CN-122015836-A - Mobile robot path planning method, system and storage medium based on marine predator algorithm

CN122015836ACN 122015836 ACN122015836 ACN 122015836ACN-122015836-A

Abstract

The invention provides a mobile robot path planning method, a system and a storage medium based on a marine predator algorithm, which relate to the technical field of mobile robot path planning and specifically comprise the following steps: S1, acquiring motion state parameters and information parameters of N obstacles with the same size on a robot moving path, wherein the motion state parameters of the obstacles comprise a moving state and a static state, and the information parameters of the obstacles comprise real-time positions of the obstacles, real-time distances of the obstacles and the robot and moving speeds. The method is divided into a static group and a moving group through a clustering algorithm, the positions of the obstacles in the static group and the distances between the obstacles and the robot are analyzed, the shape characteristics of the obstacles are obtained, the shapes of the moving obstacles are obtained through static map matching, feature extraction and template matching, and an optimal path solution is obtained according to an objective function, so that the moving path of the robot is comprehensively optimized, and the path planning precision is improved.

Inventors

WANG QIANG
HUANG YINGHUI
DENG YUNSHENG
CAO JIANLEI
GUO CHENG

Assignees

蚌埠学院

Dates

Publication Date: 20260512
Application Date: 20240327

Claims (10)

