Robot teleoperation using a vision-based manipulation method

Du, Guanglong; Zhang, Ping; Yang, Liting; Su, Yan-Bin

doi:10.1109/icalip.2010.5685167

Cited by 14 publications

(3 citation statements)

References 4 publications

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“…Existing research on robot teleoperation using contactless gesture control could be classified into two main types and these are imitation of human movements [12,[14][15][16][17][18] and gesture recognition [19][20][21][22][23][24][25][26]. These researches have focused on technological development with a lack of application and ergonomic considerations; these could affect users' health and experience for practical long-term use.…”

Section: Introductionmentioning

confidence: 99%

The Design and Evaluation of an Ergonomic Contactless Gesture Control System for Industrial Robots

Tang

Webb

2018

Journal of Robotics

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In industrial human-robot collaboration, variability commonly exists in the operation environment and the components, which induces uncertainty and error that require frequent manual intervention for rectification. Conventional teach pendants can be physically demanding to use and require user training prior to operation. Thus, a more effective control interface is required. In this paper, the design and evaluation of a contactless gesture control system using Leap Motion is described. The design process involves the use of RULA human factor analysis tool. Separately, an exploratory usability test was conducted to compare three usability aspects between the developed gesture control system and an off-the-shelf conventional touchscreen teach pendant. This paper focuses on the user-centred design methodology of the gesture control system. The novelties of this research are the use of human factor analysis tools in the human-centred development process, as well as the gesture control design that enable users to control industrial robot's motion by its joints and tool centre point position. The system has potential to use as an input device for industrial robot control in a human-robot collaboration scene. The developed gesture control system was targeting applications in system recovery and error correction in flexible manufacturing environment shared between humans and robots. The system allows operators to control an industrial robot without the requirement of significant training.

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Section: Introductionmentioning

confidence: 99%

The Design and Evaluation of an Ergonomic Contactless Gesture Control System for Industrial Robots

Tang

Webb

2018

Journal of Robotics

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“…A number of researches have been conducted on robot teleoperation using contactless human tracking. These control techniques are mainly divided into two types which include the imitation of human natural movement gestures (Du et al, 2010;Kruse et al, 2015;Nguyen and Perdereau, 2011;Stanton et al, 2012) and gesture recognition (Kornuta and Zielinski, 2011;Parkale, 2012;Yanik et al, 2012;Wan et al, 2012). have developed a method of dual robot manipulator interface using markerless Kinect-based three-dimensional (3D) hand tracking of the human operator.…”

Section: Introductionmentioning

confidence: 99%

The integration of contactless static pose recognition and dynamic hand motion tracking control system for industrial human and robot collaboration

Tang

Asif

Webb

2015

Industrial Robot: An International Journal

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Purpose – The purpose of this paper is to describe the integration of a gesture control system for industrial collaborative robot. Human and robot collaborative systems can be a viable manufacturing solution, but efficient control and communication are required for operations to be carried out effectively and safely. Design/methodology/approach – The integrated system consists of facial recognition, static pose recognition and dynamic hand motion tracking. Each sub-system has been tested in isolation before integration and demonstration of a sample task. Findings – It is demonstrated that the combination of multiple gesture control methods can increase its potential applications for industrial robots. Originality/value – The novelty of the system is the combination of a dual gesture controls method which allows operators to command an industrial robot by posing hand gestures as well as control the robot motion by moving one of their hands in front of the sensor. A facial verification system is integrated to improve the robustness, reliability and security of the control system which also allows assignment of permission levels to different users.

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“…Because vision-based techniques are noncontacting, they seldom hinder the hand-arm motion. Vision-based methods often use physical markers which are placed on the anatomical body part [ 6 – 8 ]. There are a lot of applications [ 6 , 9 , 10 ] using this marker-based tracking method.…”

Section: Introductionmentioning

confidence: 99%

Human-Manipulator Interface Using Particle Filter

Zhang

Wang

2014

The Scientific World Journal

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This paper utilizes a human-robot interface system which incorporates particle filter (PF) and adaptive multispace transformation (AMT) to track the pose of the human hand for controlling the robot manipulator. This system employs a 3D camera (Kinect) to determine the orientation and the translation of the human hand. We use Camshift algorithm to track the hand. PF is used to estimate the translation of the human hand. Although a PF is used for estimating the translation, the translation error increases in a short period of time when the sensors fail to detect the hand motion. Therefore, a methodology to correct the translation error is required. What is more, to be subject to the perceptive limitations and the motor limitations, human operator is hard to carry out the high precision operation. This paper proposes an adaptive multispace transformation (AMT) method to assist the operator to improve the accuracy and reliability in determining the pose of the robot. The human-robot interface system was experimentally tested in a lab environment, and the results indicate that such a system can successfully control a robot manipulator.

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Robot teleoperation using a vision-based manipulation method

Cited by 14 publications

References 4 publications

The Design and Evaluation of an Ergonomic Contactless Gesture Control System for Industrial Robots

The Design and Evaluation of an Ergonomic Contactless Gesture Control System for Industrial Robots

The integration of contactless static pose recognition and dynamic hand motion tracking control system for industrial human and robot collaboration

Human-Manipulator Interface Using Particle Filter

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