Design Of A Gesture Controlled Robotic Gripper Arm Using Neural Networks

The aim of this work is to design an efficient low cost gesture controlled robotic gripper mechanism using image processing and neural networks to be used in industrial automation process. The robot is to be controlled with gesture commands along with visual feeds. The design consists of three modules: gesture command recognition module, object classifier module and robotic gripper arm module. The robot takes gesture commands by gesture recognition module; then it tracks the desired object using the object classifier module. And finally it grabs and displaces the object according to the command using the robotic gripper arm module. The gesture recognition module and the object classifier module use two distinct neural networks along with additional hardware to perform their tasks. This paper reports the design and fabrication process of the robot discussed so that specialized robots can be made using this design that works on industrial automation.