1. A mobile robot path planning method based on a marine predator algorithm is characterized by comprising the following specific steps: S1, acquiring motion state parameters and information parameters of N obstacles with the same size on a robot moving path, wherein the motion state parameters of the obstacles comprise a moving state and a static state, and the information parameters of the obstacles comprise real-time positions of the obstacles, real-time distances of the obstacles and the robot and moving speeds of the obstacles; s2, acquiring the position of the obstacle, the distance between the obstacle and the robot when the obstacle is stationary, dividing the obstacle into static groups through a clustering algorithm, acquiring the position information of the obstacle when the obstacle moves, the distance between the obstacle and the robot, continuously updating the distance value, and dividing the obstacle into moving groups through the clustering algorithm; s3, analyzing the positions of the obstacles in the static group and the distances between the obstacles and the robot, acquiring the shape characteristics of the static obstacles, acquiring the shape characteristics of the moving obstacles by utilizing the acquired shape characteristics of the static obstacles through static map matching, feature extraction and template matching, and analyzing the real-time positions of the obstacles in the moving group, the real-time distances between the obstacles and the robot and the moving speeds of the obstacles, and acquiring the movement track of the moving obstacles; s4, searching an optimal solution by adopting a marine predator algorithm, introducing the original path length, modifying the original path length according to the motion trail of the mobile group, calculating an objective function of the modified path, and obtaining the optimal path solution according to the objective function.
2. The mobile robot path planning method based on the marine predator algorithm according to claim 1, wherein in step S2, the positions of the obstacles, the distances between the obstacles and the robot are collected when the obstacles are stationary, and the process of classifying the obstacles into static groups by the clustering algorithm is as follows: Acquiring data, namely acquiring position information of an obstacle and the distance between the obstacle and a robot through a sensor, wherein the position information of the obstacle can be acquired through GPS positioning, and the distance between the obstacle and the robot can be acquired through a distance sensor; extracting features from the acquired data, wherein the features comprise position coordinates of the obstacle and the distance between the obstacle and the robot; And a clustering algorithm, namely selecting a clustering algorithm K-means to group the static obstacles according to the similarity or the distance between the samples.
3. The mobile robot path planning method based on the marine predator algorithm according to claim 1, wherein in step S2, the position information of the obstacle, the distance between the obstacle and the robot are acquired in real time when the obstacle moves, and the process of dividing the obstacle into moving groups by the clustering algorithm is as follows: The data acquisition, namely acquiring the real-time position and the moving speed of the obstacle and the real-time distance between the obstacle and the robot in real time through a sensor, wherein the real-time position of the obstacle can be acquired through GPS positioning, the moving speed can be acquired through a speed sensor, and the real-time distance between the obstacle and the robot can be acquired through a distance sensor; extracting features from the acquired data, wherein the features comprise real-time position coordinates of the obstacle, the moving speed of the robot and the real-time distance between the obstacle and the robot; and a clustering process, namely selecting a clustering algorithm K-means to group the moving obstacles according to the similarity or distance between samples.
4. The mobile robot path planning method based on the marine predator algorithm according to claim 1, wherein in step S3, the process of obtaining the shape characteristics of the obstacle is as follows, for the position of the obstacle in the static group, the distance analysis of the obstacle and the robot: Acquiring position coordinates of obstacles in a static group and distance information between the obstacles and a robot from the static group; Boundary extraction, namely acquiring the boundary of an obstacle by adopting a Sobel operator according to the position coordinates of the obstacle; and calculating the shape characteristics, namely obtaining the shape characteristics of the obstacle by using a shape characteristic algorithm according to the obtained boundary of the obstacle.
5. The mobile robot path planning method based on marine predator algorithm according to claim 4, wherein the process of obtaining the boundary of the obstacle using Sobel operator is as follows: Image graying, namely carrying out graying treatment on an image corresponding to the position coordinates of the obstacle, and converting a color image into a gray image according to the following formula: Y=0.298*R+0.587*G+0.114*B Wherein Y is the gray value of the position coordinate of the obstacle, R, G, B is the pixel red component value, the green component value and the blue component value of the image corresponding to the position coordinate of the obstacle; And performing Sobel operator operation, namely performing edge detection on the gray level image by using the Sobel operator, wherein the Sobel operator comprises a horizontal filter and a vertical filter which are respectively used for detecting the edges of the image in the horizontal direction and the vertical direction, and the following formula can be adopted for horizontal edge detection: For vertical edge detection, the following formula may be used: The above Sobel operator formula is used to perform convolution operation on the gray image, and gradient values of the pixel points are calculated in the horizontal direction and the vertical direction respectively, wherein the gradient values in the horizontal direction are obtained by adopting the following formula: The following formula can be adopted to obtain the gradient value in the horizontal direction: Wherein G X (i,j)、G Y (i, j) is the horizontal gradient value and the vertical gradient value of the ith row and the jth column of pixel points respectively, X (i, j) is the gray value of the ith row and the jth column of pixel points, Edge intensity calculation, namely generating gradient amplitude values of pixel points according to the following formula: wherein T X (i, j) is the gradient amplitude of the pixel point in the ith row and the jth column; Edge thresholding, namely thresholding the edge intensity image according to a set threshold value, marking pixel points with the edge intensity exceeding the threshold value as edge points, marking other pixel points as non-edge points, and using the marked edge points and the non-edge points to define the boundary of the obstacle so as to simulate the shape characteristics of the static obstacle.
