Research On Robot Path Planning Based On Improved Potential Field Method And Particle Swarm Optimization

The path planning of mobile robot is an important content in the field of robotics.In this paper,the robot path planning problem with dynamic obstacle and target environment is studied according to the latest algorithms of robot path planning.In the dynamic environment,a fusion algorithm is proposed.First of all,the original artificial potential field method is improved,combining the improved artificial potential field method and particle swarm algorithm,and the fusion algorithm make a path planning in dynamic environment.When the robot is in the region of minimum value,the robot is escaped from the trap by the escape force.Then,the particle swarm optimization(PSO)algorithm is used to continue the path optimization,which ensures the mobile robot can avoid the dynamic obstacle in real time and find an optimal path.Compared with the particle swarm optimization algorithm,the fusion algorithm can plan a better path.The main contents are as follows:Firstly,make environment modeling for the global path planning,improve raster coding and determine the grid granularity,so that the planning algorithm is simple and effective.Secondly,in the static environment,this paper proposes an improved artificial potential field method.When the robot is trapped in the local minimum point,the algorithm can make the robot escape from the minimum region effectively and quickly.In this paper,an improved Particle Swarm Optimization(PSO)algorithm is proposed,which can avoid premature convergence or premature convergence.It can improve the global development ability and local search ability of the population,and improve the performance of the algorithm.So that the robot can plan a relatively better path.Thirdly,when there are fixed obstacle,dynamic obstacle and dynamic target point in the environment,this paper proposes an improved artificial potential field and particle swarm fusion algorithm.Under the artificial potential field method,an "escape force" is introduced.When the robot is trapped,the robot escapes and moves in the direction of the target point under the action of "escape force".Then the robot switches to PSO to continue the path planning.When the target moves,the robot senses the gravitational force to determine the direction of the target point.The fusion algorithm is simulated and compared with the particle swarm optimization algorithm.It shows that the fusion algorithm is simple and can be used in the real environment.