CN111610788A - Hierarchical fuzzy-artificial potential field path planning method - Google Patents
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Abstract
The invention provides a method for planning a hierarchical fuzzy-artificial potential field path, which comprises the following steps: s1, modeling an environment; s2, executing an improved elite genetic algorithm, and planning the global path of the unmanned ship; s3, executing a fuzzy-artificial potential field algorithm with fuzzy decision, and planning a local path of the unmanned ship; and S4, inserting a virtual return point on the unmanned ship global path, and fusing the unmanned ship global path and the unmanned ship local path. The method combines elite reservation, diversity increment, self-adaptive mutation probability and self-adaptive genetic algorithm to generate the optimal sparse path point and smooth the path. In order to completely adapt to unpredictable environments, a fuzzy-artificial potential field algorithm with fuzzy decision is developed, and the unmanned ship is prevented from being trapped in singular points. And inserting a virtual return point on the global path to enable the global path and the local path to be perfectly fused. The method has higher safety and flexibility, and the unmanned ship can safely and quickly complete the optimal path planning.
Description
Technical Field
The invention relates to the technical field of unmanned ships, in particular to a hierarchical fuzzy-artificial potential field path planning method.
Background
The development of oceans is becoming important today where land resources are becoming scarce. The unmanned surface vehicle is important equipment for performing surface operation and is very important for ocean resource development. Path planning for unmanned ships can be divided into global planning and local planning, wherein algorithms commonly used for global planning include grid map-based algorithms such as a-x algorithm, Dijkstra algorithm, etc., but the above algorithms are easy to generate paths which are not practical. Intelligent search algorithms, such as: genetic algorithms, ant colony algorithms, particle swarm algorithms, and the like. And according to the inspiration of the nature, obtaining an unobstructed path through continuous iterative computation. For complex marine environments, a local planning algorithm for dynamic obstacle avoidance is also an extremely important part of path planning, and common local path planning algorithms include an artificial potential field method, a D-star algorithm, a dynamic window method and the like. The artificial potential field method is a local path planning algorithm widely applied at present due to simple mathematical model and small calculated amount, but the algorithm is easy to enter a local minimum point. The D algorithm is too computationally inefficient and also belongs to a heuristic algorithm, which easily produces paths that are not practically feasible. The curvature of the path generated by the dynamic window method is always changed, and the energy consumption of the unmanned ship is increased.
Disclosure of Invention
In accordance with the technical problem set forth above, a method for hierarchical fuzzy-artificial potential field path planning is provided. The technical means adopted by the invention are as follows:
a method of hierarchical fuzzy-artificial potential field path planning, comprising the steps of:
s1, modeling an environment;
s2, executing an improved elite genetic algorithm, and planning the global path of the unmanned ship;
s3, executing a fuzzy-artificial potential field algorithm with fuzzy decision, and planning a local path of the unmanned ship;
and S4, inserting a virtual return point on the unmanned ship global path, and fusing the unmanned ship global path and the unmanned ship local path.
Further, the step S1 is modeling an environment, namely modeling a detection area of the unmanned ship, and detecting a position of the obstacle by defining the detection area of the unmanned ship so as to make a path of the unmanned ship far away from the obstacle; specifically, the method comprises the following steps:
s11, defining the detection area of the unmanned ship as follows:
wherein d issRadius of detection, p, for unmanned shipsb＝[xb,yb]TIs the position of the unmanned ship, p (t) ═ x, y]TThe position of the monitored point in the environment; x is the number ofb,ybRespectively representing the horizontal coordinate and the vertical coordinate of the current unmanned ship, and x and y respectively representing the horizontal coordinate and the vertical coordinate of a detected point;
s12, setting a navigation area:
wherein the content of the first and second substances,
in order to be a feasible region of the network,
is an infeasible area;
is a detection area of the unmanned ship,
is a navigation area;
s13, in theObtaining a slave starting point p in the set navigation areaS＝[xs,ys]TTo the end point pE＝[xE,yE]TA collision-free path of (a); x is the number ofS,ySRespectively representing the abscissa and ordinate of the origin, xE,yERespectively represent the horizontal and vertical coordinates;
s14, using the formula h (p) 0.5r (p) +0.5g (p) +0b (p) to differentiate the no-pass area and the free-run area by performing grid binarization on the original color map, so as to obtain the corresponding binary map
Wherein R (p), G (p) and B (p) are the red, green and blue three-color values corresponding to the p point, respectively, H (p) is the average value of the gray values in the surrounding 10 pixels, g (p)s) 0 represents that the region is a feasible region, g (p)s) 255 represents that the region is an infeasible region; finally obtaining feasible area
And a non-feasible region
Further, in step S2, an improved artificial potential field algorithm is executed, and the method for planning the global path of the unmanned ship includes the following sub-steps:
s21, calculating the individual self-adaptive degree;
s22, simplifying the complexity of global path planning through a genetic algorithm by adopting a decimal coding mode;
s23, performing elite reservation operation;
s24, executing diversified increment operation;
and S25, performing selection, crossing and adaptive mutation operations.
