Year: 2025
Type: conference-paper
Abstract: This paper presents an intelligent robotic arm system leveraging YOLOv8 to tackle perception delays and low target recognition accuracy in complex battlefield environments. By integrating six-axis robotic arm kinematics with deep-learning visual detection, we develop a cohesive "perception-decision-execution" framework. Initially, target detection utilizes the YOLOv8 model, with spatial pose derived from binocular vision depth calculations, enhanced by training on a specialized dataset. Subsequently, a kinematic model of the robotic arm is created, with MATLAB simulations analyzing the end-effector's workspace and enabling autonomous trajectory planning. Finally, a Python-based control system facilitates autonomous recognition and grasping. Experimental results demonstrate high target recognition accuracy and grasping success rates in complex settings, offering a viable solution for intelligent equipment development in military and industrial applications.
Authors: Yuming Zhou, Xing Xu, Yuquan Xue, Liming Wang
Institution: Naval University of Engineering
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
Award: G3835358509
OpenAlex ID: W4414179728