A proposed gesture set for the control of industrial collaborative robots

Barattini, Paolo; Morand, C.; Robertson, Neil

doi:10.1109/roman.2012.6343743

Cited by 39 publications

(24 citation statements)

References 12 publications

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“…While both approaches were accurate, the one based on Euclidean distance was associated with more limitations. Barattini et al (2012) developed a gesture set for control of industrial robots, based on distinguishability data (i.e., confusion matrix), as determined based on use of the Microsoft Kinect system and dynamic time warping (DTW) recognition algorithms. Others have also applied the Kinect capabilities for industrial robots (Lambrecht et al 2011) and for humanoid robots (Suay and Chernova 2011).…”

Section: -D Data Recognitionmentioning

confidence: 99%

“…Some progress has been made with regard to vision-based gestures in loud and cluttered settings, where user-borne devices such as microphones or gloves were not an option (Barattini et al 2012).…”

Section: Wearable Markersmentioning

confidence: 99%

“…Examples of gestural commands in these settings include "follow me", "go there", or "hand me that". There are also advancements in various industrial settings to control robotic assembly and maneuver tasks (Lambrecht et al 2011;Barattini et al 2012).…”

Section: Introductionmentioning

confidence: 99%

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Gesture-Based Controls for Robots: Overview and Implications for Use by Soldiers

Elliott¹,

Hill²,

Barnes³

2016

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Section: -D Data Recognitionmentioning

confidence: 99%

Section: Wearable Markersmentioning

confidence: 99%

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Gesture-Based Controls for Robots: Overview and Implications for Use by Soldiers

Elliott¹,

Hill²,

Barnes³

2016

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“…Gleeson et al [14] presented a gestural communication lexicon for human-robot collaboration in assembly tasks. Barattini et al [2] proposed a gesture set for the control of industrial collaborative robots. In service robotics, recognition of pointing and showing gestures for a domestic service robot was studied by Droeschel et al [10].…”

Section: Related Workmentioning

confidence: 99%

SmartWalker: An intelligent robotic walker

Shin

Rusakov

Meyer

2016

AIS

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Ensuring mobility of the elderly is an important task in our aging society. To this end, this paper presents SMART-WALKER, a high-tech extension of a regular walker that aims to navigate around its environment autonomously and assist its user intelligently. The walker is equipped with sensors and actuators and operates in two modes, autonomous and assistive. In the autonomous mode, the walker accepts gesture commands via its gesture-based interface and navigates around accordingly. The interface uses a k-nearest neighbors classifier with dynamic time warping to recognize gestures and the Viola and Jones face detector to locate the user. In the assistive mode, its automatic speed controller determines the optimal speed for the walker. The walker locates its user by detecting the user's legs using a laser range scanner and combines the information with other sensory data for the speed control. The walker was evaluated with residents of retirement homes in Zürich. The study aimed to understand the usefulness of robotic walker to the elderly, evaluate its potential to replace traditional walkers, and determine the appropriateness of the added functionality. The elderly found the SMARTWALKER's autonomy useful and exciting and the walker with the controller slightly more comfortable and easier to maneuver. They stated that the walker is too big and too heavy but liked it more than traditional walkers in the assistive mode.

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“…Natural-Language-instructed human-machine interaction is expected to enable an advanced manufacturing machine, such as a Computer Numerical Control machine or an industrial robot, to autonomously perform tasks such as rough/fine finishing [4] [5], assembly [2] [6] and packaging [7] [8] according to the end-user's NL instructions, which are given based on the user's judgement of the task progress and environmental situations. Compared with other input methods, including human hand force [9][10], hand gesture [11] [12], and body motions [13] [14][15] [16], the NL instruction method has two main advantages. First, NL instruction provides a natural, human-like, face-to-face communication manner.…”

Section: Introductionmentioning

confidence: 99%

Generating machine-executable plans from end-user's natural-language instructions

Liu

Zhang

2018

Knowledge-Based Systems

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Abstract-It is critical for advanced manufacturing machines to autonomously execute a task by following an end-user's natural language (NL) instructions. However, NL instructions are usually ambiguous and abstract so that the machines may misunderstand and incorrectly execute the task. To address this NL-based human-machine communication problem and enable the machines to appropriately execute tasks by following the end-user's NL instructions, we developed a Machine-Executable-Plan-Generation (exePlan) method. The exePlan method conducts task-centered semantic analysis to extract task-related information from ambiguous NL instructions. In addition, the method specifies machine execution parameters to generate a machine-executable plan by interpreting abstract NL instructions. To evaluate the exePlan method, an industrial robot Baxter was instructed by NL to perform three types of industrial tasks {"drill a hole", "clean a spot", "install a screw"}. The experiment results proved that the exePlan method was effective in generating machine-executable plans from the end-user's NL instructions. Such a method has the promise to endow a machine with the ability of NL-instructed task execution.Index Terms-Advanced manufacturing machine, machine-executable plan, natural language instruction, semantic analysis, task execution.

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A proposed gesture set for the control of industrial collaborative robots

Cited by 39 publications

References 12 publications

Gesture-Based Controls for Robots: Overview and Implications for Use by Soldiers

Gesture-Based Controls for Robots: Overview and Implications for Use by Soldiers

SmartWalker: An intelligent robotic walker

Generating machine-executable plans from end-user's natural-language instructions

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