Further, the method for calculating the fitness of the individual in the step S21 includes:
adopting a fitness function containing a B spline, wherein the fitness function is designed as follows:
wherein l, Δ are constants and Δ > 0;
are the control points of the basis functions in the B-spline,
is the nth waypoint in the ith iteration;
Lpis the path length:
lpis the closest distance between a certain waypoint and the obstacle,/pExpressed by euclidean distance as:
wherein the content of the first and second substances,
is a distance waypoint
The nearest obstacle position of;
respectively showing the abscissa and ordinate of the nth barrier;
wherein the content of the first and second substances,
a set of positive integers is represented that,
representing the set of path points in the ith iteration, pd,i(u)＝[x(u),y(u)]T,
Represents a real number set, an
Vector of nodes
Cutting the raw materials into pieces;
denotes the Nthw+ k +1 nodes, ΩθIs a non-decreasing node sequence; n is a radical ofi,k(u) is a B-spline basis function of order i k defined by the Boor-Cox recursive function, expressed as follows:
wherein the content of the first and second substances,
represents a sequence of nodes that is not decremented, and
further, the method for performing the elite reservation operation in the step S23 includes:
calculating the fitness of all individuals according to a path length formula, wherein the fitness is more than 0 and less than Pe:＝ne/NpAnd (3) the elite retention rate is less than 1, and all individuals are sorted according to the fitness and subjected to elite retention. Wherein, PeRepresenting the elite retention, neIndicates the number of Elite individuals retained, NpRepresenting the total number of individuals.
Further, the method for performing diversified increment operations in step S24 includes:
by adopting a formula of 0 < Pd:＝nd/Np(ii) a diversified increment ratio from feasible region of < 1 >
In random generation of nd·NwOrderly combination of candidate waypoints as ndAn individual, wherein PdDenotes the diversity increment ratio, ndIndicating an increased number of individuals.
Further, the method for performing selection, crossover, adaptive mutation operations in step S25 includes:
s251, perform selection operation, i.e.
Individuals with high fitness values are reserved in a wheel probability selection mode, the individuals with high fitness values have higher selection probability to enter the next generation, and therefore the probability that the ith individual in the population is selected is as follows:
wherein (p)d,i) Calculating according to a formula of a fitness function;
s252, performing a crossover operation, i.e.
The process of gene recombination, i.e. generating new chromosome, of chromosome between different individuals is expressed by the following crossover operations:
aqj＝aqj(1-b)aljb
alj＝alj(1-b)akjb
wherein, aqj,aljB ∈ [0,1 ] representing the values of the j-th gene in the q-th and l-th chromosomes, respectively]The random number of (2);
s253, performing adaptive mutation operation, namely
And (3) adopting an adaptive mutation operation to determine the mutation probability of the individual through the adaptive value of the individual:
wherein, Pm,maxAnd Pm,minRespectively representing the maximum and minimum mutation probabilities of the individuals; (p)d,max) And (p)d,avg) The maximum and mean fitness values in the population are indicated separately.
Further, the fuzzy-artificial potential field algorithm with fuzzy decision executed in the step S3 has a function expression as follows:
where ζ is a self-correcting blurring factor, ρ0The maximum radius of the acting range of the repulsive force field of the obstacle; in which the repulsive force correction factor is such that, as the two components of the repulsive force act, the obstacleThe repulsive force to the unmanned ship is gradually reduced, and the attractive force of the target point to the unmanned ship is gradually increased, so that lambda ∈ (0,1), krAnd kaIs the thrust and attraction coefficients, ρλ(px,pE) The power of λ, ρ (p), representing the distance of the USV to the target pointb,pE)ρ(pb,pobs) The distances from the obstacle and the target point to the current position, respectively, are:
ρ(px,p*)＝||px,p*||2
in order to ensure that the unmanned ship can safely complete local collision avoidance and quickly return to a preset global course, the following three aspects are analyzed:
1) when the obstacle is not on the global path
When the unmanned ship is in the action range of the repulsive force field of the obstacle, the corrected repulsive force field is respectively opposite to rho (p)x,pobs) And ρλ(px,pE) Derivation to obtain two components of the corrected repulsive force
And
the expression form is as follows:
where ρ (p)x,pobs) Distance of obstacle to USV, ρ (p)x,pE) Is the distance of the target point to the USV, vector component
In the direction of the obstacle pointing to the unmanned ship, vector component
The direction of the unmanned ship is the motion direction of the unmanned ship and the safety area dsThe tangential direction of (a);
the gravitational component is the negative gradient of its potential field, expressed as follows:
from this, the resultant force FhIs a repulsive force
And the gravitational force FaThe sum of (1):
2) when the obstacle is on the global path
By component of repulsive force
And component of repulsive force
By changing the resultant force F of the force applied by the potential fieldhThe USV can successfully bypass the barrier to complete local path planning, the distance between the USV and the barrier is gradually reduced, the self-correction fuzzy factor zeta is increased, the repulsive force of the barrier is increased, and the navigation safety of the USV is ensured.
