CN113867369B - Robot path planning method based on alternating current learning seagull algorithm - Google Patents
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Abstract
The invention provides a robot path planning method based on a gull algorithm of communication learning, which comprises the following steps: acquiring a robot moving area map; establishing an objective function for path planning of the moving area map according to the moving area map;based on the seagull algorithm, byPiecewiseMapping the initialized gull population position, and calculating an optimal fitness value and an optimal gull position according to a target function; introducing interchange learning, updating the gull position, and determining the updated optimal fitness value and optimal gull position; performing Cauchy variation on the optimal gull position to obtain the varied optimal fitness value and the optimal gull position; taking the gull position with the optimal fitness value before and after variation as an updated optimal gull position; and determining an optimal path planning result according to the optimal gull position which is updated in sequence according to the preset maximum iteration times. The method overcomes the defects of the existing gull algorithm, and can remarkably improve the path planning effect.
Description
Technical Field
The invention relates to the technical field of robotics, in particular to a robot path planning method based on a gull algorithm of alternating current learning.
Background
Path planning of a mobile robot is a key technology in mobile robot technology. The path planning technology is to make the mobile robot automatically find a collision-free track from a starting point to a target point according to the surrounding environment information. The path planning algorithm of the mobile robot is the core of the path planning of the mobile robot. The path planning of the mobile robot means that after sensing the surrounding environment, the mobile robot can self-plan an optimal moving path from a starting point to a terminal point, and the optimal path can meet the requirements of shortest moving path, shortest time consumption, minimum energy consumption and the like.
According to the current research, the gull algorithm is a novel intelligent optimization algorithm for simulating gull foraging behavior, and can be applied to the path planning problem. However, there are still some drawbacks to the gull optimization algorithm, such as: (1) when the position of the population is determined, the position of the individual gull is determined randomly, so that the algorithm has certain blindness and randomness; (2) the position updating of the gull algorithm is to move to the optimal position by adopting a spiral attack mode according to the position of a target object, but if the gull algorithm is moved only according to the optimal target position, the gull is easy to fall into a local optimal solution; (3) when the gull algorithm falls into the local optimal solution, no measures are provided to help the gull algorithm jump out of the local optimal solution.
Therefore, the invention provides a new robot path planning method based on the gull algorithm of the communication learning.
Disclosure of Invention
In order to solve the problems, the invention provides a robot path planning method based on a gull algorithm of alternating current learning, overcomes the defects of the existing gull algorithm, and can remarkably improve the path planning effect.
In order to achieve the above purpose, the present invention provides the following technical solutions.
A robot path planning method based on a gull algorithm of communication learning comprises the following steps:
acquiring a robot moving area map;
establishing an objective function for path planning of the moving area map according to the moving area map;
based on the seagull algorithm, byPiecewiseMapping the initialized gull population position, and calculating an optimal fitness value and an optimal gull position according to a target function;
introducing exchange learning, updating the gull position by carrying out exchange learning to the optimal gull and other gulls in the population, and determining the updated optimal fitness value and optimal gull position;
performing Cauchy variation on the optimal gull position to obtain the varied optimal fitness value and the optimal gull position; taking the gull position with the optimal fitness value before and after variation as an updated optimal gull position;
and determining an optimal path planning result according to the optimal gull position which is updated in sequence according to the preset maximum iteration times.
Preferably, the acquiring the robot moving area map includes the following steps: and modeling the environment of the mobile robot to obtain a mobile area map and performing grid processing on the map.
Preferably, the objective function is the shortest moving path, the shortest consumed time or the least consumed energy, and the corresponding constraint condition and the number of key nodes of the path are determined according to the objective function.
Preferably, said passing throughPiecewiseMapping and initializing a seagull population position, comprising the following steps:
determining the size of a defined populationPopsizeSea gull optimizing lower boundaryLBAnd seagull optimizing upper boundaryUB；
According toPiecewiseMapping to generate random numbersx t ：
In the formula:Pandxin the range of [0,1]；
According to generation ofPiecewiseRandom number initialized seagull positionP s (t)：
Preferably, the gull position updating through learning to the optimal gull and communication learning with other gulls in the population comprises the following steps: updating the seagull position through the seagull migration behavior and the seagull global attack behavior.
