Abstract
There is growing interest in creating agile industrial robotics applications for autonomous operations on small-volumes of mixed parts to complement traditional industrial robotics that handle large-volume, single-part operations. Cloud robotics, which leverages cloud computing, cloud storage and high-speed networks (between factory floors and data centers), is seen as a technological approach to help build such agile industrial robotics applications.
This thesis describes an agile industrial robotics application, named Gilbreth, for picking up objects of different types from a moving conveyor belt and sorting the object into bins according to type. The Gilbreth implementation leveraged a number of Robot Operating System (ROS) and ROS-Industrial (ROS-I) packages. Gazebo, a robotics simulation package, is used to simulate a factory environment that consists of a moving conveyor belt, a break beam sensor, a 3D Kinect camera, a UR10 industrial robot arm mounted on a linear actuator with a vacuum gripper, and different types of object such as pulleys, disks, gears and piston rods.
Experimental studies were undertaken to measure the CPU usage and processing time of different ROS nodes. These experiments found that object recognition time and robot execution time were similar in magnitude, and that motion planning sometimes yielded incorrect trajectories. Therefore, improvements were made to reduce object recognition time, using a Convolutional Neural Network (CNN) method and with a new pipeline, and to the motion-planning pipeline. Evaluation of the object recognition improved pipeline demonstrates that it outperforms the original Correspondence Grouping (CG) method by reducing execution time even while achieving the same success rate. Experiments were conducted to evaluate the pick-and-sort success rate of the Gilbreth application after the improved pipelines were incorporated. Specifically, we found that objects should be spaced at least 14 sec apart from each other on the conveyor belt. Multiple robot workcells are required to handle conveyor belts with faster arriving objects.
Finally, we undertook an experiment to evaluate the scalability of the CNN algorithm. Our conclusion is while CNN-based object-recognition saves processing time within the run-time operation of the Gilbreth application when compared the CG algorithm, the cost of CNN-based object-recognition is that it requires significant compute cycles for training. Given that this training can be done offline, the extensive resources of cloud computing can be leveraged.
