Autonomous Path Planning Logistics Sorting Robot Experimental Teaching Platform Based on Aruco Marker

Abstract

In order to optimize the teaching content of “Engineering Training II” for Automation Specialty, the curriculum teaching system has been reshaped with project-based teaching as the center based on the characteristics of the discipline. We have designed a logistics robot experimental teaching platform that combines intelligent vehicles and robotic arms based on ARUCO Marker positioning. The system uses STM32 as the main control chip, Quanzhi H6 as the computing power platform, L298N and DC deceleration motor as running drivers, and the Yuejiang robotic arm as the cargo grabbing device. At the same time, combining ARUCO Marker recognition, Dijkstra optimal path planning, and incremental PID as intelligent algorithms, it achieves functions such as navigation control, cargo handling, and fixed point stacking for the logistics sorting robot system. The experimental results show that the system has fast recognition speed, high positioning accuracy, low price, and is easy to expand. It can effectively improve students' ability to comprehensively apply basic theoretical knowledge to solve practical problems and enhance their engineering literacy.