Mobile Robot Motion Planning Based On Particle Swarm Optimization Algorithm

As one of the most important technologies studied in recent years,mobile robot has been widely used in many fields.Robot navigation is an important research direction of mobile robot.All the functions are based on autonomously moving,as one of the most important research topic in robot navigation,it has drawn much attention from academia.Path planning refers to finding a safe,collision-free path from the starting point to the ending point in an environment with different obstacles.Path planning is not independent in robotics research.Any research project can not be done independently,and will join other related disciplines,path planning is also the same.In order to complete a path planning successfully,but also need for example robot sensing,communication and other functions to assist in the completion,so the robot path planning is a very meaningful research.This article focuses on robot local path planning,the main contents of this article are as follows:Firstly,after investigating the existing problem of robot local path planning,we proposed a PSO-based receding horizon control approach is proposed,in which the robot is unknown to environment information and the obstacles is stationary.In order to avoid obstacles,a virtual robot moving along the edge of the obstacle at a certain speed is proposed where the virtual robot and the target position are integrated,which implies that mobile robots are controlled to keep a security distance and velocity consensus with virtual robots,and to move toward the target position.Next,the proposed cost function with constraints is processed by a particle swarm optimization algorithm such that the PSO-based receding horizon control approach is developed.On the Matlab platform and ROS platform,simulation and experiment is used to demonstrate the effectiveness of the PSO-based receding horizon control approach.Secondly,after repeated simulations and experiments,we find that the local path planning method is easy to fall into local minimum in some special circumstances.After studying some existing methods for solving local minimum,we propose a virtual target point method.When a robot reaches a local minimum,we assume a virtual target point outside the perceived obstacle boundary.The robot will first reach our set virtual target point and then move to the real target point.This method cansolve local minimum problem very well,and will not fall into the minimum point repeatedly.We use the simulation and ROS platform experiments have fully validated the feasibility of the improved algorithm.Thirdly,multi-robot particle path planning based on multi-objective particle swarm optimization is proposed.The algorithm can improve the working efficiency of robots in practical application and has very far-reaching significance.By prioritizing robot priorities and traffic rules,coordinate the resource-conflicting areas,the way robots move,and the order in which they occur.The simulation results show the effectiveness of the algorithm.