CN-116652947-B - Mobile robot global path planning method based on improved mayhead algorithm

CN116652947BCN 116652947 BCN116652947 BCN 116652947BCN-116652947-B

Abstract

The invention discloses a mobile robot global path planning method based on an improved dayf algorithm, which comprises the following steps of modeling a map environment; initializing the population parameters, randomly generating the positions of the population of the dayf, setting the initial speed, generating the initial path, calculating the fitness value, updating the fitness value of the dayf, sorting, generating offspring, performing mutation operation on the offspring, dividing the offspring into male and female, updating the optimal global and related parameters, updating the weight coefficient, and judging whether the optimal iteration times are reached. The invention introduces the ion motion cross search rule and the self-adaptive inertia weight to improve the convergence efficiency and the global search capability of the algorithm, and simultaneously introduces the F distribution random variation to increase the diversity of the search result of the algorithm, so that the convergence process is more rapid and stable.

Inventors

LI PENG
WANG ZIXU
CHEN JIEYONG
PAN HONGBIN
LI XIMIN
DENG YUANMING
LI YANLONG
CHEN LIJIU
DENG GANLIN

Assignees

湘潭大学

Dates

Publication Date: 20260508
Application Date: 20230531

Claims (7)

1. A mobile robot global path planning method based on an improved mayday algorithm, comprising the steps of: step 1, obtaining map information and modeling a map environment; Initializing a dayfish population parameter, randomly generating a position x of the dayfish population, setting an initial speed v to be zero, generating an initial path, and calculating an fitness value; Step 3, updating the speed and position of male and female dayfish; Step 4, calculating an adaptation value of the updated mayday, and sequencing the positions of the mayday from small to large according to the adaptation value; step 5, generating filial generation and carrying out mutation operation on the filial generation; the method comprises the steps of introducing an ion movement cross search equation in the mating stage of a dayfish algorithm, introducing an attraction factor AF and BF self-adaptive adjustment to increase the search step length, and assuming that a male dayfish is a cation, a female dayfish is an anion, the male dayfish and the female dayfish are similar to each other by attraction force, and the female dayfish is close to each other and two sub-generations generated after mating And The expression is: ; ; ; wherein, malee and female respectively represent male parent and female parent, Representing the optimal individual of male moths, Representing the optimal individual of female dayfish, , ; Dividing the offspring dayfish into male and female, calculating the fitness value of the offspring dayfish, and updating the individual optimal, global optimal and related parameters; step 7, updating the dance coefficient and the random flight coefficient, and updating the weight coefficient; And 8, judging whether the maximum iteration times are reached, if so, outputting an optimal solution, otherwise, returning to the step 3.
2. The global path planning method of mobile robot based on improved dayff algorithm of claim 1, wherein in step 1, in order to facilitate the establishment of a geometric model of the path planning of the robot, the following assumptions are made: (1) The robot motion is assumed to be in a two-dimensional space, i.e. the problem of height of the robot and the obstacle does not need to be considered in the environment; (2) Assuming that there are no dynamic obstacles in the environment and that the mobile robot has knowledge of all static obstacles in the environment; (3) Assuming that the mobile robot is treated as a particle, its size and shape need not be considered; The method is characterized in that an environment model is directly built by adopting a plane rectangular coordinate system method, and in an actual working environment, the shapes of the obstacles are diversified, so that a method for representing the obstacles is simplified, various irregularly-shaped obstacles in the environment are processed in a swelling mode, the obstacles in various shapes are represented by a minimum circle capable of containing the obstacles, the size of the obstacles is represented by a circular area, the processing mode is called swelling processing, and the method is used for processing the obstacles, so that the geometric model is more convenient to build; In an actual path planning task, in order to avoid the situation that the robot is too close to an obstacle to threaten the safety of the robot, the range of expansion of the obstacle is required to be enlarged when modeling the obstacle, the enlarged radius is R, and R is the safety distance of the robot close to the obstacle; the robot path planning requires that in the environment of a known obstacle, an optimal path for avoiding the obstacle is searched from a starting point S to a target point G, a two-dimensional coordinate system x-o-y is established by considering the length of the path, the safety of the path and the smoothness of the path in the process of searching the path, the black solid filled object is used for representing the obstacle, the solid small round dot is used for representing the starting point S of the robot path planning at the lower left corner of the two-dimensional coordinate system, and the coordinates are expressed as The solid small dots are used for representing a target point G of the robot path planning at the right upper corner of the two-dimensional coordinate system, and the coordinates are expressed as The task of the robot path planning is to find a collision-free path from the starting point S to the target point G, which is represented by a set , The node is represented as an mth node, and is represented as coordinates in a rectangular coordinate system The connecting lines of the adjacent points do not pass through the obstacle in the environment, and the connecting lines of the line segments are optimized paths.
