The Research Of Autonomous Mobile Robot Path Planning Algorithms

Since the 21 st century,with the rapid development of modern science and technology and intelligent level,mobile robot technology has obtained the attention and research from more and more experts.Mobile robot technology involved in many research fields,such as machinery manufacturing,computer programming,electronic science,control science,etc.In recent years,mobile robot technology has been successfully applied in warehousing logistics,machinery manufacturing,medical rehabilitation,and space navigation,and many other areas of public service.As a result,people put forward higher requirements on the robot in terms of intelligent degree,stability,security and efficiency,etc.Path planning technology is one of the key issues in the study of mobile robot navigation technology.This topic aims to autonomous mobile robot path planning algorithm research and make simulation by using MATLAB/2014 b.Firstly,this paper introduces the background,significance,development level,application field and the research status at home and abroad of the intelligent mobile robot path planning.Analyze some of the problems of path planning technology,in the field of robotics research at present,and summed up the problems that need to be solved.At the same time,the paper analyzes the generation,the basic principle and the research status of the particle swarm optimization(pso)algorithm and ant colony algorithm.Improved particle swarm optimization(pso)algorithm for easily comes into premature convergence,and add premature convergence judgment mechanism.If the algorithm comes into premature convergence,and assign a value to the speed of the particles anew.The ant colony algorithm,with the increase of the number of iterations,ant colony easily trapped in local optimal solution.In view of this point,improve pheromone update rule.Secondly,fuse the two algorithms together,this is improved particle swarm optimization(pso)algorithm and ant colony algorithm.The basic idea of the fusion algorithm is: first using the particle swarm algorithm for global path for rough search in the obstacle space.At the same time,make dynamic intelligent distribution for pheromones.And eliminate some poor path according to the content of pheromones.Use improved ant colony algorithm for the secondary search,to search the optimal path that not to be eliminated.Until find out the optimal solution.The fusion algorithm can speed up the optimal path search time and improve the search accuracy.Finally,using MATLAB/2014 b simulate the fusion algorithm.Then compare the simulation results with traditional particle swarm optimization(pso)algorithm,improved particle swarm algorithm,traditional ant colony algorithm and improved ant colony algorithm.By comparing the optimal path,all paths and the convergence rate of the five groups algorithm we can know,compared with the other four groups algorithm,the fusion algorithm has higher precision and faster convergence speed.