Research On Path Planning For Mobilerobots Based On Improved RRT~* Andartificial Potential Field Method

With the continuous development of science and technology and the application of artificial intelligence,mobile robots have entered thousands of households and are widely used in daily life.Path planning is the core technology in the field of mobile robots.When mobile robots operate in complex dynamic scenes,traditional path planning algorithms often have some defects,which make it difficult for robots to complete tasks smoothly.This thesis mainly studies the RRT~* algorithm and artificial potential field method and optimizes and improves the problems they have.At the same time,simulation and physical experiments are carried out to verify the effectiveness of the algorithm.The main research contents are as follows:(1)In response to the shortcomings of the RRT~* algorithm,such as strong randomness,slow convergence speed,and poor path feasibility,an improved RRT~* algorithm is proposed.Firstly,the target attraction bias strategy is introduced to determine the direction of random tree growth.Then,adaptive step size and target bias strategy are proposed to accelerate the convergence speed of the random tree.Finally,the inflection points of the generated path are smoothed.Simulation results show that the improved RRT~* algorithm significantly improves the convergence speed and generates higher quality paths.(2)To address issues with traditional artificial potential field methods such as local minima and unsuitability for dynamic scenes,an improved method is proposed.Firstly,a collision angle constraint factor and distance factor are added to the repulsion function to solve the problem of unreachable targets.Next,a virtual escape force and escape radius are implemented to prevent the robot from getting stuck in local minima.Finally,a velocity repulsion potential field is established to allow for dynamic obstacle avoidance.Experimental results demonstrate that the improved artificial potential field method can overcome these issues and successfully accomplish local path planning in dynamic environments.(3)For the global dynamic scenario,the fusion algorithm of improved RRT~* algorithm and artificial potential field method is proposed.The improved RRT~* algorithm is used to generate the global path,and the artificial potential field method is used to plan the local path when encountering unknown obstacles.The simulation results show that the fusion algorithm can effectively avoid dynamic obstacles,and the planned path is smooth and feasible.Finally,the fusion algorithm is verified by experiments in real dynamic scenarios.The ROS robot platform was built and relevant parameters were configured.The remote control of the upper computer was used to conduct map modeling and dynamic environment path planning experiments for the mobile robot.The ROS robot successfully avoided obstacles and reached the end point,which verified the effectiveness of the fusion algorithm.