3. The method of global path planning for mobile robot based on improved F-algorithm of claim 1, wherein in said step 2, the value of fitness function is regarded as the length of path, F-is continuously changed in the course of optimizing, part of path can pass through obstacle and become illegal path, in order to maintain the diversity of path, the fitness function with penalty function is adopted, the path is automatically eliminated in the course of optimizing by adding penalty value, m nodes are set on one path, each node coordinate is The coordinates of the start point and the end point of the path are respectively And Setting the fitness function as follows: ; In the above formula, μ is a penalty value, L is a robot motion path length, i.e., a sum of distances between each node from the start point to the target point, calculated as: ; v is the illegality of the path, and is used for judging whether the path node is located in the obstacle area, namely whether the path passes through the obstacle, the initial value of V is 0, and the mode of calculating the V value is as follows: ; ; ; Wherein, the Representing the coordinates of the i-th node on the path, Representing the coordinates of the center of the kth circular obstacle, A distance value representing the kth obstacle to the ith node on the path, A distance value representing a kth obstacle to a node on the path; for the radius of the kth circular obstacle, NUM is the number of obstacles, ave () represents taking the average, Used for judging whether the path passes through the obstacle, if so, the point falling on the obstacle area corresponds to The element value in (a) is greater than 0, when V >0, otherwise the array The fitness value is equal to the path length when the elements in (a) are all 0, i.e. v=0.
4. The method for planning a global path of a mobile robot based on the improved mayday algorithm according to claim 1, wherein in the step 3, the speed update formula of the male mayday is: ; Wherein, the Representing the speed of the ith male in dimension j for the t +1 iteration, Representing the position of the ith male, t iterations in dimension j, f (x) is the objective function, And Representing the positive attraction coefficient, beta being the visibility coefficient, For dance coefficients, r is a random number ranging between [ -1,1], For the i-th, best position that has been reached in the j-th dimension, gbest is the globally optimal position, And Respectively representing the position of the i-th male And Between and between And gbest, g is the inertial weight; ; Wherein, the And Respectively minimum and maximum weight coefficients, wherein T is the current iteration number, and T is the maximum iteration number; the female dayfish's velocity update formula is: ; wherein, the And The position and velocity of the i-th female mayhead in the j-dimension after t iterations are expressed, Distance between male and female dayfish, Is a random flight coefficient.
5. The global path planning method of mobile robot based on improved mayday algorithm according to claim 1, wherein in the step 3, an adaptive inertial weight coefficient is introduced into the search equation of the mayday algorithm, and the position update expressions of male and female mayday are ; ; Wherein, the Represents the position of the i-th male-manifold at the t +1 iteration, Then the i-th male is represented by the speed at the t +1 iteration, Represents the position of the ith female, at the t+1st iteration, ω is the weight coefficient.
6. The global path planning method of mobile robot based on improved dayfish algorithm of claim 4, wherein in step 5, a random mutation strategy of F distribution is introduced, and a group of mutation operations are randomly selected from the offspring dayfish: ; Wherein, the Representing the position of the variant progeny, F (3, 4) representing the F distribution subject to the degree of freedom (3, 4).
7. The method for planning a global path of a mobile robot based on an improved dayf algorithm according to claim 4, wherein in the step 7, the dance coefficient and the random flight coefficient are also reduced with the number of iterations, and the formula is as follows: ; ; Wherein, the 、 Respectively a dance coefficient and a random flight coefficient at the time t, 、 Dance coefficients and random flight coefficients at the initial time, 、 Respectively the damping parameter of the dance coefficient at the time t and the damping parameter of the random flight coefficient; The weight coefficient updating formula is as follows: ; wherein, the And The minimum and maximum weight coefficients are respectively, and T is the maximum iteration number.

