Study On Hybrid Path Planning For Mobile Robot With Partial Unknown Environment

With the development of science and technology,mobile robots have been well applied in the fields of industry,agriculture,hazardous places,and urban safety.Path planning is one of the key technologies of mobile robots.The main task is to plan a collision-free,optimal or near-optimal path that the robot can safely reach the destination from the starting point,based on environmental information and related algorithms.According to the different degrees of mobile robots' grasp of environmental information,path planning algorithms can be divided into global planning and local planning.Global planning is an offline planning,which requires all information about the environment to be known in advance,but most of the environment is full of changes and there are unknown obstacles.The use of global planning algorithms may prevent mobile robots from avoiding obstacles.Therefore,although global planning can achieve the goal of optimal path,it has poor real-time performance;local planning is generally used in unknown environments,and real-time acquisition of environmental information through sensors is used for planning.The real-time performance is great,but it lacks globality and is easy to fall into local optimality and the phenomenon that the goal is unreachable.In view of the limitations of single planning,thesis uses a hybrid path planning algorithm,which can achieve autonomous obstacle avoidance for uniformly variable linear motion obstacles while considering the optimal global path.This thesis provides some solutions to these problems.Firstly,in the global planning,the grid method is used for modeling,and the biologically inspired neural network algorithm is used to search a feasible global path.In order to solve the problem of non-optimal path when there are many obstacles in the algorithm,on the basis of the existing improved algorithm,the calculation method of neuron excitation input is changed to improve the efficiency of the algorithm.To solve the problem of redundant inflection points in the improved path,the key node selection strategy is adopted to smooth the path.Through the simulation and comparison experiment in MATLAB,it is verified that the algorithm can effectively solve the path error problem.Secondly,aiming at the problem that a single local obstacle avoidance algorithm is difficult to adapt to any unknown environment,the obstacle avoidance problem of uniformly variable linear motion obstacles is studied on the basis of the existing research on linear motion obstacles.With the method of rolling window method,the trajectory prediction model of obstacles is established.For obstacles that meet different conditions,the corresponding obstacle avoidance behavior is designed in combination with robot speed.Through MATLAB simulation experiment,it is verified that the algorithm can avoid obstacles effectively.Finally,for the problem of poor real-time performance of global planning and lack of globality of local planning,on the premise of ensuring a better global path,combined with local obstacle avoidance,hybrid planning is performed.In the same simulation environment,through the hybrid path planning experiment of single obstacle and multiple obstacle avoidance,it is verified that the algorithm can perform local obstacle avoidance on the premise of ensuring the optimal global path.