Research On Path Planning Of Mobile Robot Based On Swarm Intelligence Algorithm

Nowadays,with the rapid development and wide application of robotics,as a key problem in the field of robotics,path planning technology has attracted more and more attention from experts and scholars at home and abroad.Mobile robots rely on their various sensors to perceive the external space environment and their own state information and complete the task of finding the optimal path from the starting point to the end point in the working environment with obstacles,which is the path planning technology for mobile robots.As an important part of mobile robot navigation technology,path planning technology has very important research value.In robot path planning,the robot first needs to obtain accurate environmental information and plan a path from the beginning to the end after avoiding obstacles.In this thesis,the artificial fish swarm algorithm in the classical swarm intelligence algorithm is studied.On this basis,an improved A*algorithm is further studied.The research contents of this thesis mainly include the following four aspects:1.Aiming at the path planning problem of mobile robots in grid environment,the traditional artificial fish swarm algorithm is improved.The field of view and step size are designed to dynamically adjust with the algorithm process,which improves the convergence speed and optimization precision of the algorithm.At the same time,the path optimization operator is designed to solve the problem of local length of the planned path.2.To improve the problems existing in the traditional A*algorithm,the storage structure of the OPEN table is optimized.In this thesis,the structure of the minimum heap is adopted to replace the original linked list structure for the storage of nodes,which reduces the time spent for the algorithm to traverse the nodes and improves the operation efficiency of the algorithm.At the same time,the heuristic function of A*algorithm is optimized,and the shortcomings of the traditional heuristic function optimization method are analyzed.We introduce A new method to improve the heuristic function,and the path is optimized twice for the phenomenon of long path,so that the path planned by the algorithm is the optimal path.Secondly,the method of node customization is adopted to make the path more reasonable.Finally,the superiority of the improved algorithm is verified by experiments.3.In this thesis,path planning based on improved AFSA in dynamic environment is studied.Firstly,collision types and obstacle avoidance strategies of small robots in dynamic environment are analyzed.Then,obstacle avoidance strategies of robots based on rolling Windows are proposed.4.This thesis designs a hybrid path planning platform for intelligent car based on Rikirobot.Firstly,it introduces how to configure the software development environment of upper computer in detail.Secondly,the ROS operating system is introduced,and the hardware platform construction and kinematics model of the robot are analyzed.The improved A*algorithm was used to replace the global path planner of the Navigation function package of ROS,and the improved artificial fish swarm algorithm was added to the local path planner to form A hybrid path planning algorithm.At last,the obstacle avoidance experiment in real environment is carried out.