A Framework and Process Library for Human-Robot Collaboration in Creative Design and Fabrication

In the last two decades, the increasing affordability of industrial robots, along with the growing maturity of computational design software, has led architects to integrate robots into their design process. Robots have exceptional capabilities that enable the fabrication of geometrically complicated components and assembly of complex structures. However, the robot control and motion programming tools currently being adopted by designers were all initially intended for engineering-based manufacturing industries. When using computer-controlled tools, designers cannot adapt their designs to the production process in real time. Current industrial robot control systems force the designer to envision and embed all of the required machining data in the digital model before the fabrication process begins. This requirement makes the process of design to fabrication a unidirectional workflow. In pursuit of a solution, a growing body of research is exploring various human-robot collaboration methods for architectural practices. However, many of these studies are project- based, targeting the ad hoc needs of a particular robotic application or fabrication process. Consequently, this dissertation investigates a generalizable framework for human-robot collaboration that is rooted in the principles of distributed cognition. As an essential part of the research argument, the role of the tools of production in the formation of a designer's cognitive system is considered. This framework, defined for a bi-directional design and fabrication workflow, relies on and integrates material and fabrication feedback into the design process. The framework has three main components: interactive design, adaptive control, and a design and fabrication library. While different aspects of these components have been studied to various extents by other researchers, this dissertation is the first to define them in an integrated manner. Next, the requirements for each of these elements are introduced and discussed in detail. This dissertation focuses in more detail on the library component of the framework because compared to the first two components, it is the least investigated solution to date. A structure for the library is proposed so that the tacit knowledge of makers could be structured, captured, and reused. At its core, the library is a process-centric database where each process is supported by a set of tools, instructions, materials, and geometries required for the transformation of a part into its final form. Finally, this study demonstrates the generalizability of the library concept through a series of experiments developed for different material systems and with various robotic operations.Ph.D

Camera Marker Networks for Pose Estimation and Scene Understanding in Construction Automation and Robotics.

The construction industry faces challenges that include high workplace injuries and fatalities, stagnant productivity, and skill shortage. Automation and Robotics in Construction (ARC) has been proposed in the literature as a potential solution that makes machinery easier to collaborate with, facilitates better decision-making, or enables autonomous behavior. However, there are two primary technical challenges in ARC: 1) unstructured and featureless environments; and 2) differences between the as-designed and the as-built. It is therefore impossible to directly replicate conventional automation methods adopted in industries such as manufacturing on construction sites. In particular, two fundamental problems, pose estimation and scene understanding, must be addressed to realize the full potential of ARC. This dissertation proposes a pose estimation and scene understanding framework that addresses the identified research gaps by exploiting cameras, markers, and planar structures to mitigate the identified technical challenges. A fast plane extraction algorithm is developed for efficient modeling and understanding of built environments. A marker registration algorithm is designed for robust, accurate, cost-efficient, and rapidly reconfigurable pose estimation in unstructured and featureless environments. Camera marker networks are then established for unified and systematic design, estimation, and uncertainty analysis in larger scale applications. The proposed algorithms' efficiency has been validated through comprehensive experiments. Specifically, the speed, accuracy and robustness of the fast plane extraction and the marker registration have been demonstrated to be superior to existing state-of-the-art algorithms. These algorithms have also been implemented in two groups of ARC applications to demonstrate the proposed framework's effectiveness, wherein the applications themselves have significant social and economic value. The first group is related to in-situ robotic machinery, including an autonomous manipulator for assembling digital architecture designs on construction sites to help improve productivity and quality; and an intelligent guidance and monitoring system for articulated machinery such as excavators to help improve safety. The second group emphasizes human-machine interaction to make ARC more effective, including a mobile Building Information Modeling and way-finding platform with discrete location recognition to increase indoor facility management efficiency; and a 3D scanning and modeling solution for rapid and cost-efficient dimension checking and concise as-built modeling.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113481/1/cforrest_1.pd

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

EG-ICE 2021 Workshop on Intelligent Computing in Engineering

The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways