A Case Study of Cyber-Physical System Design: Autonomous Pick-and-Place Robot

Although modern robots in warehousing systems can perform adequately in a goods-to-person model using hand-designed algorithms that are specialized to a particular environment, developing a robotic system that is capable of handling new products at an inexpensive cost remains a challenge. A conspicuous example of this challenge is seen in Amazon's use of autonomous robots to fetch customers' orders in their massive warehouses. To encourage advance in this technology, Amazon organized the competition, Amazon Picking Challenge that asked participants to develop their own hardware and software for the general task of picking a designated set of products from inventory shelves and then placing them at a target location (called a pick-and-place task). Current technology for pick-and-place tasks is still insufficient to meet the demand for low-cost automation. Handling awkward or oddly shaped object must still depend on hand-programming or specialized robotic systems, making manufacturing automation less flexible and expensive. In this paper, we shall present the design and implementation of a software system that is a step in advancing the technology toward full automation at reasonable costs. Our system integrates a set of state-of-the-art techniques in computer vision, deep-learning, trajectory optimization, visual servoing to create a library of skills that can be composed to perform a variety of robotic tasks. We demonstrate the capability of our system for performing autonomous pick-and-place tasks with an implementation using Hoppy, an industrial robotic arm in an environment similar to the Amazon Picking Challenge.