Research On Hybrid Path Planning Method Of Mobile Robot

With the rapid development of science and technology,people are increasingly enjoying the convenience brought by “intelligence”.Among them,mobile robots appear as a high-tech “intelligent” product in human vision,and path planning is one of the key factor of development of mobile robots.In a complex environment,the mobile robot can not complete the scheduled task because of the traditional path planning methods with disadvantages of different levels.Aiming at the deficiencies of traditional path planning methods,a hybrid planning method based on an two-wheel differential mobile robot is proposed.This method first uses the improved particle swarm algorithm for global path planning and obtains an optimal global expectation.The path uses the inflection point of the desired path as the sub-target point of the local path planning,and then uses the improved artificial potential field algorithm to perform real-time local path planning.The global path planning and local path planning methods are combined to obtain a better path for the robot,the above-mentioned hybrid algorithm is used to complete the path planning task of the mobile robot in a complex environment.The main research contents of this paper are as follows:(1)The research background and status of mobile robot path planning and the research significance of this topic is introduced in this article,and the advantages and disadvantages of the global path planning method and local path planning method is analyzed,the main research contents of this thesis is described briefly.On the basis of this,a kinematics model of the robot is established,the control methods of the robot's basic motion such as straight line,curve,and rotation are analyzed.At the same time,the method and principle of acquiring the position information of the robot are introduced,and the geometric model method is used to create the environment map,which made the foundation of the next-step path planning for the robot.(2)Research on the global path planning of mobile robots under the known conditions of environmental information.The advantages and disadvantages of the original particle swarm algorithm and the bacterial foraging algorithm were analyzed.The chemotaxis operation of the bacterial foraging algorithm was introduced into the search process of the particle swarm algorithm.The improved particle swarm algorithm was used to carry out path planning for the mobile robot and the trapped particle swarm algorithm in the defect of the local extreme point,also accelerates the convergence speed of the particle swarm optimization algorithm.Thus,the obtained path is better.(3)Research on the local path planning of mobile robots in a dynamic obstacle environment,analyze the inaccessibility of the traditional artificial potential field method and the inability to avoid the defects of dynamic obstacles,and solve the obstacle avoidance and target inaccessibility problems of mobile robots to dynamic obstacles by improving the repulsive field function of the traditionalartificial potential field method.(4)For the path planning problem of mobile robots in complex environments,a hybrid algorithm combining the global path planning method and the local path planning method is designed.The path planning effect of mobile robots using hybrid algorithms in complex environments is verified by simulation experiments,and necessary analysis of the results is analyzed.On the basis of this,a brief summary of the full-text work is made and the existing problems are analyzed.