Autonomous Robot Packing of Complex-shaped Objects

With the unprecedented growth of the E-Commerce market, robotic warehouse automation has attracted much interest and capital investment. Compared to a conventional labor-intensive approach, an automated robot warehouse brings potential benefits such as increased uptime, higher total throughput, and lower accident rates. To date, warehouse automation has mostly developed in inventory mobilization and object picking.

Recently, one area that has attracted a lot of research attention is automated packaging or packing, a process during which robots stow objects into small confined spaces, such as shipping boxes. Automatic item packing is complementary to item picking in warehouse settings. Packing items densely improves the storage capacity, decreases the delivery cost, and saves packing materials. However, it is a demanding manipulation task that has not been thoroughly explored by the research community.

This dissertation focuses on packing objects of arbitrary shapes and weights into a single shipping box with a robot manipulator. I seek to advance the state-of-the-art in robot packing with regards to optimizing container size for a set of objects, planning object placements for stability and feasibility, and increasing robustness of packing execution with a robot manipulator.

The three main innovations presented in this dissertation are:

1. The implementation of a constrained packing planner that outputs stable and collision-free placements of objects when packed with a robot manipulator. Experimental evaluation of the method is conducted with a realistic physical simulator on a dataset of scanned real-world items, demonstrating stable and high-quality packing plans compared with other 3D packing methods.

2. The proposal and implementation of a framework for evaluating the ability to pack a set of known items presented in an unknown order of arrival within a given container size. This allows packing algorithms to work in more realistic warehouse scenarios, as well as provides a means of optimizing container size to ensure successful packing under unknown item arrival order conditions.

3. The systematic evaluation of the proposed planner under real-world uncertainties such as vision, grasping, and modeling errors. To conduct this evaluation, I built a hardware and software packing testbed that is representative of the current state-of-the-art in sensing, perception, and planing. An evaluation of the testbed is then performed to study the error sources and to model their magnitude. Subsequently, robustness measures are proposed to improve the packing success rate under such errors.

Overall, empirical results demonstrate that a success rate of up to 98\% can be achieved by a physical robot despite real-world uncertainties, demonstrating that these contributions have the potential to realize robust, dense automatic object packing.