Human-Robot Collaboration in Automotive Assembly

In the past decades, automation in the automobile production line has significantly increased the efficiency and quality of automotive manufacturing. However, in the automotive assembly stage, most tasks are still accomplished manually by human workers because of the complexity and flexibility of the tasks and the high dynamic unconstructed workspace. This dissertation is proposed to improve the level of automation in automotive assembly by human-robot collaboration (HRC). The challenges that eluded the automation in automotive assembly including lack of suitable collaborative robotic systems for the HRC, especially the compact-size high-payload mobile manipulators; teaching and learning frameworks to enable robots to learn the assembly tasks, and how to assist humans to accomplish assembly tasks from human demonstration; task-driving high-level robot motion planning framework to make the trained robot intelligently and adaptively assist human in automotive assembly tasks. The technical research toward this goal has resulted in several peer-reviewed publications. Achievements include: 1) A novel collaborative lift-assist robot for automotive assembly; 2) Approaches of vision-based robot learning of placing tasks from human demonstrations in assembly; 3) Robot learning of assembly tasks and assistance from human demonstrations using Convolutional Neural Network (CNN); 4) Robot learning of assembly tasks and assistance from human demonstrations using Task Constraint-Guided Inverse Reinforcement Learning (TC-IRL); 5) Robot learning of assembly tasks from non-expert demonstrations via Functional Objective-Oriented Network (FOON); 6) Multi-model sampling-based motion planning for trajectory optimization with execution consistency in manufacturing contexts. The research demonstrates the feasibility of a parallel mobile manipulator, which introduces novel conceptions to industrial mobile manipulators for smart manufacturing. By exploring the Robot Learning from Demonstration (RLfD) with both AI-based and model-based approaches, the research also improves robots’ learning capabilities on collaborative assembly tasks for both expert and non-expert users. The research on robot motion planning and control in the dissertation facilitates the safety and human trust in industrial robots in HRC

Rearrange Indoor Scenes for Human-Robot Co-Activity

We present an optimization-based framework for rearranging indoor furniture to accommodate human-robot co-activities better. The rearrangement aims to afford sufficient accessible space for robot activities without compromising everyday human activities. To retain human activities, our algorithm preserves the functional relations among furniture by integrating spatial and semantic co-occurrence extracted from SUNCG and ConceptNet, respectively. By defining the robot's accessible space by the amount of open space it can traverse and the number of objects it can reach, we formulate the rearrangement for human-robot co-activity as an optimization problem, solved by adaptive simulated annealing (ASA) and covariance matrix adaptation evolution strategy (CMA-ES). Our experiments on the SUNCG dataset quantitatively show that rearranged scenes provide an average of 14% more accessible space and 30% more objects to interact with. The quality of the rearranged scenes is qualitatively validated by a human study, indicating the efficacy of the proposed strategy.Comment: 7 pages, 7 figures; Accepted by ICRA 202

Exploring `Designer Context' in Engineering Design: The Relationship Between Self, Environment, and Design Methods

Engineering design methods support engineers’ decision-making throughout a design process in order to improve design outcomes. The selection and implementation of suitable design methods are therefore critical to project success. Prior engineering design research has focused on designers' professional experiences or the problem context for guiding method choices. Perhaps due to disciplinary norms of objectivity, individual characteristics outside of an engineer's professional expertise are not seen as influential on design outcomes. In contrast, theories from other design disciplines define aspects of a designer's experience outside of their professional self as central to design practice. This dissertation seeks to reconcile these two paradigms by exploring whether `designer context' factors, that are often not discussed in engineering design but are found in other design fields (i.e. - organizational culture, gender, race) can impact design outcomes via method selection and implementation. Results from practitioner interviews on designer context and prototyping methods, as well as an empirical study of a novel design method, suggest that a broad range of designer context factors can influence design method selection and implementation, ultimately impacting the efficiency and efficacy of a design process. Therefore, if engineering designers were to consider their holistic designer context and its influence on their work, as occurs in other design fields, better engineering outcomes could be achieved. An exploratory study consisting of qualitative interviews formalized designer context and illustrated how these contextual factors impacted methods used by practitioners in the medical device industry. This study provides an initial foundation of designer context factors for exploration in future research and practice. These factors were categorized into the Design Environment, or the external factors surrounding a designer when they are designing, and the Designer's Self, or the internal factors related to a designer. Interviews with design practitioners from small-to-medium sized enterprises in Rwanda and Kenya revealed specific resource constraints impacting the implementation of prototyping methods. Many of the identified constraints were related to the practitioners’ context. Limited access to quality materials or fabricators, often due to difficulties navigating a decentralized market, added time and cost to the process. Practitioners reported trying to develop simple, functional, and physical prototypes with increasing fidelity through a highly iterative process. However, these constraints negatively impacted the chosen prototyping method, suggesting that alternative methods could be beneficial. In an empirical study, our team proposed and implemented a new method for considering multiple stakeholder preferences, the Stakeholder Agreement Metric (SAM) framework, to support the design of a hand tool to reduce injuries for informal electronic-waste (e-waste) recyclers in rural Thailand. This method was compared to the Analytical Hierarchy Process (AHP), an existing method that supports similar decisions. Results showed that the SAM framework outperformed AHP in this informal setting due to the failed completion of AHP by participants. The study highlights how designer context not only influenced the implementation of design methods but also their development. This dissertation expands the boundaries of what factors should be considered influential on design processes and their outcomes. Across all three studies, designer context was shown to influence method selection and implementation. The findings suggest that contextual factors affect design methods in practice and should be included in future research to enable the selection and implementation of more suitable and effective design methods.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163100/1/suzchou_1.pd

Factors Impacting Habitable Volume Requirements: Results from the 2011 Habitable Volume Workshop

This report documents the results of the Habitable Volume Workshop held April 18-21, 2011 in Houston, TX at the Center for Advanced Space Studies-Universities Space Research Association. The workshop was convened by NASA to examine the factors that feed into understanding minimum habitable volume requirements for long duration space missions. While there have been confinement studies and analogs that have provided the basis for the guidance found in current habitability standards, determining the adequacy of the volume for future long duration exploration missions is a more complicated endeavor. It was determined that an improved understanding of the relationship between behavioral and psychosocial stressors, available habitable and net habitable volume, and interior layouts was needed to judge the adequacy of long duration habitat designs. The workshop brought together a multi-disciplinary group of experts from the medical and behavioral sciences, spaceflight, human habitability disciplines and design professionals. These subject matter experts identified the most salient design-related stressors anticipated for a long duration exploration mission. The selected stressors were based on scientific evidence, as well as personal experiences from spaceflight and analogs. They were organized into eight major categories: allocation of space; workspace; general and individual control of environment; sensory deprivation; social monotony; crew composition; physical and medical issues; and contingency readiness. Mitigation strategies for the identified stressors and their subsequent impact to habitat design were identified. Recommendations for future research to address the stressors and mitigating design impacts are presented

High-Accuracy Facial Depth Models derived from 3D Synthetic Data

In this paper, we explore how synthetically generated 3D face models can be used to construct a high accuracy ground truth for depth. This allows us to train the Convolutional Neural Networks (CNN) to solve facial depth estimation problems. These models provide sophisticated controls over image variations including pose, illumination, facial expressions and camera position. 2D training samples can be rendered from these models, typically in RGB format, together with depth information. Using synthetic facial animations, a dynamic facial expression or facial action data can be rendered for a sequence of image frames together with ground truth depth and additional metadata such as head pose, light direction, etc. The synthetic data is used to train a CNN based facial depth estimation system which is validated on both synthetic and real images. Potential fields of application include 3D reconstruction, driver monitoring systems, robotic vision systems, and advanced scene understanding