3) Fast return to global path after collision avoidance
When unmanned ship safety range dsWhen no obstacle is detected in the unmanned ship, the dynamic collision avoidance is proved to be completed, at the moment, the potential field of the obstacle and a target point is closed, a course decision angle gamma is introduced, and the unmanned ship is enabled to quickly return to a preset global path by setting attraction points Q and K, specifically:
making a ray with an included angle gamma from the current position of the unmanned ship to a global path, intersecting the global path at a point Q, taking a point K on the global path at a distance eta in front of the Q, and introducing a gravitational potential field function at the point Q and the point K respectively, wherein the gravitational potential field function is defined as:
in the formula, kaProcessing the negative gradient of the gravitational field to obtain gravitational force F as gravitational force potential field gain factor psi ∈ { Q, K }t(Ψ) is:
since the attraction force is proportional to the distance between the USV and the reference point Ψ, it is possible to ensure that the USV can safely and smoothly return to the global path, since the distance between the USV and the point K is always greater than the point Q, and further, the direction of the attraction force is always biased toward the point K and directed toward the global path, so that the USV can pass through a certain point between the point Q and the point K, so that the return angle can be restricted to conform to the motion characteristics of the USV.
Compared with the prior art, the invention has the following advantages:
1. according to the method for planning the hierarchical fuzzy-artificial potential field path, the overall path planning can take both the path length and the safety into consideration.
2. According to the method for planning the hierarchical fuzzy-artificial potential field path, provided by the invention, the fuzzy coefficient is adopted for local planning, the situation that the target cannot be reached is avoided, and the collision avoidance performance is optimized.
3. The method for planning the hierarchical fuzzy-artificial potential field path provided by the invention adopts a smoother path to return to the global path, and avoids generating an overlarge course angle.
4. The method for planning the hierarchical fuzzy-artificial potential field path can better cope with complex and changeable marine environments, and improves the capability of real-time risk avoidance of the unmanned ship.
For the above reasons, the present invention can be widely applied to the field of unmanned ships and the like.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a schematic view of the detection range of the unmanned ship provided by the embodiment of the invention.
Fig. 3 is a schematic diagram of coefficient distribution of basis functions according to an embodiment of the present invention.
FIG. 4 is a schematic diagram of a genetic algorithm encoding method provided in an embodiment of the present invention.
Fig. 5 is an overall framework of the adaptive genetic algorithm provided by the embodiment of the present invention.
FIG. 6 is a schematic diagram of a chromosome crossing process provided by an embodiment of the present invention.
Fig. 7 is a schematic view of a vector direction of the correction force according to the embodiment of the present invention.
Fig. 8 is a schematic diagram illustrating a repulsive force function introduced into a moving direction of the unmanned ship according to an embodiment of the present invention.
Fig. 9 is a schematic diagram of an obstacle not in a global path according to an embodiment of the present invention.
Fig. 10 is a schematic diagram of a global path of an obstacle according to an embodiment of the present invention.
Fig. 11 is a schematic diagram of a fast return global path of the unmanned ship according to the embodiment of the present invention.
Fig. 12 is a real world chart according to an embodiment of the present invention.
Fig. 13 is a binarized chart according to an embodiment of the present invention.
Fig. 14 is a comparison chart of the AGA and other global path planning algorithms according to the embodiment of the present invention.
Fig. 15 is a fitness curve of AGA in two environments according to an embodiment of the present invention.
Fig. 16 is a schematic diagram of a hybrid path comparison according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The invention provides a method for planning a hierarchical fuzzy-artificial potential field path, which comprises the following steps: s1, modeling an environment; s2, executing an improved elite genetic algorithm, and planning the global path of the unmanned ship; s3, executing a fuzzy-artificial potential field algorithm with fuzzy decision, and planning a local path of the unmanned ship; and S4, inserting a virtual return point on the unmanned ship global path, and fusing the unmanned ship global path and the unmanned ship local path. The method combines elite reservation, diversity increment, self-adaptive mutation probability and self-adaptive genetic algorithm to generate the optimal sparse path point and smooth the path. In order to completely adapt to unpredictable environments, an innovative fuzzy-artificial potential field algorithm with fuzzy decision is developed, and the situation that the unmanned ship is trapped in singular points is avoided. And inserting a virtual return point on the global path to enable the global path and the local path to be perfectly fused. The designed path planning method has higher safety and flexibility, so that the unmanned ship can safely and quickly complete the optimal path planning.
