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 PDF

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CN106845716A
CN106845716A CN201710055413.3A CN201710055413A CN106845716A CN 106845716 A CN106845716 A CN 106845716A CN 201710055413 A CN201710055413 A CN 201710055413A CN 106845716 A CN106845716 A CN 106845716A
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程向红
刘钰
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Southeast University
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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

A kind of unmanned surface vehicle local delamination paths planning method based on navigation error constraint
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：
E = 1 L ( s 0 , s g ) ;
Every fitness E of chromosome is otherwise calculated according to following formula：
E = 1 m L ( s 0 , s g ) ;
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：
X = ( V U S V + &Delta;V U S V ) s i n ( &alpha; + &Delta; &alpha; ) &times; &Delta; T + X p r e v Y = ( V U S V + &Delta;V U S V ) c o s ( &alpha; + &Delta; &alpha; ) &times; &Delta; T + Y p r e v
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：
p &mu; k , &Sigma; k ( X k ) = 1 2 &pi; | &Sigma; k | e - 1 2 ( X k - &mu; k ) T &Sigma; k - 1 ( X k - &mu; k )
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：
&Theta; k = &Sigma; i = 1 N X k , i
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：
&Theta; k + 1 = &Sigma; i = 1 N X k + 1 , i
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)：
L ( s k , s k + 1 ) = 1 N &Sigma; i = 1 N D ( X k , i , X k + 1 , i )
State s at initial gene point is calculated according to following formula0State s at target gene pointgEvery chromosome path generation Valency L (s0,sg)：
L ( s 0 , s g ) = &Sigma; k = 0 g - 1 L ( s k , s k + 1 ) .
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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