An Autonomous AI-Vision Robotic System with Mecanum Wheels for Real-Time Object Sorting and Navigation
Keywords:
AI vision, , Mecanum wheels, Robotic arm, Sensor fusion, Autonomous navigation, Industrial automationAbstract
This study presents an autonomous robotic system combining AI vision, a Mecanum wheel-based mobile platform, and a servo-driven robotic arm for real-time object sorting and navigation in dynamic environments. The system employs an Arduino-based control framework with multi-sensor fusion (ultrasonic, infrared, and Bluetooth) to achieve obstacle avoidance, line following, and voice-command responsiveness. A novel color-based object classification algorithm is implemented, achieving a sorting accuracy of 94.2% for standardized packages in experimental trials. The Mecanum wheels enable omnidirectional mobility, reducing navigation time by 38% compared to conventional differential drives in constrained spaces. Experimental results demonstrate the system’s efficacy in smart manufacturing and automated logistics, with potential applications in warehousing and healthcare automation. This work bridges critical gaps in flexible, low-cost robotics and provides a scalable framework for future extensions, such as machine learning-based shape-agnostic grasping.
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