The embodiments of the present invention will be described in detail with reference to the accompanying drawings.
A method of hierarchical fuzzy-artificial potential field path planning, as shown in fig. 1, comprising the steps of:
s1, modeling an environment;
s2, executing an improved elite genetic algorithm, and planning the global path of the unmanned ship;
s3, executing a fuzzy-artificial potential field algorithm with fuzzy decision, and planning a local path of the unmanned ship;
and S4, inserting a virtual return point on the unmanned ship global path, and fusing the unmanned ship global path and the unmanned ship local path.
The step S1 of modeling environment, namely modeling a detection area of the unmanned ship, and detecting the position of the obstacle by defining the detection area of the unmanned ship so as to enable the path of the unmanned ship to be far away from the obstacle; specifically, the method comprises the following steps:
s11, as shown in fig. 2, defining a detection area of the unmanned ship as follows:
wherein d issRadius of detection, p, for unmanned shipsb＝[xb,yb]TIs the position of the unmanned ship, p (t) ═ x, y]TThe position of the monitored point in the environment; x is the number ofb,ybRespectively representing the horizontal coordinate and the vertical coordinate of the current unmanned ship, and x and y respectively representing the horizontal coordinate and the vertical coordinate of a detected point;
s12, setting a navigation area:
wherein the content of the first and second substances,
in order to be a feasible region of the network,
is an infeasible area;
is a detection area of the unmanned ship,
is a navigation area;
s13, obtaining a starting point p in the set navigation areaS＝[xS,yS]TTo the end point pE＝[xE,yE]TA collision-free path of (a); x is the number ofS,ySRespectively representing the abscissa and ordinate of the origin, xE,yERespectively represent the horizontal and vertical coordinates;
s14, using the formula h (p) 0.5r (p) +0.5g (p) +0b (p) to differentiate the no-pass area and the free-run area by performing grid binarization on the original color map, so as to obtain the corresponding binary map
Wherein R (p), G (p) and B (p) are the red, green and blue three-color values corresponding to the p point, respectively, H (p) is the average value of the gray values in the surrounding 10 pixels, g (p)s) 0 represents that the region is a feasible region, g (p)s) 255 represents that the region is an infeasible region; finally obtaining feasible area
And a non-feasible region
In step S2, an improved artificial potential field algorithm is executed, and the method for planning the global path of the unmanned ship includes the following sub-steps:
s21, calculating the individual self-adaptive degree;
s22, simplifying the complexity of global path planning through a genetic algorithm by adopting a decimal coding mode; the specific encoding scheme is shown in FIG. 4, where φ*，*∈{1,2，...，NWThe ordinal number is the sequential route point serial number in the navigation area;
s23, performing elite reservation operation;
s24, executing diversified increment operation;
and S25, performing selection, crossing and adaptive mutation operations.
Specifically, the method for calculating the fitness of the individual in step S21 includes:
and adopting a fitness function containing a B spline to ensure that the final smooth global path is safe. The fitness function is designed as:
wherein l, Δ are constants and Δ > 0;
are the control points of the basis functions in the B-spline,
is the nth waypoint in the ith iteration;
Lpis the path length:
lpis aDistance between each path point and the obstacle,/pExpressed by euclidean distance as:
wherein the content of the first and second substances,
is a distance waypoint
The nearest obstacle position of;
respectively showing the abscissa and ordinate of the nth barrier;
wherein the content of the first and second substances,
a set of positive integers is represented that,
representing the set of path points in the ith iteration, pd,i(u)＝[x(u),y(u)]T,
Represents a real number set, an
Vector of nodes
Cutting the raw materials into pieces;
denotes the Nthw+ k +1 nodes, ΩθIs a non-decreasing node sequence; as shown in fig. 3，Ni,k(u) is a B-spline basis function of order i k defined by the Boor-Cox recursive function, expressed as follows:
wherein the content of the first and second substances,
represents a sequence of nodes that is not decremented, and
specifically, the method for executing the elite reservation operation in step S23 includes:
the whole genetic algorithm process is shown in FIG. 5, and when the retention operation is performed, fitness calculation is performed on all individuals through a path length formula, and the fitness is calculated according to the condition that 0 is less than Pe:＝ne/NpAnd (3) the elite retention rate is less than 1, and all individuals are sorted according to the fitness and subjected to elite retention. Wherein, PeRepresenting the elite retention, neIndicates the number of Elite individuals retained, NpRepresenting the total number of individuals.