6. The mobile robot path planning method based on the marine predator algorithm according to claim 1, wherein in step S3, after the shape of the static obstacle is acquired, the process of acquiring the shape of the mobile obstacle is as follows: Static map matching, namely matching the map data of the static obstacle with the current sensor data, wherein the static map already contains the shape information of the static obstacle, and comparing the current sensor data with the map to identify the moving obstacle matched with the map so as to acquire the shape information of the moving obstacle; Extracting edge and corner features from map data of static barriers, searching similar feature points in current sensor data, and determining the position and shape of the moving barriers through feature matching; template matching, namely taking the shape of the static obstacle as a template, then carrying out template matching in the current sensor data, finding out an area matched with the template, namely identifying the static obstacle as a moving obstacle, and acquiring the shape of the static obstacle.
7. The method for planning a path of a mobile robot based on a marine predator algorithm according to claim 1, wherein in step S3, the real-time position of the obstacle in the moving group, the real-time distance between the obstacle and the robot, and the moving speed are analyzed, and the process of obtaining the movement track of the obstacle is as follows: real-time position analysis: The real-time position of the obstacle can be obtained according to GPS positioning data, and the change of the position of the obstacle along with time can be obtained by recording the position information of the obstacle at different time points; real-time distance analysis: the real-time distance between the obstacle and the robot may be calculated using the euclidean distance formula: Wherein S is the real-time distance between the obstacle and the robot, x z is the x coordinate of the obstacle, y z is the y coordinate of the obstacle, the position of the obstacle can be positioned through x z and y z , x j is the x coordinate of the robot, y j is the y coordinate of the robot, and the position of the robot can be positioned through x j and y j ; And (3) moving speed analysis: the moving speed of the obstacle can be obtained by calculating the position change amount of the obstacle between different time points, and calculating the average speed according to the following formula: Wherein, the For average speed, W i is the distance difference between the current position and the last position of the obstacle, t i is the time interval from the last position to the current position of the obstacle, In summary, the motion trajectory graph can be drawn by moving the real-time position of the obstacle, the real-time distance between the obstacle and the robot, and the moving speed.
8. The mobile robot path planning method based on the marine predator algorithm according to claim 1, wherein in step S4, an optimal solution is searched for by using the marine predator algorithm, an original path length is introduced, the original path length is modified according to a motion trail of a mobile group, an objective function of the modified path is calculated, and the optimal path solution is obtained according to the objective function as follows: the path between the starting point and the target point is smoothed using cubic spline interpolation, and if the number of interpolation points of the entire planned path is M, the original path length L is calculated as follows: wherein pp i (pp ix ,pp iy ) is the i-th interpolation point; if all intermediate points are not located in the obstacle, the result may be accepted, but if all intermediate points are not located in the obstacle, and some interpolation points are located in the obstacle, the result must be modified; Assuming that the area formed by each individual obstacle is a defined circle, the defined circle of the obstacle is shown, the center of the i-th defined circle is defined as ob i (ob ix ,ob iy ), the radius of the i-th defined circle is ob ir , and if the interpolation point pp i is located in the obstacle j, the following equation is used to calculate the violation behavior: d ij is the distance between the interpolation point i and the center of the bounding circle j, v ij represents a violation if the interpolation point i is located in the obstacle j, and v is a complete violation. According to the motion track information of the mobile group, a certain path length is increased or reduced on each node of the original path, the increment can be adjusted according to the motion direction, speed and distance of the mobile group, if the motion direction of the group is the same as the original path direction, the path length can be reduced, and if the motion direction is opposite, the path length can be increased; Thus, the calculation formula for the modified path length L m is as follows: L m =L(1+ρv) Wherein L m is the corrected path length, ρ is an adjustment factor; thus, the objective function L f optimized for MPA is defined as follows: Wherein when all interpolation points are not in the obstacle, v ij =0, v=0, then L f =l, when part of the interpolation points are in the obstacle, L f ＝L m , when some intermediate points are in the obstacle, L f = infinity; thus, the optimal solution obtained by the objective function is when L f =l.
9. A mobile robot path planning system based on a marine predator algorithm, characterized in that the system is adapted to perform the mobile robot path planning method based on a marine predator algorithm according to any one of claims 1-8, comprising: The data acquisition module is used for acquiring motion state parameters and information parameters of N obstacles with the same size on a robot moving path, wherein the motion state parameters of the obstacles comprise a moving state and a static state, and the information parameters of the obstacles comprise real-time positions of the obstacles, real-time distances of the obstacles and the robot and moving speed of the obstacles; the group classification module is used for acquiring the position of the obstacle and the distance between the obstacle and the robot when the obstacle is stationary, classifying the obstacle into a static group through a clustering algorithm, acquiring the position information of the obstacle and the distance between the obstacle and the robot when the obstacle moves, continuously updating the distance value, and classifying the obstacle into a moving group through the clustering algorithm; The data analysis module is used for analyzing the positions of the obstacles in the static group and the distances between the obstacles and the robot, acquiring the shape characteristics of the static obstacles, acquiring the shape characteristics of the moving obstacles through static map matching, feature extraction and template matching by utilizing the acquired shape characteristics of the static obstacles, and analyzing the real-time positions of the obstacles in the moving group, the real-time distances between the obstacles and the robot and the moving speeds of the obstacles to acquire the movement track of the moving obstacles; the path planning module is used for searching an optimal solution by adopting a marine predator algorithm, introducing the original path length, modifying the original path length according to the motion trail of the mobile group, calculating an objective function of the modified path, and obtaining the optimal path solution according to the objective function.
10. A storage medium having stored therein a computer program which when executed by a processor implements the mobile robot path planning method based on marine predator algorithm of any one of claims 1-8.

Description

Mobile robot path planning method, system and storage medium based on marine predator algorithm Technical Field The invention relates to the technical field of mobile robot path planning, in particular to a mobile robot path planning method, a mobile robot path planning system and a mobile robot path planning storage medium based on a marine predator algorithm. Background Robot path planning is a field of research in robotics that deals with the robot finding the best path to navigate from a starting point to a target while avoiding obstacles and optimizing certain criteria such as time, energy consumption or safety. Robot path planning plays a key role in autonomous robots operating in complex and dynamic environments, the ability to automatically navigate being essential for the robot to perform tasks such as assembly, inspection and transportation, the path planning of the robot enabling the robot to avoid obstacles, navigate in complex structures, and optimize its movements to save energy and time. The main goal of robot path planning is to design an optimal path for the robot in order to navigate from the origin to the target while avoiding obstacles, optimizing certain criteria, the path planning algorithm needs to take into account various factors such as the kinematics and dynamics of the robot, the geometry and topology of the environment, the presence of static and dynamic obstacles and the sensors and actuators of the robot. A mobile robot path planning control method with a publication number of CN112987740B in the prior art comprises a data processing step, a judging step, a planning path forming step, a planning step and a planning step, wherein the data processing step is used for expanding an obstacle in a field into a circle with the obstacle as a center by using the radius of the robot, the judging step is used for connecting a starting point with a target point to form a straight line and judging whether the straight line has an intersection point with the circle, an auxiliary line section parallel to the straight line is constructed on the circle, the planning path after the smoothing processing is used for carrying out smoothing processing on the formed preliminary planning path, and the planning path after the smoothing processing is used as an optimal path from the starting point to the target point. The invention provides a new mobile robot path planning control method, which effectively processes circular obstacles, and as only the obstacles touchable under the current planning path are researched and processed, other irrelevant obstacles are abandoned, and the calculation time of an algorithm is improved. However, as is clear from the above description, only the path optimization problem of the mobile robot of the static obstacle is analyzed, and the path optimization problem of the mobile robot for the moving obstacle is not analyzed, so that the path optimization is incomplete and the accuracy of the optimization is required to be further optimized. The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art. Disclosure of Invention The invention aims to provide a mobile robot path planning method, a mobile robot path planning system and a storage medium based on a marine predator algorithm, so as to solve the problems in the background technology. In order to achieve the above purpose, the present invention provides the following technical solutions: a mobile robot path planning method based on ocean predator algorithm comprises the following specific steps: S1, acquiring motion state parameters and information parameters of N obstacles with the same size on a robot moving path, wherein the motion state parameters of the obstacles comprise a moving state and a static state, and the information parameters of the obstacles comprise real-time positions of the obstacles, real-time distances of the obstacles and the robot and moving speeds of the obstacles; s2, acquiring the position of the obstacle, the distance between the obstacle and the robot when the obstacle is stationary, dividing the obstacle into static groups through a clustering algorithm, acquiring the position information of the obstacle when the obstacle moves, the distance between the obstacle and the robot, continuously updating the distance value, and dividing the obstacle into moving groups through the clustering algorithm; s3, analyzing the positions of the obstacles in the static group and the distances between the obstacles and the robot, acquiring the shape characteristics of the static obstacles, acquiring the shape characteristics of the moving obstacles by utilizing the acquired shape characteristics of the static obstacles through static map matching, feature extraction and template matching, and an