Hydrogen Fuel Cell Gasket Handling and Sorting With Machine Vision Integrated Dual Arm Robot

Recently demonstrated robotic assembling technologies for fuel cell stacks used fuel cell components manually pre-arranged in stacks (presenters), all oriented in the same position. Identifying the original orientation of fuel cell components and loading them in stacks for a subsequent automated assembly process is a difficult, repetitive work cycle which if done manually, deceives the advantages offered by automated fabrication technologies of fuel cell components and by robotic assembly processes. We present an innovative robotic technology which enables the integration of automated fabrication processes of fuel cell components with robotic assembly of fuel cell stacks into a fully automated fuel cell manufacturing line. This task, which has not been addressed in the past uses a Yaskawa Motoman SDA5F dual arm robot with integrated machine vision system. The process is used to identify and grasp randomly placed, slightly asymmetric fuel cell components having a total alpha-plus-beta symmetry angle of 720o, to reorient them all in the same position and stack them in presenters for a subsequent robotic assembly process. The dual arm robot technology is selected for increased productivity and ease of gasket handling during reorientation. The initial position and orientation of the gaskets is identified by image analysis using a Cognex machine vision system with fixed camera. The process was demonstrated as part of a larger endeavor of bringing to readiness advanced manufacturing technologies for alternative energy systems, and responds the high priority needs identified by the U.S. Department of Energy for fuel cells manufacturing research and development

DEVELOPMENT AND ASSESSMENT OF ADVANCED ASSISTIVE ROBOTIC MANIPULATORS USER INTERFACES

Text BoxAssistive Robotic Manipulators (ARM) have shown improvement in self-care and increased independence among people with severe upper extremity disabilities. With an ARM mounted on the side of an electric powered wheelchair, an ARM may provide manipulation assistance, such as picking up object, eating, drinking, dressing, reaching out, or opening doors. However, existing assessment tools are inconsistent between studies, time consuming, and unclear in clinical effectiveness. Therefore, in this research, we have developed an ADL task board evaluation tool that provides standardized, efficient, and reliable assessment of ARM performance. Among powered wheelchair users and able-bodied controls using two commercial ARM user interfaces – joystick and keypad, we found that there were statistical differences between both user interface performances, but no statistical difference was found in the cognitive loading. The ADL task board demonstrated highly correlated performance with an existing functional assessment tool, Wolf Motor Function Test. Through this study, we have also identified barriers and limits in current commercial user interfaces and developed smartphone and assistive sliding-autonomy user interfaces that yields improved performance. Testing results from our smartphone manual interface revealed statistically faster performance. The assistive sliding-autonomy interface helped seamlessly correct the error seen with autonomous functions. The ADL task performance evaluation tool may help clinicians and researchers better access ARM user interfaces and evaluated the efficacy of customized user interfaces to improve performance. The smartphone manual interface demonstrated improved performance and the sliding-autonomy framework showed enhanced success with tasks without recalculating path planning and recognition

Robots that Learn and Plan — Unifying Robot Learning and Motion Planning for Generalized Task Execution

Robots have the potential to assist people with a variety of everyday tasks, but to achieve that potential robots require software capable of planning and executing motions in cluttered environments. To address this, over the past few decades, roboticists have developed numerous methods for planning motions to avoid obstacles with increasingly stronger guarantees, from probabilistic completeness to asymptotic optimality. Some of these methods have even considered the types of constraints that must be satisfied to perform useful tasks, but these constraints must generally be manually specified. In recent years, there has been a resurgence of methods for automatic learning of tasks from human-provided demonstrations. Unfortunately, these two fields, task learning and motion planning, have evolved largely separate from one another, and the learned models are often not usable by motion planners. In this thesis, we aim to bridge the gap between robot task learning and motion planning by employing a learned task model that can subsequently be leveraged by an asymptotically-optimal motion planner to autonomously execute the task. First, we show that application of a motion planner enables task performance while avoiding novel obstacles and extend this to dynamic environments by replanning at reactive rates. Second, we generalize the method to accommodate time-invariant model parameters, allowing more information to be gleaned from the demonstrations. Third, we describe a more principled approach to temporal registration for such learning methods that mirrors the ultimate integration with a motion planner and often reduces the number of demonstrations required. Finally, we extend this framework to the domain of mobile manipulation. We empirically evaluate each of these contributions on multiple household tasks using the Aldebaran Nao, Rethink Robotics Baxter, and Fetch mobile manipulator robots to show that these approaches improve task execution success rates and reduce the amount of human-provided information required.Doctor of Philosoph