Description

Mobile robot global path planning method based on improved mayhead algorithm Technical Field The invention relates to the field of robot path planning, in particular to a mobile robot global path planning method based on an improved dayf algorithm. Background Autonomous mobile robots are programmable multi-task mechanical devices capable of freely moving in environments containing obstacles, performing various functions and acquiring environmental information through sensors, where path planning is one of the most basic problems that mobile robots must solve before completing a task. The path planning technique is a behavior criterion formulated for the robot to move in a canonical space, following specific performance indicators and algorithms, with the aim of searching for a feasible expected motion path for the robot from the current position to a target point in space. Early researchers proposed many algorithms to plan paths for robots, but these methods all had some drawbacks such as being too complex, not suitable for complex environments, etc. In order to overcome these disadvantages, meta-heuristic methods have attracted a great deal of researchers in recent years to study, such as ant colony algorithms, particle swarm optimization algorithms, bacterial foraging optimization algorithms, and the like. Konstantions Zervoudakis in 2020, a new type of swarm intelligence algorithm, the f iota algorithm (Mayfly Algorithm, MA), was proposed that combines the advantages of the particle swarm algorithm, the firefly algorithm, and the genetic algorithm. The mayday algorithm has a faster convergence speed and convergence accuracy than most biological heuristic optimization algorithms. However, the global search capability of the mayday algorithm is weak, and there are also problems of low diversity and stagnation of the local optimum, which also results in difficulty in achieving the desired accuracy and algorithm stability at the later stage of the algorithm. Disclosure of Invention In order to solve the technical problems, the invention provides a mobile robot global path planning method based on an improved f-algorithm, which has the advantages of simple algorithm, good searching capability and rapid and stable convergence. The technical scheme for solving the problems is that the mobile robot global path planning method based on the improved dayf algorithm comprises the following steps: step 1, obtaining map information and modeling a map environment; Initializing a dayfish population parameter, randomly generating a position x of the dayfish population, setting an initial speed v to be zero, generating an initial path, and calculating an fitness value; Step 3, updating the speed and position of male and female dayfish; Step 4, calculating an adaptation value of the updated mayday, and sequencing the positions of the mayday from small to large according to the adaptation value; step 5, generating filial generation and carrying out mutation operation on the filial generation; dividing the offspring dayfish into male and female, calculating the fitness value of the offspring dayfish, and updating the individual optimal, global optimal and related parameters; step 7, updating the dance coefficient and the random flight coefficient, and updating the weight coefficient; And 8, judging whether the maximum iteration times are reached, if so, outputting an optimal solution, otherwise, returning to the step 3. In the above method for planning a global path of a mobile robot based on an improved dayfish algorithm, in step 1, in order to facilitate the establishment of a geometric model for path planning of the robot, the following assumptions are made: (1) The robot motion is assumed to be in a two-dimensional space, i.e. the problem of height of the robot and the obstacle does not need to be considered in the environment; (2) Assuming that there are no dynamic obstacles in the environment and that the mobile robot has knowledge of all static obstacles in the environment; (3) Assuming that the mobile robot is treated as a particle, its size and shape need not be considered; The method is characterized in that an environment model is directly built by adopting a plane rectangular coordinate system method, and in an actual working environment, the shapes of the obstacles are diversified, so that a method for representing the obstacles is simplified, various irregularly-shaped obstacles in the environment are processed in a swelling mode, the obstacles in various shapes are represented by a minimum circle capable of containing the obstacles, the size of the obstacles is represented by a circular area, the processing mode is called swelling processing, and the method is used for processing the obstacles, so that the geometric model is more convenient to build; In an actual path planning task, in order to avoid the situation that the robot is too close to an obstacle to threaten the safety of the robot, the range of expa