Design of Sorting Service Robot Based on Autonomous Navigation and Deep Learning
In response to the challenges posed by the inefficiency,high error rates,and elevated labor costs associated with traditional manual sorting methods,this study investigated the design of a sorting service robot system.A sorting service robot was conceptualized based on autonomous navigation and deep learning.The system optimized the global path planning for indoor robot navigation using an improved A* algorithm.Additionally,object recognition and three-dimensional positioning were achieved through the integration of the YOLOv8 algorithm with 3D point cloud data.Moreover,the ROSClient mobile app,connected to the robot system via Rosbridge over a local network,enabled re-mote scheduling and monitoring.Experimental validation confirmed the sorting service robot's ability to accurately i-dentify different types of goods and efficiently complete sorting tasks within a short time frame.In comparison to tra-ditional sorting methods,the robot significantly improved sorting efficiency and accuracy.The system's intelligent sorting process presents an innovative solution for the field of service robot research.Applicable in various settings such as households,supermarkets,and hotels,this system demonstrates significant economic and societal value.
YOLOv83D point cloudsautonomous navigationRosbridgetarget scrapingtask scheduling
NETL
NSTL
万方数据
