Research On Path Planning Of Intelligent Service Robots

With the development of science and technology,the issue of autonomous movement of intelligent service robot has always been concerned by researchers.As one of the main technologies of this issue,path planning has far-reaching research significance.Path planning is a key problem for intelligent service robot to walk freely.Its essence is a collision free path that the robot seeks from the starting position to the target point without human participation.According to the robot's mastery of the working environment,path planning can be divided into two types: global path planning and local path planning.The former is used for the known environment of the interior map of the motion region;The latter can work under the condition that the map environment is completely unknown,and the robot's own sensor is needed to continuously collect and analyze the environmental data for path planning,so that the robot can constantly update the path while finding the target point.Common global path planning algorithms include A~* algorithm,viewable method,genetic algorithm,ant colony algorithm and so on.Local programming algorithms include neural network algorithm,artificial potential field algorithm,fuzzy logic algorithm,RRT algorithm and so on.Aiming at the actual demand of intelligent service robot path planning,based on the advantages and disadvantages of the existing algorithm of external environment of practical ability,this paper proposes A method based on A * and artificial potential field method,path planning algorithm for intelligent service robot path planning in dynamic environment,can let the intelligent service robot and has strong path search ability and the ability of obstacle avoidance.Firstly,to solve the problems of multiple inflection points and long search time in the traditional A~* algorithm path planning process,an improved global path planning algorithm that extends the parallel search neighborhood is used to make the path smoother.Secondly,the characteristics of artificial potential field method in path planning are studied and analyzed,and the existing problems are improved.Finally,A~* algorithm is used to obtain the global optimal path,and artificial potential field method is used to implement local path planning and guide robot movement.According to the actual movement of the robot,the algorithm adds processing methods for dynamic environment,including detection and processing of local minimum state,updating rules of global path and local path,and processing of other abnormal states.Simulation results show that the fused algorithm can effectively shorten the path length and improve the planning efficiency.