CN-122015861-A - Robot path planning method, system, equipment and medium

CN122015861ACN 122015861 ACN122015861 ACN 122015861ACN-122015861-A

Abstract

The invention discloses a robot path planning method, a system, equipment and a medium, which relate to the technical field of robot path planning and comprise the steps of modeling a robot path planning environment, constructing an objective function, initializing the population scale of badgers, generating a candidate path for each badger individual, acquiring the fitness of each candidate path, automatically selecting an excavation mode and a honey acquisition stage according to the environment parameters, carrying out disturbance and elite tangent search on the badger individual, updating the current position and fitness value of the badger individual, setting the maximum iteration times, carrying out an iteration updating process, reserving the optimal badger individual, and updating the positions and fitness of other badgers individual until the optimal path is found. The improved badger algorithm is used for robot path planning, so that the robot can realize safe obstacle avoidance under a dynamic environment.

Inventors

WANG GUOGANG
SUN HONGWEI
ZHANG TIANYU
CHEN XU

Assignees

吉林化工大学

Dates

Publication Date: 20260512
Application Date: 20260211

Claims (9)

1. A robot path planning method, comprising: modeling an environment of robot path planning to obtain an environment model with an obstacle area and a free area; In an environment model, taking the minimum total length of a path, the minimum turning times and the minimum planning time as optimization targets when the robot path is planned, constructing a badger optimization algorithm to find an objective function of the path, and taking obstacle avoidance and the maximum corner as constraint conditions; Generating a candidate path for each badger individual based on the objective function and the constraint condition, acquiring the fitness of each candidate path, selecting an excavation mode and a honey collection stage according to environmental parameters, performing disturbance and elite tangent search on the badger individual, and updating the current position and fitness value of the badger individual, wherein the environmental parameters represent the density of obstacles, and the current position of the badger individual represents one path point of the robot; And iteratively executing the updating process of the current position and the fitness value of the badger individual, reserving the optimal badger individual, and connecting candidate paths corresponding to each generation of optimal badger individual to serve as the optimal path of the robot planning.
2. The method for planning a path of a robot according to claim 1, wherein the obtaining the fitness of each candidate path comprises: obtaining density factors of populations in badger optimization algorithm ; Initializing a population according to the Tent chaotic map, judging whether the category number of the initial population is higher than a threshold value, increasing the global searching range through a density factor when the category number of the initial population is higher than the threshold value, setting the initial value of the density factor to be a preset value higher than the initial value when the category number of the initial population is lower than the threshold value, searching again, generating the adaptability of each candidate path and sequencing.
3. The method for planning a path of a robot according to claim 2, wherein the generating the fitness of each candidate path comprises: through the current meles Generating fitness of each candidate path by the sum of the penalty term function of the path collision obstacle and the penalty function of the path smoothness The specific expression is: ; Wherein, the As a penalty term function of the path collision obstacle, As a penalty function of the path smoothness, And (3) with Is a weight coefficient.
4. The robot path planning method according to claim 1, wherein the selecting the mining mode and the honey mining stage according to the environmental parameters comprises: When the excavation mode is selected, the specific expression is as follows: ; Wherein, the Is a new position of the honey badger, As a parameter of the random direction of the light, For the food to acquire the capacity coefficient, Respectively random numbers between [0,1], For the distance between the prey and the i-th badger, Is the density factor of the population, I is the odor intensity coefficient, For the current global optimum position, Is the relevant position when digging; when the honey collecting stage is selected, the honey badgers are positioned close to the honeycomb along with the straight line of the honey collecting birds, and the specific expression is as follows: ; Wherein, the Is the current position of the badger with honey, Is a random number between 0, 1.
5. The method for planning a path of a robot according to claim 1, wherein the disturbance to the individual meles is specifically: the method comprises the steps of disturbing an individual badger by adopting a Cauchy variation strategy, and updating the current position of the individual badger, wherein the specific expression is as follows: ; wherein f (x) is the current position of the updated badger individual, and x is the current position vector of the badger individual.
6. The robot path planning method according to claim 1, wherein the elite tangent search is performed on the meles, specifically: adopting elite retention mechanism to screen out the badger individual with highest adaptability in the population to form elite pool, the specific expression is: ; Wherein, the Represents a threshold value and, The optimal fitness of the mth generation and the average fitness of the population are respectively, Is elite individual; Aiming at each elite individual in the elite pool, constructing a non-uniform step length adjusting mechanism by adopting a hyperbolic tangent function, and carrying out local search when the elite individual approaches to a global optimal solution, wherein the specific expression is as follows: ; Wherein, the In order for the convergence factor to be a factor, As a coefficient of sensitivity, a reference number, For the distance between the current elite individual and the global optimal solution, ∇ x is the local search result, x b 、x e is the position vector of the global optimal individual and the position vector of the elite individual in the elite pool, The optimal fitness and the population average fitness are respectively.
7. A robot path planning system, comprising: The model construction module is used for modeling an environment of robot path planning to obtain an environment model with an obstacle area and a free area; the objective function module is used for constructing an objective function of a badger optimization algorithm searching path in an environment model by taking the minimum total length of the path, the minimum turning times and the minimum planning time as optimization targets when the robot path is planned, and taking obstacle avoidance and the maximum corner as constraint conditions; the position updating module is used for generating a candidate path for each badger individual based on the objective function and the constraint condition, acquiring the fitness of each candidate path, automatically selecting an excavation mode and a honey collection stage according to environmental parameters, carrying out disturbance and elite tangent search on the badger individual, and updating the current position and fitness value of the badger individual, wherein the environmental parameters represent the density of obstacles, and the current position of the badger individual represents one path point of the robot; the path acquisition module is used for iteratively executing the updating process of the current position and the fitness value of the badger individual, reserving the optimal badger individual, and connecting candidate paths corresponding to each generation of optimal badger individual to serve as the optimal path planned by the robot.
8. A computer device comprising a memory and a processor, the memory having stored therein a program which, when executed by the processor, causes the processor to perform the steps of a robot path planning method according to any one of claims 1 to 6.
9. A storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of a robot path planning method according to any of claims 1 to 6.

