DeepNet-Based 3D Visual Servoing Robotic Manipulation

Abstract

The fourth industrial revolution (industry 4.0) demands high-autonomy and intelligence robotic manipulators. The goal is to accomplish autonomous manipulation tasks without human interventions. However, visual pose estimation of target object in 3D space is one of the critical challenges for robot-object interaction. Incorporating the estimated pose into an autonomous manipulation control scheme is another challenge. In this paper, a deep-ConvNet algorithm is developed for object pose estimation. Then, it is integrated into a 3D visual servoing to achieve a long-range mobile manipulation task using a single camera setup. The proposed system integrates (1) deep-ConvNet training using only synthetic single images, (2) 6DOF object pose estimation as sensing feedback, and (3) autonomous long-range mobile manipulation control. The developed system consists of two main steps. First, a perception network trains on synthetic datasets and then efficiently generalizes to real-life environment without postrefinements. Second, the execution step takes the estimated pose to generate continuous translational and orientational joint velocities. The proposed system has been experimentally verified and discussed using the Husky mobile base and 6DOF UR5 manipulator. Experimental findings from simulations and real-world settings showed the efficiency of using synthetic datasets in mobile manipulation task.