Preferably, the gull migration behavior comprises:
using additional variablesACalculate the new position of the gull to avoid collision with other gulls:
in the formula:C s (t) A new position which does not conflict with the positions of other seagulls;P s (t) Is the current position of the seagull;tthe current iteration number is;Athe motion behavior of the gull in a given search space;f c for controlling the coefficient, the value is reduced from 2 to 0;
moving towards the direction of the optimal position:
in the formula:M s (t) The direction of the optimal position;P gs (t) Is the best position;Bis the random number responsible for balancing the global and local searches;r d is [0,1]]A random number within a range;
arrival at the new location:
in the formula:D s (t) Is sea gullDistance moved to a new position.
Preferably, the gull global attack behavior comprises:
by changing the attack angle and speed continuously through the spiral motion, the spiral motion behavior is expressed as:
in the formula:rfor the radius of each of the spirals,θis [0, 2 π ]]Random angle values within a range;uandva correlation constant that is a helical shape;eis the base of the natural logarithm;
introducing interchange learning, and exchanging learning with other gulls in the population to update the position of the gull when the gull is learned to the optimal gull:
in the formula:r 1 andr 2 random learning weights, the sum of which is 1;P gs (t) Is the best position;P k (t) Other seagulls selected from the seagull population;
calculating a fitness value:
the optimal gull in the current iteration is recorded.
Preferably, the performing cauchy variation on the optimal gull position comprises:
in the formula: cauchy (0,1) is the standard Cauchy distribution.
Preferably, the determining, as the updated optimal gull position, a gull position with an optimal fitness value before and after mutation specifically includes:
and (3) judging the fitness values before and after mutation, namely:
determining an updated optimal gull position asP gs (t)。
The invention provides a robot path planning method based on a gull algorithm of alternating current learning, which has the following beneficial effects:
(1) by introduction ofPiecewiseThe gull population position is initialized by mapping, the uniformity and diversity of population position distribution can be improved, and the stability of the algorithm is enhanced.
(2) An interchange learning mechanism is introduced into the position updating mode of the gull, so that the search range of the algorithm is enlarged, and the adaptability of the algorithm is enhanced.
(3) The optimal gull is updated by utilizing the Cauchy variation, and the capability of jumping out of a local optimal solution in the later stage of the algorithm is realized.
Drawings
FIG. 1 is a flow chart of a method of an embodiment of the present invention;
FIG. 2 is a diagram illustrating a path planning result according to an embodiment of the present invention;
fig. 3 is a graph of an iterative process of an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
The invention discloses a robot path planning method based on a gull algorithm of communication learning, which specifically comprises the following steps as shown in fig. 1:
s1: and modeling the environment of the mobile robot to obtain a mobile area map and performing grid processing on the map.
S2: the objective function for establishing the path planning of the map of the moving area can be the shortest moving path, the shortest time consumption or the least energy consumption.
S3: according to the objective functionfuntionDetermining corresponding constraint conditions and key node number of pathD(ii) a Performing parameter setting, including: size of gull population (i.e. number of gull individuals)Popsize(ii) a Maximum number of iterations (i.e. conditions under which iterations stop)Miter(ii) a Seagull optimization lower boundaryLB(ii) a Seagull optimization upper boundaryUB。
S4: based on the seagull algorithm, byPiecewiseAnd mapping the initialized gull population position, and calculating the optimal fitness value and the optimal gull position according to the target function. The method specifically comprises the following steps:
determining the size of a populationPopsizeSea gull optimizing lower boundaryLBAnd seagull optimizing upper boundaryUB；
According toPiecewiseMapping generates random numbers:
in the formula:Pandxin the range of [0,1]；
According to generation ofPiecewiseRandom number initialized seagull positionP s (t)：
S5: introducing interchange learning, updating the gull position by performing interchange learning to the optimal gull and interchange learning with other gulls in the population, and determining the updated optimal fitness value and optimal gull position, specifically comprising:
s5.1: seagull migration behavior
During migration, the algorithm simulates how gull clusters move from one location to another. Three behaviors are mainly involved in this phase: avoid collision, move to the optimal position direction and approach the optimal position.