Description

Robot path planning method, system, equipment and medium Technical Field The present invention relates to the field of robot path planning technologies, and in particular, to a method, a system, an apparatus, and a medium for robot path planning. Background Along with the continuous development of industrialization and intellectualization, the mobile robot is applied to various fields, particularly the field of logistics robots, has remarkable development, and the efficiency of picking per hour of the logistics robots independently researched and developed by the Jingdong logistics company in China is 400-500, wherein path planning is a key technology of the mobile robot, and the purpose of the path planning is to plan a safe and collision-free path from a starting point to a terminal point of the robot under a known or unknown environment, so that the technology is not separated from the support of an autonomous navigation algorithm. The path planning algorithm is divided into a global path planning algorithm and a local path planning algorithm, the global path planning algorithm comprises an A-type algorithm, an RRT algorithm, a particle swarm algorithm, an ant colony algorithm and the like, the robot technology is continuously developed, the traditional algorithm can not meet the requirements of modern production and manufacturing, the robot planning efficiency is low and the calculation complexity is high in the face of large-scale obstacles in a complex environment, and in recent years, a plurality of optimization algorithms are derived to be used in the path planning of the robot by combining bionics and artificial intelligence subjects, so that the planning efficiency of the mobile robot is improved. In the prior art, a melis optimization algorithm (Honey Badger Algorithm, HBA) is provided for carrying out robot path planning, the algorithm is a meta heuristic optimization algorithm, the algorithm is derived from the excavation and honey-picking foraging behaviors of melis, the melis approaches to food through excavation, in the path planning, the algorithm is characterized in that local adjustment is carried out on a current path, and when fitness value convergence is achieved after path updating, the current optimal path is output. However, the algorithm relies on random exploration and usually only combines path length and obstacle punishment, which is helpful for global search, but can cause excessive iteration to be wasted in non-optimal areas, so that the algorithm is easy to fall into inefficient exploration, and the path planning effect is poor. Disclosure of Invention The invention aims to overcome the defects in the prior art and provide a robot path planning method, a system, equipment and a medium, so as to solve the problems in the prior art. The invention specifically provides the following technical scheme: a robot path planning method, comprising: modeling an environment of robot path planning to obtain an environment model with an obstacle area and a free area; In an environment model, taking the minimum total length of a path, the minimum turning times and the minimum planning time as optimization targets when the robot path is planned, constructing a badger optimization algorithm to find an objective function of the path, and taking obstacle avoidance and the maximum corner as constraint conditions; Generating a candidate path for each badger individual based on the objective function and the constraint condition, acquiring the fitness of each candidate path, selecting an excavation mode and a honey collection stage according to environmental parameters, performing disturbance and elite tangent search on the badger individual, and updating the current position and fitness value of the badger individual, wherein the environmental parameters represent the density of obstacles, and the current position of the badger individual represents one path point of the robot; And iteratively executing the updating process of the current position and the fitness value of the badger individual, reserving the optimal badger individual, and connecting candidate paths corresponding to each generation of optimal badger individual to serve as the optimal path of the robot planning. Preferably, the obtaining the fitness of each candidate path includes: obtaining density factors of populations in badger optimization algorithm ; Initializing a population according to the Tent chaotic map, judging whether the category number of the initial population is higher than a threshold value, increasing the global searching range through a density factor when the category number of the initial population is higher than the threshold value, setting the initial value of the density factor to be a preset value higher than the initial value when the category number of the initial population is lower than the threshold value, searching again, generating the adaptability of each candidate path and sequencing. Preferably