Specifically, the method for executing the diversified increment operation in step S24 includes:
in order to avoid the premature genetic algorithm and enable the algorithm to have certain exploration capacity, P is more than 0d:＝nd/Np(ii) a diversified increment ratio from feasible region of < 1 >
In random generation of ndNwOrderly combination of candidate waypoints as ndAnd (4) individuals. Wherein, PdDenotes the diversity increment ratio, ndIndicating an increased number of individuals.
Specifically, the method for performing the selection, intersection and adaptive mutation operations in step S25 includes:
s251, perform selection operation, i.e.
The selection operation is similar to the operation retention operation, individuals with high fitness values are retained in a roulette probability selection mode, and the individuals with high fitness values have higher selection probability to enter the next generation, so that the probability that the ith individual in the population is selected is as follows:
wherein (p)d,i) Calculating according to a formula of a fitness function;
s252, performing a crossover operation, i.e.
As shown in fig. 6, the process of gene recombination, i.e. generating new chromosome, between different individuals is represented by the following crossover operations:
aqj＝aqj(1-b)aljb
alj＝alj(1-b)akjb
wherein, aqj,aljB ∈ [0,1 ] representing the values of the j-th gene in the q-th and l-th chromosomes, respectively]The random number of (2);
s253, performing adaptive mutation operation, namely
In order to further increase the diversity of the population and to obtain a better pathway in the population, the individual may be subjected to genetic mutation, i.e., mutation manipulation. In addition, in order to ensure that excellent individuals are not destroyed by the operation, an adaptive mutation operation is adopted, and the mutation probability of an individual is determined by an adaptive value of the individual:
wherein, Pm,maxAnd Pm,minRespectively representing the maximum and minimum mutation probabilities of the individuals; (p)d,max) And (p)d,avg) The maximum and mean fitness values in the population are indicated separately.
The fuzzy-artificial potential field algorithm with fuzzy decision-making executed in the step S3 includes a completely new repulsion function, and completes accurate collision avoidance through a fuzzy control algorithm, so that the unmanned ship smoothly avoids unknown obstacles, and the function expression is as follows:
ζ is a self-correcting fuzzy factor, and specific values are shown in table 1:
the limiting conditions are as follows: OB*∈ { F, L, R, U }: relative position of the obstacle to the USV, including front, left, right, and rear, D*∈ {1,2,3 }: distance of obstacle from USV, dsThe distance between obstacles is 0-0.5 d in the detection range of the unmanned shipsTime corresponding to d1When the distance between obstacles is 0.5-0.75 dsTime corresponding to d2When the distance between obstacles is 0.75-1.0 dsTime corresponding to d3(ii) a O, Y and Z are the heading of the unmanned ship and respectively correspond to straight going, left turning and right turning, and the values of zeta comprise 1.4, 1.2, 1.0, 0.8, 0.6, 0.4 and 0.2; rho0Is the maximum radius of the action range of the repulsive force potential field of the obstacle, wherein the repulsive force correction coefficient is the maximum radius, the repulsive force of the obstacle to the unmanned ship is gradually reduced along with the action of two components of the repulsive force, the attractive force of the target point to the unmanned ship is gradually increased, so lambda ∈ (C)0,1)，krAnd kaIs the thrust and attraction coefficients, ρλ(px,pE) The power of λ, ρ (p), representing the distance of the USV to the target pointb,pE)ρ(pb,pobs) The distances from the obstacle and the target point to the current position, respectively, are:
ρ(px,p*)＝||px,p*||2
where ρ (p)x,pobs) The potential field direction of the barrier is that the barrier points to the unmanned ship. ρ (p)x,pE) Potential field of
The direction is as shown in fig. 7, that is, when the unmanned ship detects an obstacle, the unmanned ship is taken as a starting point, a tangent line is made to the circular collision area of the obstacle, and a deviation angle β which is smaller than the moving direction angle at the moment of the unmanned ship is formed2Direction is taken as rho (p)x,pE) When the obstacle is close to the USV, or when the angle α between the obstacle and the direction of movement of the USV is small, the repelling effect of the obstacle should be increased appropriately, as shown in fig. 8.