Using additional variablesACalculate the new position of the gull to avoid collision with other gulls:
in the formula:C s (t) A new position which does not conflict with the positions of other seagulls;P s (t) Is the current position of the seagull;tthe current iteration number is;Athe motion behavior of the gull in a given search space;f c for controlling the coefficient, the value is reduced from 2 to 0;
after avoiding coincidence with the positions of other gulls, the gull moves in the direction of the optimal position:
in the formula:M s (t) The direction of the optimal position;P gs (t) Is the best position;Bis the random number responsible for balancing the global and local searches;r d is [0,1]]A random number within a range;
when the seagull moves to a position where the seagull does not collide with other seagulls, the seagull moves towards the optimal position, and the new position is reached:
in the formula:D s (t) Is the distance that the gull moves to the new position.
S5.2: seagull global attack behavior
By changing the attack angle and speed continuously through the spiral motion, the spiral motion behavior is expressed as:
in the formula:rfor the radius of each of the spirals,θis [0, 2 π ]]Random angle values within a range;uandva correlation constant that is a helical shape;eis the base of the natural logarithm;
in the original gull, only the global optimal gull position is used for guiding to update the gull position, in order to more effectively improve the global search capability of the gull, the communication learning is introduced, and when the gull learns the optimal gull, the gull and other gulls in the population are subjected to communication learning to update the position of the gull:
in the formula:r 1 andr 2 the influence of the gull learning to the optimal gull and the gull learning in the population can be adjusted for randomly learning the weight, and the sum is 1;P gs (t) Is the best position;P k (t) Other seagulls selected from the seagull population;
calculating a fitness value:
the optimal gull in the current iteration is recorded.
S6: performing Cauchy variation on the optimal gull position to obtain the varied optimal fitness value and the optimal gull position; and the gull position with the optimal fitness value before and after the variation is used as the updated optimal gull position, which specifically comprises the following steps:
performing Cauchy variation on the optimal gull position, comprising:
in the formula: cauchy (0,1) is the standard Cauchy distribution.
Regarding the gull position with the optimal fitness value before and after variation as the updated optimal gull position, the method specifically comprises the following steps:
judging the fitness value before and after variation, namely greedy updating:
determining an updated optimal gull position asP gs (t)。
S7: and determining an optimal path planning result according to the optimal gull position which is updated in sequence according to the preset maximum iteration times.
In this embodiment:
and (3) establishing a 20 multiplied by 20 grid map of the mobile robot by taking MATLAB as a simulation platform, and analyzing the SOA method and the ISOA method by taking the shortest moving distance as a target. The parameters in the SOA algorithm are:Popsize=50，Maxiter=200，LB=1，UB= 20; the parameters in the ISOA algorithm are:Popsize=50，Maxiter=200，LB=1，UB= 20. The simulation environment and the movement paths obtained by the two methods are shown in fig. 2, and fig. 3 is an iterative process curve. Table 1 compares the data results of the two algorithms.
TABLE 1 Algorithm Path result comparison
| Algorithm | Path length |
| SOA | 36.7279 |
| ISOA | 32.14213 |
It can be intuitively found from fig. 2 that the moving path obtained by the SOA is longer than that of the ISOA, the path is roundabout, and the path obtained by the ISOA is reasonable. Further analyzing the results in fig. 2 and fig. 3, it can be seen that when the SOA algorithm is adopted, the algorithm convergence speed is relatively slow; when the ISOA algorithm is adopted, the convergence speed is higher, and a better path can be found faster. It can be seen that the ISOA algorithm designed by the method has higher convergence speed and convergence accuracy, and the SOA falls into the local optimum. Simulation results show that the ISOA algorithm has stronger searching capability under various identical environments, obtains a better moving path and verifies the effectiveness of the algorithm.
The present invention is not limited to the above preferred embodiments, and any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A robot path planning method based on a gull algorithm of communication learning is characterized by comprising the following steps:
acquiring a robot moving area map;
establishing an objective function for path planning of the moving area map according to the moving area map;
initializing a gull population position through Piecewise mapping based on a gull algorithm, and calculating an optimal fitness value and an optimal gull position according to a target function;
introducing exchange learning, updating the gull position by carrying out exchange learning to the optimal gull and other gulls in the population, and determining the updated optimal fitness value and optimal gull position;
performing Cauchy variation on the optimal gull position to obtain the varied optimal fitness value and the optimal gull position; taking the gull position with the optimal fitness value before and after variation as an updated optimal gull position;
determining an optimal path planning result according to the optimal gull position which is updated in sequence according to the preset maximum iteration times;
the gull position updating is carried out by learning to the optimal gull and by communicating with other gulls in the population, and the method comprises the following steps: updating the seagull position through seagull migration behavior and seagull global attack behavior;
the gull migration behavior comprises:
the new position of the gull is calculated using the additional variable a to avoid collision with other gulls:
Cs(t)＝A×Ps(t)
A＝fc-(t×fc/Miter)
in the formula: cs(t) is a new position that does not conflict with other seagulls' positions; ps(t) is the current position of the seagull; t is the current iteration number; a is the movement behavior of the seagull in a given search space; f. ofcFor controlling the coefficient, the value is reduced from 2 to 0; miter is the maximum number of iterations;
moving towards the direction of the optimal position:
Ms(t)＝B×(Pgs(t)-Ps(t))
B＝2×A2×rd
in the formula: ms(t) is the direction in which the optimal position is located; pgs(t) is the optimal position; b is a random number responsible for balancing global and local search; r isdIs [0,1]]A random number within a range;
arrival at the new location:
Ds(t)＝|Cs(t)+Ms(t)|
in the formula: ds(t) is the distance the gull has moved to the new position;
the gull global attack behavior comprises:
by changing the attack angle and speed continuously through the spiral motion, the spiral motion behavior is expressed as:
in the formula: r is the radius of each helix, θ is a random angle value in the range of [0, 2 π ]; u and v are the correlation constants of the helical shape; e is the base number of the natural logarithm;
introducing interchange learning, and exchanging learning with other gulls in the population to update the position of the gull when the gull is learned to the optimal gull:
in the formula: r is1And r2Random learning weights, the sum of which is 1; pgs(t) is the optimal position; pk(t) other gulls selected from the gull population;
calculating a fitness value:
fitness(t)＝Fitnessfunc(Ps(t))
the optimal gull in the current iteration is recorded.
2. The communication-learning-based gull algorithm-based robot path planning method of claim 1, wherein the obtaining of the robot movement area map comprises the following steps: and modeling the environment of the mobile robot to obtain a mobile area map and performing grid processing on the map.
3. The ac-learning-based gull algorithm-based robot path planning method of claim 1, wherein the objective function is the shortest moving path, the shortest consumed time or the least consumed energy, and the corresponding constraint condition and the number of key nodes of the path are determined according to the objective function.
4. The alternating current learning based gull algorithm robot path planning method of claim 1, wherein initializing gull population positions through the Piecewise mapping comprises:
determining the size Popsize of the population, a gull optimizing lower boundary LB and a gull optimizing upper boundary UB;
generating a random number x from a Piecewise mappingt：
In the formula: p and x range from [0,1 ];
initializing seagull position P according to generated Piecewise random numbers(t)：
Ps(t)＝(UB-LB)×xt-LB。
5. The ac-learning gull algorithm-based robot path planning method of claim 1, wherein the performing the cauchy variation on the optimal gull position comprises:
in the formula: cauchy (0,1) is the standard Cauchy distribution.
6. The ac-learning-based gull algorithm-based robot path planning method of claim 5, wherein the using the gull position with the optimal fitness value before and after the variation as the updated optimal gull position specifically comprises:
and (3) judging the fitness values before and after mutation, namely:
determining the updated optimal gull position as Pgs(t)。
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