Object Recognition and Localization for Pick-and-Place Task using Difference-based Dynamic Movement Primitives

In this paper, a framework is developed for three dimensional (3D) object recognition, localization, and anthropomorphic manipulation using YOLOv5, two low-cost webcams, and a Franka Emika (FE) Panda manipulator. Autonomous pick-and-place tasks are executed in a human-like manner using impedance control and novel difference-based Dynamic Movement Primitives (DMP) trajectory planning. The high accuracy vision system is developed with two webcams by using triangular relation to determine the object's position. The difference-based DMP algorithm and task execution plan are used to learn unique trajectories for different objects based on human demonstration and the object's coordinates. By integrating these components together, experimental studies on pick-and-place tasks are successfully performed for two different objects.