A Robotic Lift Assister: A Smart Companion for Heavy Payload Transport and Manipulation in Automotive Assembly

Chen, Yi; Wang, Weitian; Abdollahi, Zoleikha; Ze-bin, Wang; Schulte, Joerg; Krovi, Venkat; Jia, Yunyi

doi:10.1109/mra.2018.2815704

Cited by 18 publications

(3 citation statements)

References 18 publications

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“…Many robotic systems found applications in assembly lines and industrial handling, including processes such as transporting, palletizing, grasping, packaging, and picking. Chen et al [ 20 ] developed a smart companion robot for the automotive assembly industry to assist human workers in lifting, transporting, and manipulating heavy payloads such as batteries and car body parts. Unhelkar et al [ 21 ] designed a mobile robot system capable of operating on the moving floors of automotive assembly lines.…”

Section: Robotics In Industrial Applicationsmentioning

confidence: 99%

Modular Robots for Enabling Operations in Unstructured Extreme Environments

Sayed

Roberts

Donaldson

et al. 2022

Advanced Intelligent Systems

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Operations in extreme and hostile environments, such as offshore oil and gas production, nuclear decommissioning, nuclear facility maintenance, deep mining, space exploration, and subsea applications, require the execution of sophisticated tasks. In nuclear environments, robotic systems have advanced significantly over the past years but still suffer from task failures caused by informational and physical uncertainty of the highly unstructured nature of the environment and exasperated by the time constraints imposed by high radiation levels. Herein, a survey is presented of current robotic systems that can operate in such extreme environments and offer a novel approach to solving the challenges they impose, encapsulated by the mission statement of providing structure in unstructured environments and exemplified by a new self‐assembling modular robotic system, the Connect‐R.

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Section: Robotics In Industrial Applicationsmentioning

confidence: 99%

Modular Robots for Enabling Operations in Unstructured Extreme Environments

Sayed

Roberts

Donaldson

et al. 2022

Advanced Intelligent Systems

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show abstract

“…A hybrid framework including visual and force sensing is proposed for human-robot co-carrying tasks. In [39], a companion robot is designed to switch between the visual servoing and force servoing modes, different from our framework using visual and force sensing to estimate the human motion intention. In [40], [41], robots learn the teaching-learning-collaboration model and predict human motion through learning by demonstrations or using historical data for training, while our method requires neither of them.…”

Section: Introductionmentioning

confidence: 99%

Human-Robot Co-Carrying Using Visual and Force Sensing

et al. 2021

IEEE Trans. Ind. Electron.

127

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In this paper, we propose a hybrid framework using visual and force sensing for human-robot co-carrying tasks. Visual sensing is utilized to obtain human motion and an observer is designed for estimating control input of human, which generates robot's desired motion towards human's intended motion. An adaptive impedance-based control strategy is proposed for trajectory tracking with neural networks (NNs) used to compensate for uncertainties in robot's dynamics. Motion synchronization is achieved and this approach yields a stable and efficient interaction behavior between human and robot, decreases human control effort and avoids interference to human during the interaction. The proposed framework is validated by a co-carrying task in simulations and experiments.

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“…In the workspace and time‐sharing systems, operators and robots share the workspace and cooperate to perform common tasks. The robot has to be able to safely approach the operator and adapt its behavior to the worker's one to accomplish the task (e.g., Chen et al [12] and Wang et al [13]). Hand‐over applications, in which objects are exchanged between human and robot, are classified as workspace and time‐sharing tasks and are the subject of study of this work.…”

Section: Introductionmentioning

confidence: 99%

Vision‐based control architecture for human–robot hand‐over applications

Melchiorre

Scimmi

Mauro

et al. 2020

Asian Journal of Control

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Human-robot collaboration will be an essential part of the production processes in the factories of tomorrow. In this paper, a human-robot handover control strategy is presented. Human and robot can be both giver and receiver. A path-planning algorithm drives the robotic manipulator towards the hand of the human and permits to adapt the pose of the tool center point of the robot to the pose of the hand of the human worker. The movements of the operator are acquired with a multi 3D-sensors setup so to avoid any possible occlusion related to the presence of the robot or other dynamic obstacles. Estimation of the predicted position of the hand is performed to reduce the waiting time of the operator during the handover task. The hardware setup is described, and the results of experimental tests, conducted to verify the effectiveness of the control strategy, are presented and discussed.

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A Robotic Lift Assister: A Smart Companion for Heavy Payload Transport and Manipulation in Automotive Assembly

Cited by 18 publications

References 18 publications

Modular Robots for Enabling Operations in Unstructured Extreme Environments

Modular Robots for Enabling Operations in Unstructured Extreme Environments

Human-Robot Co-Carrying Using Visual and Force Sensing

Vision‐based control architecture for human–robot hand‐over applications

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