A Visual Servoing System for Interactive Human-Robot Object Transfer

Wang, Ying; Ewert, Daniel; Vossen, René; Jeschke, Sabina

doi:10.12720/joace.3.4.277-283

Cited by 10 publications

(7 citation statements)

References 17 publications

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“…This solution includes an RGB streaming data system using Gabor filters and the combination of feature representation, visual feedback dictionary, and sparse linear representation, enabling complex manipulation [94]. Another approach presents a visual servoing system for transferring objects between an operator and a robot [95]. An architecture for image visualization where human is detected and the item is classified and tracked to estimate their poses is proposed.…”

Section: ) Object Handlingmentioning

confidence: 99%

Human-Robot Interaction Review: Challenges and Solutions for Modern Industrial Environments

et al. 2021

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The demand for collaborative robots is growing in industrial environments due to their versatility and low prices. Thus, more collaborative solutions are emerging for industrial scenarios. However, implementing scenarios where robots work autonomously while synchronizing their operations in a safe industrial environment with shop-floor workers is not easy. To fill the gap existing in the safe implementation of industrial collaborative scenarios, this manuscript presents a review based on five identified challenges that gathers the primary vital aspects to bear in mind while developing applications for them. Thus, a four-level classification is proposed, which collects the identified challenges and the previous developments in the field of human-robot interaction. The five identified challenges pretends to be the missing enabling key for implementing industrial collaborative scenarios in modern industrial plants. Lastly, a discussion and conclusion are exposed to analyze the degree of development in the field and its potential growth.

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Section: ) Object Handlingmentioning

confidence: 99%

Human-Robot Interaction Review: Challenges and Solutions for Modern Industrial Environments

et al. 2021

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“…Towards robots that can interact with the human body, visual servoing has been applied in medical robotics contexts, such as tracking human body parts during surgery [56], [57] and commanding robotic surgical tools [58], [59], [60]. Visual servoing has also been used for collision avoidance with a human collaborator wearing motion capture equipment [61], to track human trajectories during human-robot collaboration [62], and for human-robot object transfers [63].…”

Section: B Servoingmentioning

confidence: 99%

Characterizing Multidimensional Capacitive Servoing for Physical Human-Robot Interaction

Erickson¹,

Clever²,

Gangaram³

et al. 2021

Preprint

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Towards the goal of robots performing robust and intelligent physical interactions with people, it is crucial that robots are able to accurately sense the human body, follow trajectories around the body, and track human motion. This study introduces a capacitive servoing control scheme that allows a robot to sense and navigate around human limbs during close physical interactions. Capacitive servoing leverages temporal measurements from a multi-electrode capacitive sensor array mounted on a robot's end effector to estimate the relative position and orientation (pose) of a nearby human limb. Capacitive servoing then uses these human pose estimates from a datadriven pose estimator within a feedback control loop in order to maneuver the robot's end effector around the surface of a human limb. We provide a design overview of capacitive sensors for human-robot interaction and then investigate the performance and generalization of capacitive servoing through an experiment with 12 human participants. The results indicate that multidimensional capacitive servoing enables a robot's end effector to move proximally or distally along human limbs while adapting to human pose. Using a cross-validation experiment, results further show that capacitive servoing generalizes well across people with different body size.

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“…In intelligent manufacturing systems, the development of detection technology with high intelligence and strong environmental adaptability is of great significance to improve production efficiency and enhance the flexibility of manufacturing systems and product quality [1,2]. Machine vision-based human-computer interaction coordination assembly technology uses human assembly gestures obtained from image analysis as input information for robot task planning [3] to realize an efficient and flexible coordinated assembly process [4]. The overall information, including the biological structure of the human hand image and associated hand assembly posture [5], is the basis for inferring the gesture intention.…”

Section: Introductionmentioning

confidence: 99%

Image offset density distribution model and recognition of hand knuckle

Gong

Qiao

2019

J Image Video Proc.

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The accurate description of hand posture plays an important role in the man-machine interaction involved in coordinated assembly. Knuckle image extraction and recognition are of great significance to refine and enrich hand-pose information. These are based on nonparametric density kernel estimation observation sets corresponding to unilateral and bilateral excursion of the hand knuckle gray image. In this paper, sets of pixel positions belonging to the upper-and middle-density intervals are used as two types of image targets. Random clustering and random field multi-classification target modeling are used to learn and estimate the two target distributions of the image. The discriminant field classification learning method is used to fuse the two kinds of target models. A comprehensive representation of the image offset features is obtained. Finally, the knuckle image sample set is used to train the model, and the adaptive threshold is used to identify the hand knuckle image. The results show that the proposed method is feasible.

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A Visual Servoing System for Interactive Human-Robot Object Transfer

Cited by 10 publications

References 17 publications

Human-Robot Interaction Review: Challenges and Solutions for Modern Industrial Environments

Human-Robot Interaction Review: Challenges and Solutions for Modern Industrial Environments

Characterizing Multidimensional Capacitive Servoing for Physical Human-Robot Interaction

Image offset density distribution model and recognition of hand knuckle

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