An Interactive Perception Method for Warehouse Automation in Smart Cities

Abstract

The smart city is an integrated environment that heavily relies on intelligent robots, which provides the basis for the warehouse automation. However, a warehouse is a typical unstructured environment, and robotic grasp and manipulation are extremely important for the package, transfer, search, and so on. Currently, the most usual method is to detect the picking or grasping points for some specific end-effector including suction cup, gripper, or robotic hand. The manipulation performance is, therefore, strongly influenced by the visual detector. To tackle this problem, the affordance map has recently been developed. It characterizes the operation possibilities afforded by the operation scene and has been used for several grasp tasks. Nevertheless, the conventional affordance method often fails in complicated environments due to the mistake calculation results. In this article, we develop a novel framework to integrate the interactive exploration with a composite robotic hand for robotic grasping in a complicated environment. The exploration strategy is obtained by a deep reinforcement learning procedure. The developed new composite hand, which integrates the suction cup and grippers, is used to test the merits of the proposed interactive perception method. Experimental results show the proposed method significantly increases the manipulation efficiency and may bring great economic and social and benefits for smart cities.