Application And Research Of Brain-controlled Wheelchair Autonomous Navigation System

The brain-controlled wheelchair is which combines brain-computer interface technology with mobile robot technology perfectly.With the growth of the smart medical industry,the brain-controlled wheelchair as a service robot has attracted more and more attention.Especially the disabled who has barrier in movement is eager to use brain-controlled wheelchair with the intention of improving their daily life's quality.The operation of traditional brain-controlled wheelchair may put the subject under great mental stress.During the using process,the subjects who wear the brain control equipment are required to concentrate themselves for a long time.In the case of distracting,some accident will occur easily.This thesis combines autonomous driving with brain-controlled wheelchairs which introduces environmental map construction,autonomous obstacle avoidance,path planning and navigation technologies to the brain-controlled wheelchairs.This thesis focuses on the fusion of brain-controlled wheelchairs and automatic driving technology.The main work of this article is as follows.Firstly,this thesis investigates the real-time location and map construction(SLAM)method of brain-controlled wheelchairs.This thesis discusses the optimization method and filtering method of the SLAM problem in detail which focused on the analysis and research of the SLAM method.The grid map representation method,the point cloud data matching processing and the particle filtering method are used for processing the environment map,front-end data and back-end data respectively.Besides,a loopback detection step finally merged into a complete environment map is adopted to fix the problem of position drift during the operation of the brain-controlled wheelchair.Secondly,the methods of brain-controlled wheelchair obstacle detection and obstacle avoidance are investigated.This thesis introduces several commonly used obstacle detection techniques and data processing methods which focuses on the research of Lidar data processing.The mean value filtering method and the K-means mean clustering algorithm are used to filter the sensor data and perform cluster analysis on the data respectively.Some experiments are performed to verify the effectiveness of the algorithm.Finally,autonomous navigation and path planning of brain-controlled wheelchairs is investigated.A * algorithm is adopted for global path planning,and different heuristic functions are used to optimize the algorithm.The results of experiments show that the improved algorithm can accelerate the operation speed.In order to solve the impact of uncertain factors in the environment,the DWA algorithm is used for local path planning.There are some experiments conducted to verify the effectiveness of the DWA algorithm.