In order to ensure that the unmanned ship can safely complete local collision avoidance and quickly return to a preset global course, the following three aspects are analyzed:
1) when the obstacle is not on the global path
When the unmanned ship is in the action range of the repulsive force field of the obstacle, the corrected repulsive force field is respectively opposite to rho (p)x,pobs) And ρλ(px,pE) Derivation to obtain two components of the corrected repulsive force
And
the expression form is as follows:
where ρ (p)x,pobs) Distance of obstacle to USV, ρ (p)x,pE) Is the distance from the target point to the USV, the specific direction is shown in FIG. 9, the vector component
In the direction of the obstacle pointing to the unmanned ship, vector component
The direction of the unmanned ship is the motion direction of the unmanned ship and the safety area dsThe tangential direction of (a);
the gravitational component is the negative gradient of its potential field, expressed as follows:
from this, the resultant force FhIs a repulsive force
And the gravitational force FaThe sum of (1):
2) when the obstacle is on the global path
As shown in fig. 10, by the repulsive force component
And component of repulsive force
By changing the resultant force F of the force applied by the potential fieldhThe USV can successfully bypass the barrier to complete local path planning, and the problem that a target point in the traditional APF is inaccessible is solved. At this time, the USVThe distance between the self-correcting fuzzy factor zeta and the obstacle is gradually reduced, the repulsive force of the obstacle is increased, and the navigation safety of the USV is guaranteed.
3) Fast return to global path after collision avoidance
When unmanned ship safety range dsWhen no obstacle is detected in the unmanned ship, the dynamic collision avoidance is proved to be completed, at the moment, the potential field of the obstacle and a target point is closed, a course decision angle gamma is introduced, and the unmanned ship is enabled to quickly return to a preset global path by setting attraction points Q and K, specifically:
as shown in fig. 11, a ray with an angle γ is made from the current position of the unmanned ship to the global path, the global path is intersected to a point Q, and a point K is taken on the global path at a distance η in front of Q, and at this time, a gravitational potential field function is introduced at the point Q and the point K, which is defined as:
in the formula, kaProcessing the negative gradient of the gravitational field to obtain gravitational force F as gravitational force potential field gain factor psi ∈ { Q, K }t(Ψ) is:
since the attraction force is proportional to the distance between the USV and the reference point Ψ, it is possible to ensure that the USV can safely and smoothly return to the global path, since the distance between the USV and the point K is always greater than the point Q, and further, the direction of the attraction force is always biased toward the point K and directed toward the global path, so that the USV can pass through a certain point between the point Q and the point K, so that the return angle can be restricted to conform to the motion characteristics of the USV.
Simulation example:
to demonstrate the effectiveness and superiority of the proposed hybrid path planning scheme, local and hybrid path planning simulations were performed in two real geographical areas, rubble gate and mondshuria in hong kong, china, with map preprocessing as shown in fig. 12-13. The simulation was performed in MATLAB R2018a simulation environment with the simulation parameters as shown in table 2:
example 1: global path
The provided AGA algorithm generates a continuous path from a starting point to a terminal point, and the continuous path is compared with a path generated by performing B spline interpolation fitting on discrete route points generated by a particle swarm optimization algorithm and an ant colony optimization algorithm. As can be seen from table 2 and fig. 14, in the red stoneway environment, the length of the path obtained by using the AGA algorithm is similar to that of the PSO, but the path obtained by the PSO is closer to the obstacle, so that the navigation safety cannot be ensured. For the path generated by the ACO, too many waypoints are generated, so that the path finally obtained by the B-spline has too many inflection points, which does not conform to the motion characteristics of the USV. In the mondsand environment, the AGA algorithm is ideal in terms of both path length and path security. In addition, the adaptive genetic algorithm proposed by the patent is least time-consuming in terms of convergence speed and path length, and it can be seen in fig. 15 that 12 iterations or so converge in both environments. While the diversification of increments can cause small fluctuations, this is only a normal exploration of the environment. In summary, the proposed AGA path length is not necessarily the shortest, but can guarantee security.
Example 2: hybrid algorithm
Compared with the DWA algorithm, the proposed IAPF algorithm has enough safety performance in dealing with static obstacles that are not on the global path, although the IAPF algorithm has a slight difference in obstacle distance compared with the DWA. As shown in fig. 16(a), a static obstacle exists in the global path, and both the IAPF algorithm and the DWA algorithm operate in the same and timely manner while avoiding the obstacle, but when returning to the global path, the DWA algorithm returns to the original path more quickly, and a large turning angle occurs, which is not in accordance with the unmanned ship motion characteristics. The proposed IAPF algorithm returns to the global path at a small angle by increasing the local path, but ensures the USV path to be smooth and avoids large-angle turning. The flight trajectories of the IAPF and DWA algorithms under dynamic obstacles are shown in fig. 16 (b). When a dynamic obstacle is encountered, the IAPF algorithm selects to navigate downwards when the obstacle does not reach the global path, and when the obstacle reaches the global path and continues to move forwards, the IAPF algorithm selects to control the USV to bypass from the rear of the dynamic obstacle. But the DWA still chooses to pass ahead of dynamic obstacles, with a longer trajectory than IAPF, increasing the risk of collision with the obstacle.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. A method for hierarchical fuzzy-artificial potential field path planning is characterized by comprising the following steps:
s1, modeling an environment;
s2, executing an improved elite genetic algorithm, and planning the global path of the unmanned ship;
s3, executing a fuzzy-artificial potential field algorithm with fuzzy decision, and planning a local path of the unmanned ship;
and S4, inserting a virtual return point on the unmanned ship global path, and fusing the unmanned ship global path and the unmanned ship local path.
