Category-Association Based Similarity Matching for Novel Object Pick-and-Place Task

Chen, Hao; Kiyokawa, Takuya; Wan, Weiwei; Harada, Kensuke

doi:10.1109/lra.2022.3145956

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…The advanced technologies used to enhance the availability of the robotic sorter are automated disassembling to change the waste of assembly products into a recyclable state [37], [80], [208], [209], [210], [211], [212], [213], autonomous mobile robots capable of manipulating waste items on the ground [214], [215] equipped with a function to handle objects of unknown shapes [216], [218], and Internet of Things that enables communication between their mobile devices [219],…”

Section: B Challenges To Developing Global Recyclingmentioning

confidence: 99%

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

Kiyokawa

Takamatsu

Koyanaka

2024

IEEE Trans. Automat. Sci. Eng.

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To achieve recycling of mixed industrial waste toward an advanced sustainable society, waste sorting automation through robots is crucial and urgent. For this purpose, a robot is required to recognize the category, shape, pose, and condition of different waste items and manipulate them according to the category to be sorted. This survey considers three potential difficulties in the sorting automation: 1) End-effector: to robustly grasp and manipulate different waste items with dirt and deformations; 2) Sensor: to recognize the category, shape, and pose of existing objects to be manipulated and the wet and dirty conditions of their surfaces; and 3) Planner: to generate feasible and efficient sequences and trajectories. This survey includes 76 references to studies related to automatic waste sorting and 159 references to worldwide waste recycling attempts. This pioneering investigation reveals the possibility and limitations of conventional systems; thus, providing insights on open issues and potential technologies to achieve a robot-incorporated sorter for the chaotic mixed waste is one of its contributions. This paper further presents a system design policy for readers and discusses future advanced sorters, thereby contributing to the field of robotics and automation.Note to Practitioners-Most automated sorting systems operate for limited target waste items. This study is motivated by the automation of mixed industrial waste treatment facilities using advanced robotic sorters. Emerging advances and increasing functionalities of robot system components will widen system applicability and increase use cases in the chaotic mixed industrial waste domain. This paper surveys the research conducted to date, discusses open issues and potential approaches, and presents user guides that provide practitioners with a system design policy. The user guides created according to the strengths and weaknesses of each system configuration provide future researchers and developers with a useful a priori design policy that has been thus far validated on efficiency, quality, productivity, and reliability. A question-and-answer style guide Manuscript

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Section: B Challenges To Developing Global Recyclingmentioning

confidence: 99%

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

Kiyokawa

Takamatsu

Koyanaka

2024

IEEE Trans. Automat. Sci. Eng.

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“…The first characteristic is local perception. The aim of using local perception is that researchers consider parts in the picture that are near to each other for relevancy Chen et al (2022).…”

Section: Related Work Convolution Layersmentioning

confidence: 99%

Convolution Neural Network Based Human and Robot Collaboration for 3D Scene Perception

Alkali¹,

Ahmed²,

Ayuba³

et al. 2022

AJBAR

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The approach of algorithmic operation of the Convolutional Neural Network (ConvNet/CNN) is like that of Deep Learning. The algorithm receives input instruction as an image by assigning characteristics of learnable weights and preferences on all inputs (aspects/objects) to the image. These features enable scalable input processing and effective differentiation and assignment of input and output images. For pre-processing requirements, ConvNet uses significantly low attributes as compared to other algorithms. However, in a primitive approach, filters are hand-engineered having sufficient algorithmic training. The ConvNets possess other abilities to learn image filtering and its characteristics. In this paper, an analysis of robot visual reasoning (for pick and place) is conducted. This refers to reasoning the latent meaning of visual signals or indications for future robot actions from visual observations of an HRC scene. In this paper, projection matrix estimation is computed using 2D points to represent the figure plane and 3D points to represent scene detection for initiating pick and place operation. During simulation, equations are represented using a matrix format based on figure coordinates. The design also implements automatic calibration and accuracy for the robot workspace. In addition, vision-based management for the robot end effector is also conducted based on horizontal targets, and upright targets. However, the pick and the place are articulated in the experiment without visual control. In our results, analysis of joint tangential forces is computed for the three joints, Alink1, Blink2, and Clink3. The outcome of axial force variation of the three joints is conducted in their state of mobility. Finally, the result of torque acting on the three joints increases when the simulation time is increased to achieve optimum performance

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“…The Faster-R-CNN uses a VGG-net as a backbone model and is trained on the ImageNet classification dataset. The work ofChen et al (2022) applies a convolutional network called Panoptic FPN…”

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confidence: 99%

Bio-inspired circular latent spaces to estimate objects' rotations

Plebe,

Da Lio

2023

Front. Comput. Neurosci.

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This paper proposes a neural network model that estimates the rotation angle of unknown objects from RGB images using an approach inspired by biological neural circuits. The proposed model embeds the understanding of rotational transformations into its architecture, in a way inspired by how rotation is represented in the ellipsoid body of Drosophila. To effectively capture the cyclic nature of rotation, the network's latent space is structured in a circular manner. The rotation operator acts as a shift in the circular latent space's units, establishing a direct correspondence between shifts in the latent space and angular rotations of the object in the world space. Our model accurately estimates the difference in rotation between two views of an object, even for categories of objects that it has never seen before. In addition, our model outperforms three state-of-the-art convolutional networks commonly used as the backbone for vision-based models in robotics.

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Category-Association Based Similarity Matching for Novel Object Pick-and-Place Task

Cited by 7 publications

References 20 publications

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

Convolution Neural Network Based Human and Robot Collaboration for 3D Scene Perception

Bio-inspired circular latent spaces to estimate objects' rotations

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