Manipulator system designs for drawing random objects through point cloud posture estimation

Abstract

This paper designed a 7-DOF redundant robot manipulator that can flexibly and efficiently pick-up random objects. The developed 7-DOF machine with an additional redundancy achieved great progress in terms of flexibility and efficiency in the operational space. A robot operating system (ROS) was used to configure the manipulator system’s software modules, supporting convenient system interface, appropriate movement control policy, and powerful hardware device management for better regulation of the manipulator’s motions. A 3D type Point Cloud Library (PCL) was utilized to perform a novel point cloud image pre-processing method that did not only reduce the point cloud number but also maintained the original quality. The results of the experiment showed that the estimation speed in object detection and recognition procedure improved significantly. The redundant robot manipulator architecture with the two-stage search algorithm was able to find the optimal null space. Suitable parameters in D-H transformation of forward kinematics were selected to efficiently control and position the manipulator in the right posture. Meanwhile, the reverse kinematics estimated all angles of the joints through the known manipulator position, orientation, and redundancy. Finally, motion panning implementation of manipulator rapidly and successfully reached the random object position and automatically drew it up to approximate the desired target.