2. The method for hierarchical fuzzy-artificial potential field path planning according to claim 1, wherein said step S1 environmental modeling, namely modeling the unmanned ship ' S detection area, detecting the position of the obstacle by defining the unmanned ship ' S detection area, and keeping the unmanned ship ' S path away from the obstacle; specifically, the method comprises the following steps:
s11, defining the detection area of the unmanned ship as follows:
wherein d issRadius of detection, p, for unmanned shipsb＝[xb,yb]TIs the position of the unmanned ship, p (t) ═ x, y]TThe position of the detected point in the environment; x is the number ofb,ybRespectively representing the horizontal coordinate and the vertical coordinate of the current unmanned ship, and x and y respectively representing the horizontal coordinate and the vertical coordinate of a detected point;
s12, setting a navigation area:
wherein the content of the first and second substances,
in order to be a feasible region of the network,
is an infeasible area;
is a detection area of the unmanned ship,
is a navigation area;
s13, obtaining a starting point p in the set navigation areaS＝[xs,ys]TTo the end point pE＝[xE,yE]TA collision-free path of (a); x is the number ofS,ySRespectively representing the abscissa and ordinate of the origin, xE,yERespectively represent the horizontal and vertical coordinates;
s14 using the formula h (p) 0.5R (p) +0.5G (p) +0B (p) carrying out grid binarization on the original color map to distinguish a no-pass area from a free-running area so as to obtain a corresponding binarization map
Wherein R (p), G (p) and B (p) are the red, green and blue three-color values corresponding to the p point, respectively, H (p) is the average value of the gray values in the surrounding 10 pixels, g (p)s) 0 represents that the region is a feasible region, g (p)s) 255 represents that the region is an infeasible region; finally obtaining feasible area
And a non-feasible region
3. The method for hierarchical fuzzy-artificial potential field path planning according to claim 1, wherein said step S2 is implemented with an improved artificial potential field algorithm, and the method for planning global path of unmanned ship comprises the following sub-steps:
s21, calculating the individual self-adaptive degree;
s22, simplifying the complexity of global path planning through a genetic algorithm by adopting a decimal coding mode;
s23, performing elite reservation operation;
s24, executing diversified increment operation;
and S25, performing selection, crossing and adaptive mutation operations.
4. The method of hierarchical fuzzy-artificial potential field path planning according to claim 3, wherein said method of calculating the fitness of an individual in step S21 comprises:
adopting a fitness function containing a B spline, wherein the fitness function is designed as follows:
wherein l, Δ are constants and Δ > 0;
are the control points of the basis functions in the B-spline,
is the nth waypoint in the ith iteration;
Lpis the path length:
lpis the closest distance between a certain waypoint and the obstacle,/pExpressed by euclidean distance as:
wherein the content of the first and second substances,
is a distance waypoint
The nearest obstacle position of;
respectively showing the abscissa and ordinate of the nth barrier;
wherein the content of the first and second substances,
a set of positive integers is represented that,
representing the set of path points in the ith iteration, pd,i(u)＝[x(u),y(u)]T,
Represents a real number set, an
Vector of nodes
Cutting the raw materials into pieces;
denotes the Nthw+ k +1 nodes, ΩθIs a non-decreasing node sequence; n is a radical ofi,k(u) is a B-spline basis function of order i k defined by the Boor-Cox recursive function, expressed as follows:
wherein the content of the first and second substances,
represents a sequence of nodes that is not decremented, and
5. the method of hierarchical fuzzy-artificial potential field path planning according to claim 3, wherein said method of performing elite reservation operation in step S23 comprises:
calculating the fitness of all individuals according to a path length formula, wherein the fitness is more than 0 and less than Pe:＝ne/Np(ii) an elite retention rate of < 1, sorting all individuals according to fitness and performing elite retention, wherein P iseRepresenting the elite retention, neIndicates the number of Elite individuals retained, NpRepresenting the total number of individuals.
6. The method of hierarchical fuzzy-artificial potential field path planning according to claim 3, wherein said method of performing diversified increment operations in step S24 comprises:
by adopting a formula of 0 < Pd:＝nd/Np(ii) a diversified increment ratio from feasible region of < 1 >
In random generation of nd·NwOrderly combination of candidate waypoints as ndAn individual, wherein PdDenotes the diversity increment ratio, ndIndicating increased individualsNumber of the cells.
