Research On Improved Intelligent Algorithm In Mobile Robot Path Planning

Mobile robots play an important role in People's Daily life.Robot's simple,convenient,intelligent and other advantages have been loved by people.Robots can meet people's various needs,such as cargo handling,terrain detection and other heavy,dangerous work.In order to enable the mobile robot to find its path autonomously,the research on its path planning is particularly necessary.Many scholars have proposed a variety of algorithms to study the path planning of mobile robots,but these algorithms still have some shortcomings in the path planning of mobile robots.In order to make up for the shortcomings of the robot path planning algorithm,improve the performance of the algorithm,and make it more helpful for the robot to adapt to a variety of map environments when walking,this paper improves and verifies the three intelligent algorithms.In this paper,three basic path planning algorithms are improved to make each algorithm suitable for different map environments.The main work of this paper is as follows:Firstly,in order to solve the problems of ant colony algorithm,such as large computation and long computation time,the heuristic function was improved.In order to solve the problems of easy to fall into local optimum and slow convergence speed,the pheromone volatile factor was adaptively adjusted,and the experimental simulation was carried out to verify it.Secondly,in order to solve the problems of genetic algorithm,such as slow convergence speed,easy to fall into the local optimal solution and many turns,the smoothness function of the penalty term and the elite reservation strategy were introduced to adjust the size of the crossover mutation probability adaptively,and the simulation comparison was conducted to verify the effectiveness and superiority of the algorithm.Finally,in order to solve the problem of particle swarm optimization,which is easy to fall into the local optimal solution and has a long planning path,the inertia weight is introduced into the velocity vector updating formula and makes it adapt to adjust,which is verified by the simulation experiment.In this paper,the disadvantages of the above three algorithms are described and analyzed,and then the three intelligent algorithms are respectively improved and verified by simulation.Finally,the effectiveness of the algorithm is verified by comparing with the basic algorithm and other algorithms in the literature.