Better Teaming Through Visual Cues: How Projecting Imagery in a Workspace Can Improve Human-Robot Collaboration

Ganesan, R.; Rathore, Yash K.; Ross, Heather M.; Amor, Heni Ben

doi:10.1109/mra.2018.2815655

Cited by 53 publications

(25 citation statements)

References 21 publications

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“…projectors integrated into the smart environment could visualize the user-defined virtual borders directly on the ground without change of attention. Although there are already solutions in the industrial context [14,24], projectors are not yet widely distributed in current smart homes. Moreover, we currently evaluated our interaction method with a sin-gle mobile robot, which is valid for most households.…”

Section: Discussionmentioning

confidence: 99%

Interactive restriction of a mobile robot’s workspace in a smart home environment

Sprute

Tönnies

König

2019

AIS

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Virtual borders are employed to allow humans the interactive and flexible restriction of their mobile robots' workspaces in human-centered environments, e.g. to exclude privacy zones from the workspace or to indicate certain areas for working. They have been successfully specified in interaction processes using methods from human-robot interaction. However, these methods often lack an expressive feedback system, are restricted to robot's on-board interaction capabilities and require a direct line of sight between human and robot. This negatively affects the user experience and interaction time. Therefore, we investigate the effect of a smart environment on the teaching of virtual borders with the objective to enhance the perceptual and interaction capabilities of a robot. For this purpose, we propose a novel interaction method based on a laser pointer, that leverages a smart home environment in the interaction process. This interaction method comprises an architecture for a smart home environment designed to support the interaction process, the cooperation of human, robot and smart environment in the interaction process, a cooperative perception including stationary and mobile cameras to perceive laser spots and an algorithm to extract virtual borders from multiple camera observations. The results of an experimental evaluation support our hypotheses that our novel interaction method features a significantly shorter interaction time and a better user experience compared to an approach without support of a smart environment. Moreover, the interaction method does not negatively affect other user requirements concerning completeness and accuracy.

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Section: Discussionmentioning

confidence: 99%

Interactive restriction of a mobile robot’s workspace in a smart home environment

Sprute

Tönnies

König

2019

AIS

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“…x − y 2 (7) We utilise this functionality to calculate the collision vector to both HMIs in each moment of the trajectory execution and publish it to other ROS nodes. Each observed HMI is simplistically represented as a spherical collision object.…”

Section: Motion Planningmentioning

confidence: 99%

“…Better user awareness can be provided by utilising notification devices (humanmachine interfaces, HMIs) that allow to understand motion plans and status of the robot. In order to convey information, these systems may utilise the primary sensory modalities of a human: vision (monitors [6], light projectors [7,8], mixed reality devices [9][10][11]), hearing (sound alerts and speech notifications [2,12]), touch (tactile feedback devices [4,[13][14][15]). Touch modality represents a robust and direct way of transferring information to the user, making it suitable to convey information to workers in industrial environments, where visual and auditory modalities might be busy or blocked.…”

Section: Introductionmentioning

confidence: 99%

Intuitive Spatial Tactile Feedback for Better Awareness about Robot Trajectory during Human–Robot Collaboration

Grushko

Vysocký

Heczko

et al. 2021

Sensors

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In this work, we extend the previously proposed approach of improving mutual perception during human–robot collaboration by communicating the robot’s motion intentions and status to a human worker using hand-worn haptic feedback devices. The improvement is presented by introducing spatial tactile feedback, which provides the human worker with more intuitive information about the currently planned robot’s trajectory, given its spatial configuration. The enhanced feedback devices communicate directional information through activation of six tactors spatially organised to represent an orthogonal coordinate frame: the vibration activates on the side of the feedback device that is closest to the future path of the robot. To test the effectiveness of the improved human–machine interface, two user studies were prepared and conducted. The first study aimed to quantitatively evaluate the ease of differentiating activation of individual tactors of the notification devices. The second user study aimed to assess the overall usability of the enhanced notification mode for improving human awareness about the planned trajectory of a robot. The results of the first experiment allowed to identify the tactors for which vibration intensity was most often confused by users. The results of the second experiment showed that the enhanced notification system allowed the participants to complete the task faster and, in general, improved user awareness of the robot’s movement plan, according to both objective and subjective data. Moreover, the majority of participants (82%) favoured the improved notification system over its previous non-directional version and vision-based inspection.

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“…Recent works in this area include approaches using AR for robot design [35], calibration [42], and training [46]. Moreover, there are a number of approaches towards communicating robots' perspectives [20], intentions [2,9,10,12,15], and trajectories [14,31,36,54].…”

Section: Related Work 21 Ar For Hrimentioning

confidence: 99%

Exploring Mixed Reality Robot Communication Under Different types of Mental Workload

Tran

Mizuno²,

Grant

et al. 2020

Preprint

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This paper explores the tradeoffs between different types of mixed reality robotic communication under different levels of user workload. We present the results of a within-subjects experiment in which we systematically and jointly vary robot communication style alongside level and type of cognitive load, and measure subsequent impacts on accuracy, reaction time, and perceived workload and effectiveness. Our preliminary results suggest that although humans may not notice differences, the manner of load a user is under and the type of communication style used by a robot they interact with do in fact interact to determine their task effectiveness

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Better Teaming Through Visual Cues: How Projecting Imagery in a Workspace Can Improve Human-Robot Collaboration

Cited by 53 publications

References 21 publications

Interactive restriction of a mobile robot’s workspace in a smart home environment

Interactive restriction of a mobile robot’s workspace in a smart home environment

Intuitive Spatial Tactile Feedback for Better Awareness about Robot Trajectory during Human–Robot Collaboration

Exploring Mixed Reality Robot Communication Under Different types of Mental Workload

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