7. The method of hierarchical fuzzy-artificial potential field path planning according to claim 3, wherein said method of performing selection, intersection, adaptive mutation operations in step S25 comprises:
s251, perform selection operation, i.e.
Individuals with high fitness values are reserved in a wheel probability selection mode, the individuals with high fitness values have higher selection probability to enter the next generation, and therefore the probability that the ith individual in the population is selected is as follows:
wherein (p)d,i) Calculating according to a formula of a fitness function;
s252, performing a crossover operation, i.e.
The process of gene recombination, i.e. generating new chromosome, of chromosome between different individuals is expressed by the following crossover operations:
aqj＝aqj(1-b)aljb
alj＝alj(1-b)akjb
wherein, aqj,aljB ∈ [0,1 ] representing the values of the j-th gene in the q-th and l-th chromosomes, respectively]The random number of (2);
s253, performing adaptive mutation operation, namely
And (3) adopting an adaptive mutation operation to determine the mutation probability of the individual through the adaptive value of the individual:
wherein, Pm,maxAnd Pm,minRespectively represent individualsMaximum and minimum mutation probabilities; (p)d,max) And (p)d,avg) The maximum and mean fitness values in the population are indicated separately.
8. The method of hierarchical fuzzy-artificial potential field path planning according to claim 1, wherein said fuzzy-artificial potential field algorithm with fuzzy decision executed in step S3 has a function expression as follows:
where ζ is a self-correcting blurring factor, ρ0The maximum radius of the action range of the repulsive force potential field of the obstacle, wherein the repulsive force correction coefficient is that the repulsive force of the obstacle to the unmanned ship is gradually reduced and the attractive force of the target point to the unmanned ship is gradually increased along with the action of two components of the repulsive force, so that lambda ∈ (0,1), krAnd kaIs the coefficient of thrust and gravity, ρλ(px,pE) The power of λ, ρ (p), representing the distance of the USV to the target pointb,pE)ρ(pb,pobs) The distances from the obstacle and the target point to the current position, respectively, are:
ρ(px,p*)＝||px,p*||2
in order to ensure that the unmanned ship can safely complete local collision avoidance and quickly return to a preset global course, the following three aspects are analyzed:
1) when the obstacle is not on the global path
When the unmanned ship is in the action range of the repulsive force field of the obstacle, the corrected repulsive force field is respectively opposite to rho (p)x,pobs) And ρλ(px,pE) Derivation to obtain two components of the corrected repulsive force
And
the expression form is as follows:
where ρ (p)x,pobs) Distance of obstacle to USV, ρ (p)x,pE) Is the distance of the target point to the USV, vector component
In the direction of the obstacle pointing to the unmanned ship, vector component
The direction of the unmanned ship is the motion direction of the unmanned ship and the safety area dsThe tangential direction of (a);
the gravitational component is the negative gradient of its potential field, expressed as follows:
Fa(pE)＝-▽[Ua(pE)]＝kaρ(pb,pE)
from this, the resultant force FhIs a repulsive force
And the gravitational force FaThe sum of (1):
2) when the obstacle is on the global path
By component of repulsive force
And component of repulsive force
By changing the resultant force F of the force applied by the potential fieldhThe USV can successfully bypass the barrier to complete local path planning, the distance between the USV and the barrier is gradually reduced, the self-correction fuzzy factor zeta is increased, the repulsive force of the barrier is increased, and the navigation safety of the USV is ensured.
3) Fast return to global path after collision avoidance
When unmanned ship safety range dsWhen no obstacle is detected in the unmanned ship, the dynamic collision avoidance is proved to be completed, at the moment, the potential field of the obstacle and a target point is closed, a course decision angle gamma is introduced, and the unmanned ship is enabled to quickly return to a preset global path by setting attraction points Q and K, specifically:
making a ray with an included angle gamma from the current position of the unmanned ship to a global path, intersecting the global path at a point Q, taking a point K on the global path at a distance eta in front of the Q, and introducing a gravitational potential field function at the point Q and the point K respectively, wherein the gravitational potential field function is defined as:
in the formula, kaProcessing the negative gradient of the gravitational field to obtain gravitational force F as gravitational force potential field gain factor psi ∈ { Q, K }t(Ψ) is:
Ft(Ψ)＝-▽[Ut(p)]＝kaρ(pb,Ψ)
since the attraction force is proportional to the distance between the USV and the reference point Ψ, it is possible to ensure that the USV can safely and smoothly return to the global path, since the distance between the USV and the point K is always greater than the point Q, and further, the direction of the attraction force is always biased toward the point K and directed toward the global path, so that the USV can pass through a certain point between the point Q and the point K, so that the return angle can be restricted to conform to the motion characteristics of the USV.
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