Research On Fusion Path Planning Algorithm And SLAM Technology For Mobile Robot

Mobile robots,as intelligent devices that improve factory production efficiency and human quality of life,are widely used in industries such as warehousing,healthcare,special operations,and smart homes.With the development of technology,the demand for quantity and performance continues to increase.Autonomous navigation ability,environmental perception ability,and real-time positioning ability are important evaluation indicators for robot intelligence.This article addresses the issues of path optimization and real-time obstacle avoidance in mobile robot path planning,as well as the low accuracy of Gmapping algorithm in mapping and positioning.Based on the ROS operating system experimental platform,a fusion path planning algorithm and SLAM technology are used to achieve autonomous robot movement.The main research contents are as follows:(1)A path planning method(IHHO-DWA)is proposed,which combines the improved Harris Hawk algorithm(IHHO)and dynamic window approach(DWA),to address the problems of real-time obstacle avoidance and multi peak optimization of the objective function in traditional grid based path planning algorithms.Propose a square neighbor lattice neighboring diffusion method to initialize the position of the Harris Hawk population,solving the problem of population initialization being stochastic and falling into local optima;Propose a nonlinear energy factor implementation algorithm to effectively switch between the search and development stages,improving the global search ratio.Introducing the dynamic window method as local path planning,constructing a dynamic window evaluation function that combines global paths to improve the lack of foresight in the dynamic window method.Verify the effectiveness and real-time obstacle avoidance of the fusion path planning algorithm through simulation experiments.(2)An improved Gmapping method is proposed to address the issue of insufficient positioning and mapping accuracy caused by accumulated errors in traditional Gmapping algorithms that rely on odometer motion models for estimating pose and mapping.In the scanning matching stage,when the environmental similarity is low or the matching score is low due to noise,an extended Kalman filter is introduced to fuse the motion model odometer data and the laser data after likelihood estimation to reduce the impact of odometer errors on particle pose updates;Propose a resampling strategy that integrates motion model weights to further reduce the impact of the algorithm’s simple use of odometer motion models as particle postures.Through simulation experiments,the improved Gmapping algorithm is validated in improving the accuracy of mapping.(3)According to the actual application scenarios of robots,the Mecanum wheel mobile experimental platform based on ROS is used to complete the indoor mapping,positioning and path planning experiments.Verify the global consistency of the improved algorithm by comparing the grid map and sensor errors of the Gmapping algorithm before and after improvement in actual scenarios;On the premise of establishing a global map,compare the improved Harris Hawk path planning algorithm and particle swarm optimization path planning algorithm in this paper to verify the superiority of the proposed method;Verify the feasibility of the fusion algorithm in this paper by adding dynamic obstacles to the global map.