Human–Robot Team Interaction Through Wearable Haptics for Cooperative Manipulation

Musić, Selma; Salvietti, Gionata; Dohmann, Pablo Budde gen.; Chinello, Francesco; Prattichizzo, Domenico; Hirche, Sandra

doi:10.1109/toh.2019.2921565

Cited by 23 publications

(15 citation statements)

References 39 publications

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“…A similar system is deployed in [10] and [11] to teleoperate different types of mobile robots. A multiple robot team interaction is developed in [12] using wearable haptic devices. In [13] and [14] robot arm interaction is used off-line for path planning and task learning, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

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Advanced teleoperation and control system for industrial robots based on augmented virtuality and haptic feedback

González

Solanes

Muñoz

et al. 2021

Journal of Manufacturing Systems

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There are some industrial tasks that are still mainly performed manually by human workers due to their complexity, which is the case of surface treatment operations (such as sanding, deburring, finishing, grinding, polishing, etc.) used to repair defects. This work develops an advanced teleoperation and control system for industrial robots in order to assist the human operator to perform the mentioned tasks. On the one hand, the controlled robotic system provides strength and accuracy, holding the tool, keeping the right tool orientation and guaranteeing a smooth approach to the workpiece. On the other hand, the advanced teleoperation provides security and comfort to the user when performing the task. In particular, the proposed teleoperation uses augmented virtuality (i.e., a virtual world that includes non-modeled real-world data) and haptic feedback to provide the user an immersive virtual experience when remotely teleoperating the tool of the robot system to treat arbitrary regions of the workpiece surface. The method is illustrated with a car body surface treatment operation, although it can be easily extended to other surface treatment applications or even to other industrial tasks where the human operator may benefit from robotic assistance. The effectiveness of the proposed approach is shown with several experiments using a 6R robotic arm.

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Section: Literature Reviewmentioning

confidence: 99%

“…In order to use the SMC detailed in Section 2.4 to satisfy the approach con-straint in (12), the following second-order dynamical system (8) is considered:…”

Section: Level 2: Approach Controlmentioning

confidence: 99%

Advanced teleoperation and control system for industrial robots based on augmented virtuality and haptic feedback

González

Solanes

Muñoz

et al. 2021

Journal of Manufacturing Systems

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show abstract

“…This body of research has contributed advancements in tele-presence [23,39,17,40], tele-robotics [59,66], tele-operation [33,52,72,54,31], and tele-surgery [64,73,15]. This research has focused on identifying suitable interfaces and improving their usability [42,50,70,68,58,22,82,32], as well as proposing novel control architectures for these interfaces [12,18,45,44]. Chen et al [10] categorize existing research according to the factors that affect remote control of robots.…”

Section: Related Workmentioning

confidence: 99%

On Multi-Human Multi-Robot Remote Interaction: A Study of Transparency, Inter-Human Communication, and Information Loss in Remote Interaction

Patel¹,

Prajankya²,

Pinciroli³

2021

Preprint

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In this paper, we investigate how to design an effective interface for remote multi-human multi-robot interaction. While significant research exists on interfaces for individual human operators, little research exists for the multihuman case. Yet, this is a critical problem to solve to make complex, largescale missions achievable in which direct human involvement is impossible or undesirable, and robot swarms act as a semi-autonomous agents. This paper's contribution is twofold. The first contribution is an exploration of the design space of computer-based interfaces for multi-human multi-robot operations. In particular, we focus on information transparency and on the factors that affect inter-human communication in ideal conditions, i.e., without communication issues. Our second contribution concerns the same problem, but considering increasing degrees of information loss, defined as intermittent reception of data with noticeable gaps between individual receipts. We derived a set of design recommendations based on two user studies involving 48 participants.

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“…And the robustness of the available mathematical models in dynamic task is low; so does the efficiency of adjustment of short run production [4,5]. Hence, combining the intelligence of cobot with that of human by introducing human into the manipulating cell [6,7], which digitizes the manipulation experience of human via human-cyber physical systems and other methods, has attracted the attention of scholars again [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

An intelligent manufacturing cell based on human–robot collaboration of frequent task learning for flexible manufacturing

Zhang

Wang

et al. 2022

Int J Adv Manuf Technol

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The manufacturing industry was moving towards the trend of short run production and personalized customization. That results in the challenge of the efficiency of task adjustment and the complexity of tasks for robots. Thus, this paper developed the intelligent manufacturing cell based on human-robot collaboration(HRC-IMC), combining the intelligence of cobot with that of human. And the intelligent manufacturing cell was composed with the modules of imitating learning, human-robot safety planning, task planning and visual inferring. Moreover, all modules were designed to provide a set of systematic and effective method which can improve the efficiency of task planning and new task learning. The experimental results indicated that the the efficiency of task adjustment of HRC-IMC can be increased 42.8 % than that of Moveit. All in all, this study is of great significance for improving the efficiency of new task planning of cobots by digitizing the manipulation experience of human.

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Human–Robot Team Interaction Through Wearable Haptics for Cooperative Manipulation

Cited by 23 publications

References 39 publications

Advanced teleoperation and control system for industrial robots based on augmented virtuality and haptic feedback

Advanced teleoperation and control system for industrial robots based on augmented virtuality and haptic feedback

On Multi-Human Multi-Robot Remote Interaction: A Study of Transparency, Inter-Human Communication, and Information Loss in Remote Interaction

An intelligent manufacturing cell based on human–robot collaboration of frequent task learning for flexible manufacturing

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