Research On Mobile Robot Path Planning Based On Hybrid Algorithm

In recent years,with the great upsurge brought by artificial intelligence,robot technology has attracted more and more attention.As industrial robots,unmanned aerial vehicles,sweeping robots and other robots gradually enter our daily life,robots are becoming more and more common.Path planning is an important research branch in robot technology field.The problem of path planning is simply to find an existing route from the starting point to the end point.This route needs to meet some preset standards,such as the shortest distance,the shortest time needed,the safety and reliability,and the smoothness of the route.There are different preset standards for different problems.This paper mainly studies the hybrid algorithm to solve the problem.Firstly,some relevant background knowledge and the significance of this research are introduced.Then it presents the classification of path planning methods as well as the current situation and development trend,and briefly introduces some theories and inspirations of hybrid algorithm.Two intelligent bionic algorithms,genetic algorithm and ant colony algorithm,are used to solve the path planning problem of mobile robot in static environment.Before that,the principle,basic steps and parameter influence of these two intelligent algorithms are briefly introduced.Then the mobile robot working environment model is established,and the complexity of the environment Settings is not the same.Finally,the results of the two algorithms are compared and their performance characteristics are analyzed.After determining the advantages and limitations of the two algorithms,appropriate improvements can be made to prepare for the next step of fusion.A hybrid algorithm is used to solve the problem in a static environment.The advantages of ant colony algorithm are fast convergence and strong searching ability.The disadvantage is that when the initial pheromone is initialized,the search purpose is not strong,which affects the efficiency.As the scale of the problem increases,it is easy to fall into local optimization.First,it is improved,and then the genetic algorithm is used to optimize the results obtained by the ant colony algorithm,and the two are effectively integrated(advantages reserved,shortcomings improved)to form a new iaco-ga hybrid algorithm for the path planning of mobile robots in a static environment.Finally,another hybrid algorithm is used to solve the problem in a dynamic environment.Ant colony algorithm(ACO)and genetic algorithm(GA)are both global planning algorithms.However,the artificial potential field method is a common local planning algorithm,which has the advantages of simple description,smooth and safe path generation,etc.,but it is easy to fall into the local minimum,and there is also the problem of unreachable target.Then the global planning algorithm(ant colony algorithm)is combined with the local planning algorithm(artificial potential field method),and the problem is solved in a dynamic environment.Firstly,it is necessary to improve the disadvantages of ant colony algorithm and artificial potential field method,and then effectively combine them to drive different algorithms to solve problems in a dynamic environment according to different conditions.