Journal: IEEE Transactions on Automation Science and Engineering

Abbreviation

IEEE trans. autom. sci. eng.

Publisher

IEEE

Journal Volumes

ISSN

1545-5955
1558-3783

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Publications 1 - 10 of 21
  • A Dynamic Region-of-Interest Vision Tracking System Applied to the Real-Time Wing Kinematic Analysis of Tethered Drosophila
    Item type: Journal Article
    Graetzel, Chauncey F.; Nelson, Bradley J.; Fry, Steven N. (2010)
    IEEE Transactions on Automation Science and Engineering
  • Towards Nanotube Linear Servomotors
    Item type: Journal Article
    Dong, Lixin; Nelson, Bradley J.; Fukuda, Toshio; et al. (2006)
    IEEE Transactions on Automation Science and Engineering
  • Control of drug administration during Monitored Anesthesia Care
    Item type: Journal Article
    Caruso, Antonello L.G.; Bouillon, Thomas W.; Schumacher, Peter M.; et al. (2009)
    IEEE Transactions on Automation Science and Engineering
  • Artificial Cognition in Production Systems
    Item type: Review Article
    Bannat, Alexander; Bautze, Thibault; Beetz, Michael; et al. (2011)
    IEEE Transactions on Automation Science and Engineering
  • Cloud-Based Collaborative 3D Mapping in Real-Time With Low-Cost Robots
    Item type: Journal Article
    Mohanarajah, Gajamohan; Usenko, Vladyslav; Singh, Mayank; et al. (2015)
    IEEE Transactions on Automation Science and Engineering
  • Dry Coupled Ultrasonic Non-Destructive Evaluation Using an Over-Actuated Unmanned Aerial Vehicle
    Item type: Journal Article
    Watson, Robert; Kamel, Mina; Zhang, Dayi; et al. (2021)
    IEEE Transactions on Automation Science and Engineering
    Unmanned aerial vehicles (UAVs) are seeing increasing adoption to automated remote and in situ inspection of industrial assets, removing the need for hazardous manned access. Aerial manipulator architectures supporting pose-decoupled exertion of force and torque would further enable UAV deployment of contact-based transducers for sub-surface structural health assessment. Herein, for the first time, we introduce an over-actuated multirotor deploying a dry-coupled ultrasonic wheel probe as a novel means of wall thickness mapping. Using bi-axial tilting propellers in a unique tricopter layout, this system performs direct thrust vectoring for efficient omnidirectional flight and application of interaction forces. In laboratory testing, we demonstrate stable and repeatable probe deployment in a variety of representative asset inspection operations. We obtain a mean absolute error (MAE) in measured thickness of under 0.10 mm when measuring an aluminum sample with varying wall thickness. This is maintained over repeated exit and reentry of surface contact and when the sample is mounted vertically or on the underside of a 45 degrees overhang. Furthermore, when rolling the probe dynamically across the sample surface in an area scanning modality, an MAE in wall thickness below 0.28 mm is recorded. Multi-modal operational confidence bounds of the system are thereby quantitatively defined.
  • Cooperative Product Agents to Improve Manufacturing System Flexibility: A Model-Based Decision Framework
    Item type: Journal Article
    Kovalenko, Ilya; Balta, Efe C.; Tillbury, Dawn M.; et al. (2023)
    IEEE Transactions on Automation Science and Engineering
    Due to the advancements in manufacturing system technology and the ever-increasing demand for personalized products, there is a growing desire to improve the flexibility of manufacturing systems. Multi-agent control is one strategy that has been proposed to address this challenge. The multi-agent control strategy relies on the decision making and cooperation of a number of intelligent software agents to control and coordinate various components on the shop floor. One of the most important agents for this control strategy is the product agent, which is the decision maker for a single part in the manufacturing system. To improve the flexibility and adaptability of the product agent and its control strategy, this work proposes a direct and active cooperation framework for the product agent. The directly and actively cooperating product agent can identify and actively negotiate scheduling constraints with other agents in the system. A new modeling formalism, based on priced timed automata, and an optimization-based decision making strategy are proposed as part of the framework. Two simulation case studies showcase how direct and active cooperation can be used to improve the flexibility and performance of manufacturing systems.
  • Direct Motion Planning for Vision-Based Control
    Item type: Journal Article
    Pieters, Roel; Ye, Zhenyu; Jonker, Pieter; et al. (2014)
    IEEE Transactions on Automation Science and Engineering
  • Robust Electromagnetic Control of Microrobots Under Force and Localization Uncertainties
    Item type: Journal Article
    Marino, Hamal; Bergeles, Christos; Nelson, Bradley J. (2014)
    IEEE Transactions on Automation Science and Engineering
  • Integrating With Multimodal Information for Enhancing Robotic Grasping With Vision-Language Models
    Item type: Journal Article
    Zhao, Zhou; Zheng, Dongyuan; Chen, Yizi; et al. (2025)
    IEEE Transactions on Automation Science and Engineering
    As robots grow increasingly intelligent and utilize data from various sensors, relying solely on unimodal data sources is becoming inadequate for their operational needs. Consequently, integrating multimodal data has emerged as a critical area of focus. However, the effective combination of different data modalities poses a considerable challenge, especially in complex and dynamic settings where accurate object recognition and manipulation are essential. In this paper, we introduce a novel framework integrating with Multimodal Information for Grasping Synthesis with vision-language models (MIG) designed to improve robotic grasping capabilities. This framework incorporates visual data, textual information, and human-derived prior knowledge. We start by creating target object masks based on this prior knowledge, which are then used to segregate the target objects from their surroundings in the image. Subsequently, we employ language cues to refine the visual representations of these objects. Finally, our system executes precise grasping actions using visual and textual data synthesis, thus facilitating more effective and contextually aware robotic grasping. We carry out experiments using the OCID-VLG dataset. We observe that our methodology surpasses current state-of-the-art (SOTA) techniques, delivering improvements of 9.91% and 5.70% for top-1 and top-5 predictions in grasp accuracy. Moreover, when apply to the reconstructed Grasp-MultiObject dataset, our approach demonstrates even more substantial enhancements, achieving gains of 17.63% and 22.76% over SOTA methods for top-1 and top-5 predictions, respectively.
Publications 1 - 10 of 21