STUDY ON THE POSITIONING ACCURACY FOR A NEURAL NETWORK CONTROLLED ROBOTIC ARM END-EFFECTOR

Cristian Marius BACICAN, Cristian Emil MOLDOVAN, Mihai MICEA

Abstract


This paper explores the potential of using a neural network to position a robotic arm's end effector. Traditional methods rely on direct and inverse kinematics, but this study draws inspiration from human hand-eye coordination—how people learn their hand’s workspace and reach for objects. In robotics, a similar approach could involve using a depth camera to detect and grasp objects directly. The neural network learns the relationship between joint angles and end-effector positions within the camera’s field of view, with applications in manipulation, object handling, and collaborative robotics in dynamic environments. Simulations on a 3-DOF robotic arm tested 10 architectures, revealing feasibility for direct kinematics but challenges in inverse kinematics requiring further research.

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