USING THE LIGHTWEIGHT YOLOV8 SYSTEM FOR VISUAL DETECTION, LOGISTICS PACKAGE GRASPING, AND AUTONOMOUS NAVIGATION FOR MOBILE MANIPULATORS

In recent years, the global industrial production and manufacturing paradigm from mass production has continued to shift to customized production. Enterprise order processing is also increasingly showing strong timeliness, variety, small batch, and batch characteristics. The market demand for highly flexible robotic automated production lines is also continuing to grow. Mobile manipulator is a new type of robot that integrates two functions of mobile robots and robotic arms, which can plan routes, navigate accurately, avoid obstacles, and identify, sort, grasp, and transport items. It is widely used in logistics and warehousing, factories, indoor exhibition halls, etc., which can save the cost of manpower, improve efficiency, and create differentiated competitiveness. Despite its promising application, it also faces some problems, especially in the cooperative operation of the mobile platform and robotic arm. Understanding how to realize vision-based target intelligent recognition and grasping is still a current research challenge. In this paper, we build a mobile manipulator platform, deploy and verify a YOLOv8n-SCS-CE lightweight detection network proposed in the previous stage (which can detect common logistics parcels), and test the robot’s autonomous mobile grasping of parcels and autonomously navigating to the target place. The test demonstrates that it can utilize the improved YOLOv8 to achieve intelligent grasping and autonomous navigation, thereby solving the challenges of intelligent grasping and autonomous transportation for indoor mobile manipulators. This study provides key technologies and methodologies for the intelligent grasping and manipulation capabilities of mobile manipulators.