
In formula, X, Y are respectively abscissa value and ordinate of the unmanned surface vehicle in rectangular coordinate system after one △ T time of planning
Value, Xprev、YprevAbscissa value and ordinate value of the unmanned surface vehicle in rectangular coordinate system, α, V before respectively this time planningUSV
The course angle and velocity magnitude of unmanned surface vehicle are represented respectively;
Whether the current location for judging unmanned surface vehicle is current Dynamic Programming impact point, in this way, then into step 14), otherwise
Return to step 11);
Step 14) according to the step 1) the aiming spot Goal that obtains, judge unmanned surface vehicle current location whether be
Aiming spot Goal, in this way, then terminates this method flow, otherwise using current Dynamic Programming impact point as next dynamic rule
Starting point is drawn, using next local way point as next Dynamic Programming impact point, return to step 11).
2. it is according to claim 1 based on navigation error constraint unmanned surface vehicle local delamination paths planning method, its
It is characterised by, described step 4) in, navigation error model is:
Xk~N (μk,Σk)
In formula,It is the expectation navigation position of unmanned surface vehicle, Xk=(xk,yk) at k-th gene point the water surface without
The navigation position of people's ship, ΣkIt is k-th covariance matrix of gene point;
The then navigation position distribution density function of the navigation error modelFor:
The covariance matrix ΣkDetermined according to following formula:
Wherein, εk=3 σk
In formula, εkIt is k-th navigation error of gene point;σkIt is k-th standard deviation of gene point;
Path cost L (the s of every chromosome0,sg) specific calculating process include:
The 3 σ criterions in probabilistic model, the state s at k-th gene pointkNavigation uncertain region in, i.e., with k-th
Expectation navigation position μ at gene pointkIn the 3 σ confidence regions in the center of circle, using the Monte Carlo method N number of sample point of sampling, to obtain
State s at k-th gene pointkNavigation uncertain region in sampling set Θk:
State s at+1 gene point of kthk+1Navigation uncertain region in the N number of sample point of sampling, obtain+1 gene of kth
State s at pointk+1Navigation uncertain region in sampling set Θk+1:
According to k-th sampling set Θ of gene pointkWith the sampling set Θ of+1 gene point of kthk+1, calculate k-th gene point
To+1 N of gene point of kth to the Euclidean distance between sampled point, then N is taken to the Euclidean distance summation between sampled point
Averagely, k-th gene point to the transfer value L (s of+1 gene point of kth is obtainedk,sk+1):
State s at initial gene point is calculated according to following formula0State s at target gene pointgEvery chromosome path generation
Valency L (s0,sg):
3. it is according to claim 1 based on navigation error constraint unmanned surface vehicle local delamination paths planning method, its
Be characterised by, the step 12) in fusion maritime affairs rule Robot dodge strategy be:
In formula:ei+fi=1, wherein eiAnd fiBe binary variable, take 0 or 1, ξ be arithmetic number and infinity, work as unmanned surface vehicle
With barrier right side crossing situation or front meet situation when, avoidance on the right side, now ei=1, fi=0, when the water surface nobody
When ship and barrier left side crossing situation or overtaking situation, keep left side avoidance, now ei=0, fi=1;Δ V be the water surface without
Velocity V of people's ship with respect to barrierUOSize;When being turned left for unmanned surface vehicle, unmanned surface vehicle can flee from and touch
Under the premise of hitting region, the minimum value of the direction knots modification of relative velocity Δ V;When being turned right for unmanned surface vehicle, the water surface without
People's ship can be fled under the premise of collision area, the maximum of the direction knots modification of relative velocity Δ V;For unmanned surface vehicle is relative
The velocity V of barrierUOTo unmanned surface vehicle velocity VUSVAngle;ΔVUSVIt is changing for unmanned surface vehicle velocity magnitude
Variable;Δ α is the knots modification of unmanned surface vehicle course angle;
The step 12) in, according to above-mentioned collision prevention strategy and the Dynamic Constraints of unmanned surface vehicle, using MIXED INTEGER linear gauge
The method of drawing, obtains the course angle of unmanned surface vehicle and the adjustment amount of velocity magnitude.
4. according to claim 1,2 or 3 based on navigation error constraint unmanned surface vehicle local delamination path planning side
Method, it is characterised in that the step 11) in, for starboard target ωD=5, ωT=0.5, for larboard target ωD=5, ωT
=1.
5. according to claim 1,2 or 3 based on navigation error constraint unmanned surface vehicle local delamination path planning side
Method, it is characterised in that the step 12) in, the dynamics constraint condition of unmanned surface vehicle includes maximal rate, maximum acceleration
Degree, min. turning radius.
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