Markerless Human–Manipulator Interface Using Leap Motion With Interval Kalman Filter and Improved Particle Filter

Du, Guanglong; Zhang, Ping; Liu, Xin

doi:10.1109/tii.2016.2526674

Cited by 87 publications

(54 citation statements)

References 31 publications

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“…Employing an information fusion process would be one effective way of further improving the system robustness. Recent studies have shown that Kalman filters and particle filters greatly contribute to human hand tracking by compensating for tracking error and sensor noise [52][53][54]. The current picking system uses only one robotic-hand-mounted camera.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Depth Image–Based Deep Learning of Grasp Planning for Textureless Planar-Faced Objects in Vision-Guided Robotic Bin-Picking

Jiang

Ishihara

Sugiyama

et al. 2020

Sensors

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Bin-picking of small parcels and other textureless planar-faced objects is a common task at warehouses. A general color image–based vision-guided robot picking system requires feature extraction and goal image preparation of various objects. However, feature extraction for goal image matching is difficult for textureless objects. Further, prior preparation of huge numbers of goal images is impractical at a warehouse. In this paper, we propose a novel depth image–based vision-guided robot bin-picking system for textureless planar-faced objects. Our method uses a deep convolutional neural network (DCNN) model that is trained on 15,000 annotated depth images synthetically generated in a physics simulator to directly predict grasp points without object segmentation. Unlike previous studies that predicted grasp points for a robot suction hand with only one vacuum cup, our DCNN also predicts optimal grasp patterns for a hand with two vacuum cups (left cup on, right cup on, or both cups on). Further, we propose a surface feature descriptor to extract surface features (center position and normal) and refine the predicted grasp point position, removing the need for texture features for vision-guided robot control and sim-to-real modification for DCNN model training. Experimental results demonstrate the efficiency of our system, namely that a robot with 7 degrees of freedom can pick randomly posed textureless boxes in a cluttered environment with a 97.5% success rate at speeds exceeding 1000 pieces per hour.

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Section: Limitations and Future Workmentioning

confidence: 99%

Depth Image–Based Deep Learning of Grasp Planning for Textureless Planar-Faced Objects in Vision-Guided Robotic Bin-Picking

Jiang

Ishihara

Sugiyama

et al. 2020

Sensors

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“…For example, recently, wearable devices have proved to be a valid interface between humans and hosts [13]. Systems using commercial sensors [14], transparent solutions based on super senses [15], and high-tech mechanisms based on, for example, leap motion [16] have been proposed. From a theoretical point of view, other ideas such as people-oriented interfaces [17] have been also reported.…”

Section: State Of the Artmentioning

confidence: 99%

People-as-a-Service Dilemma: Humanizing Computing Solutions in High-Efficiency Applications

Bordel¹,

Hernández²

2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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Next-generation computing solutions, such as cyber-physical systems or Industry 4.0, are focused on increasing efficiency in process execution as much as possible. Removing unproductive delays or keeping infrastructures operating at their total capacity are typical objectives in these future systems. Decoupling infrastructure providers and service providers using Anything-as-a-Service (XaaS) paradigms is one of the most common approaches to address this challenge. However, many real scenarios not only include machines or controllers but also people and workers. In this case, deploying process execution algorithms and XaaS solutions degenerates in a People-as-a-Service scenario, which poses a critical dilemma: Can highly efficient production scenarios guarantee people’s wellbeing? In this paper, we address this problem and propose a new process execution algorithm based on a novel understanding of efficiency. In this case, a humanized efficiency definition combining traditional efficiency ratios and wellbeing indicators is used to allocate tasks and assign them to different existing workers. In order to evaluate the proposed solution, a simulation scenario including social and physical elements was built. Using this scenario, a first experimental validation was carried out.

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“…Hence, this demonstrates that the suggested method (AMSSDR) is capable of switching and resampling algorithms in various physical memory requirements. The authors of this paper wish to extend this work gradually by implementing their suggested method in a number of different promising applications (for instance, in medical software [66] or real-time locator systems [67]. …”

Section: Conclusion and Future Implementationsmentioning

confidence: 99%

Adaptive memory-based single distribution resampling for particle filter

et al. 2017

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IntroductionOver the past two decades, particle filtering has emerged as a procedure for sequential signal processing (Refs. [1][2][3][4] presents the review). Particle filter has also become popular because it is capable of processing observations signified by nonlinear state-space models. In such models, the noises can be non-Gaussian. Several fields have adopted this methodology including: finance [5][6][7][8], wireless communications [9][10][11][12], geophysical systems [13][14][15][16][17], navigation and tracking [18][19][20], control [21][22][23][24][25], and robotics [26][27][28][29][30][31]. Generally, this methodology can approximate state density p(x k ) using a range of random particles that have related nonnegative weights: AbstractThe restrictions that are related to using single distribution resampling for some specific computing devices' memory gives developers several difficulties as a result of the increased effort and time needed for the development of a particle filter. Thus, one needs a new sequential resampling algorithm that is flexible enough to allow it to be used with various computing devices. Therefore, this paper formulated a new single distribution resampling called the adaptive memory size-based single distribution resampling (AMSSDR). This resampling method integrates traditional variation resampling and traditional resampling in one architecture. The algorithm changes the resampling algorithm using the memory in a computing device. This helps the developer formulate a particle filter without over considering the computing devices' memory utilisation during the development of different particle filters. At the start of the operational process, it uses the AMSSDR selector to choose an appropriate resampling algorithm (for example, rounding copy resampling or systematic resampling), based on the current computing devices' physical memory. If one chooses systematic resampling, the resampling will sample every particle for every cycle. On the other hand, if it chooses the rounding copy resampling, the resampling will sample more than one of each cycle's particle. This illustrates that the method (AMSSDR) being proposed is capable of switching resampling algorithms based on various physical memory requirements. The aim of the authors is to extend this research in the future by applying their proposed method in various emerging applications such as real-time locator systems or medical applications.

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Markerless Human–Manipulator Interface Using Leap Motion With Interval Kalman Filter and Improved Particle Filter

Cited by 87 publications

References 31 publications

Depth Image–Based Deep Learning of Grasp Planning for Textureless Planar-Faced Objects in Vision-Guided Robotic Bin-Picking

Depth Image–Based Deep Learning of Grasp Planning for Textureless Planar-Faced Objects in Vision-Guided Robotic Bin-Picking

People-as-a-Service Dilemma: Humanizing Computing Solutions in High-Efficiency Applications

Adaptive memory-based single distribution resampling for particle filter

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