Robot manipulation in human environments

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 211-228).Human environments present special challenges for robot manipulation. They are often dynamic, difficult to predict, and beyond the control of a robot engineer. Fortunately, many characteristics of these settings can be used to a robot's advantage. Human environments are typically populated by people, and a robot can rely on the guidance and assistance of a human collaborator. Everyday objects exhibit common, task-relevant features that reduce the cognitive load required for the object's use. Many tasks can be achieved through the detection and control of these sparse perceptual features. And finally, a robot is more than a passive observer of the world. It can use its body to reduce its perceptual uncertainty about the world. In this thesis we present advances in robot manipulation that address the unique challenges of human environments. We describe the design of a humanoid robot named Domo, develop methods that allow Domo to assist a person in everyday tasks, and discuss general strategies for building robots that work alongside people in their homes and workplaces.by Aaron Ladd Edsinger.Ph.D

Research and Technology

Johnson Space Center (JSC) accomplishments in new and advanced concepts during 1989 are highlighted. This year, reports are grouped in sections, Medical Science, Solar System Sciences, Space Transportation Technology, and Space Systems Technology. Summary sections describing the role of JSC in each program are followed by descriptions of significant tasks. Descriptions are suitable for external consumption, free of technical jargon, and illustrated to increase ease of comprehension

Haptics: Science, Technology, Applications

This open access book constitutes the proceedings of the 12th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2020, held in Leiden, The Netherlands, in September 2020. The 60 papers presented in this volume were carefully reviewed and selected from 111 submissions. The were organized in topical sections on haptic science, haptic technology, and haptic applications. This year's focus is on accessibility

Reasoning about Geometric Object Interactions in 3D for Manipulation Action Understanding

In order to efficiently interact with human users, intelligent agents and autonomous systems need the ability of interpreting human actions. We focus our attention on manipulation actions, wherein an agent typically grasps an object and moves it, possibly altering its physical state. Agent-object and object-object interactions during a manipulation are a defining part of the performed action itself. In this thesis, we focus on extracting semantic cues, derived from geometric object interactions in 3D space during a manipulation, that are useful for action understanding at the cognitive level. First, we introduce a simple grounding model for the most common pairwise spatial relations between objects and investigate the descriptive power of their temporal evolution for action characterization. We propose a compact, abstract action descriptor that encodes the geometric object interactions during action execution, as captured by the spatial relation dynamics. Our experiments on a diverse dataset confirm both the validity and effectiveness of our spatial relation models and the discriminative power of our representation with respect to the underlying action semantics. Second, we model and detect lower level interactions, namely object contacts and separations, viewing them as topological scene changes within a dense motion estimation setting. In addition to improving motion estimation accuracy in the challenging case of motion boundaries induced by these events, our approach shows promising performance in the explicit detection and classification of the latter. Building upon dense motion estimation and using detected contact events as an attention mechanism, we propose a bottom-up pipeline for the guided segmentation and rigid motion extraction of manipulated objects. Finally, in addition to our methodological contributions, we introduce a new open-source software library for point cloud data processing, developed for the needs of this thesis, which aims at providing an easy to use, flexible, and efficient framework for the rapid development of performant software for a range of 3D perception tasks

Haptics: Science, Technology, Applications

This open access book constitutes the proceedings of the 12th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2020, held in Leiden, The Netherlands, in September 2020. The 60 papers presented in this volume were carefully reviewed and selected from 111 submissions. The were organized in topical sections on haptic science, haptic technology, and haptic applications. This year's focus is on accessibility