A Markerless Human–Robot Interface Using Particle Filter and Kalman Filter for Dual Robots

Du, Guanglong; Zhang, Ping

doi:10.1109/tie.2014.2362095

Cited by 111 publications

(104 citation statements)

References 26 publications

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“…Until now, a variety of methods have been proposed for controlling a robot arm, such as the vision-based method [3], the sensor-based method [4][5][6], and the joystick-or master armbased method [7][8][9]. Due to the irreplaceable performance in unstructured and complicated subsea environments, the master arm-based method [10][11][12][13] is mostly used for controlling underwater manipulators, in which an operator controls a large slave arm by operating its miniature replica as a master arm.…”

Section: Development Of a Virtual Platform For Telepresence Control Omentioning

confidence: 99%

Development of a Virtual Platform for Telepresence Control of an Underwater Manipulator Mounted on a Submersible Vehicle

Zhang

et al. 2017

IEEE Trans. Ind. Electron.

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Abstract-This paper develops a virtual platform of an underwater manipulator mounted on a submersible vehicle via the three-dimensional simulator "Webots" for teleoperation through a replica master arm. The graphical, kinematic, and dynamic models of the manipulator refer to a master-slave servo hydraulic manipulator with seven functions, consisting of six degrees of freedom and a parallel gripper, while the "Jiaolong" deep-manned submersible vehicle, operating below the sea surface down to 7000 m, is chosen as the underwater manipulator carrier. This study uses the virtual platform for training an operator to telepresence control the virtual manipulator to complete basic tasks in subsea environments. When training the operator, one has to consider uncertain external disturbances and the visual impacts that stem from subsea environments. In order to demonstrate the feasibility and effectiveness of the virtual platform, one designs two typical underwater operational tasks: grasping a marine organism sample and reaching at a given position. This paper presents the comparative studies: 1) the performances demonstrated by remotely controlling the virtual manipulator and the real manipulator; 2) the operating performances delivered by three operators before and after training when using the platform.Index Terms-Telepresence control, training an operator, underwater manipulator, virtual platform.

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Section: Development Of a Virtual Platform For Telepresence Control Omentioning

confidence: 99%

Development of a Virtual Platform for Telepresence Control of an Underwater Manipulator Mounted on a Submersible Vehicle

Zhang

et al. 2017

IEEE Trans. Ind. Electron.

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“…In recent years, the Leap Motion Controller has been used for the remote control of robots [6], as a humanrobot-interface [7,8], for teleoperation [9], etc. In most of these work the Leap Motion Controller is always placed on a flat surface and the human operator carries out the task on the air, which is compensated using different kind of controllers to estimate the desired position and orientation.…”

Section: Related Workmentioning

confidence: 99%

“…Figure 2 shows the experimental setup for tracking the assembly of a ring-shaped object into a cylinder (fixed on a table). Typically [6][7][8], the Leap Motion Controller is placed on a flat surface facing upwards. However, to improve the Leap Motion Controller field of view, we placed it in a tripod above the cylinder's surface to reduce the occlusion of the hands by the cylinder, as shown in Fig.…”

Section: Human's Movements Acquisitionmentioning

confidence: 99%

Human-based framework for the assembly of elastic objects by a dual-arm robot

2017

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This paper proposes a new framework for planning assembly tasks involving elastic parts. As an example of these kind of assembly tasks, we deal with the insertion of ring-shaped objects into a cylinder by a dual-arm robot. The proposed framework is a combination of human movements to determine the overall assembly strategy and an optimizationbased motion planner to generate the robot trajectories. The motion of the human's hands, more specifically, the motion of the fingers gripping the object is captured by a Leap Motion Controller. Then, key points in the recorded trajectory of the position and orientation of the human's fingers are extracted. These points are used as partial goals in the optimization-based motion planner that generates the robot arms' trajectories which minimize the object's deformation. Through experimental results it was verified the validity of the extracted key points from the human's movements that enable the robot to successfully assemble ring-shaped elastic objects. We compared these results with the assembly done by purely repeating all of the human's hands movements.

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“…It is mainly used in the application such as robotics, visual surveillance (7), Human-to-Machine Interface (HMI) (8), video editing (9), motion control (10), (5), (11), (12), and activity recognition (6) to extract the target status. There are number of visual tracking systems to identify the targeted region from the frame of a given video.…”

Section: Introductionmentioning

confidence: 99%

Background Normalization and Texture Pattern-Based Video Segmentation for Visual Tracking

Rajagopal¹,

Sankaragomathi²

2017

Int. Res. J. Pharm.

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Visual tracking is the task of estimating the path of a target object in each frame of the video. Various tracking approaches are developed for tracking the position of the target. However, the tracker may fail to find the correct position, if the appearance of the target is similar to the background. This paper proposes a background normalization technique based on the textural pattern analysis to verify the matching of the features for the target region analysis. In this work, a novel model of background clustering is presented by using Multi-Weighted Chain Prediction (MWCP) algorithm for the uneven background. A Neighborhood Differential Binary Pattern (NDBP) is proposed to extract the texture for suppressing the shadow pixels in the image frame. From this equalized frame of the given video, the frame is split into several grids. From the grid format frame, the histogram features of the targeted frame are extracted, and each grid in that frame is classified. The Multi-Grid Weighted Classifier (MGWC) algorithm is used to find the matching of the grids to separate the background and foreground. This type of visual tracking system is robust over the sudden illumination changes and dynamic background. The experimental result proves that the proposed approach yields better precision, recall and F-score performance than the existing tracking approaches.

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A Markerless Human–Robot Interface Using Particle Filter and Kalman Filter for Dual Robots

Cited by 111 publications

References 26 publications

Development of a Virtual Platform for Telepresence Control of an Underwater Manipulator Mounted on a Submersible Vehicle

Development of a Virtual Platform for Telepresence Control of an Underwater Manipulator Mounted on a Submersible Vehicle

Human-based framework for the assembly of elastic objects by a dual-arm robot

Background Normalization and Texture Pattern-Based Video Segmentation for Visual Tracking

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