Assembly Sequences Based on Multiple Criteria Against Products with Deformable Parts

Kiyokawa, Takuya; Takamatsu, Jun; Ogasawara, Tsukasa

doi:10.1109/icra48506.2021.9561925

Cited by 7 publications

(2 citation statements)

References 38 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 geometry of individual components was given in a table. Kiyokawa et al [7] proposed a method to generate easy-to-handle assembly sequences for robots by considering two tradeoff objectives about the insertion conditions and the degree of constraints among assembled parts.…”

Section: Related Workmentioning

confidence: 99%

Error Correction in Robotic Assembly Planning From Graphical Instruction Manuals

Wang,

Kiyokawa,

Sera

et al. 2023

IEEE Access

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To enhance the accuracy of robotic assembly planning by understanding the graphical instruction manual, this paper proposes a novel two-step error correction method. While constructing the Assembly Task Sequence Graph (ATSG) from the instruction manual, we performed an error correction focusing on the component, symbol, speech bubble, and model number included in an instruction manual. The component and symbol information were used to check the correctness of the manipulated components, the needed motion and tool used in a single step of the assembly task. The speech bubble information was used to remove the repeated drawn components on the instruction images. The model number was recognized to distinguish components that have different models but were classified into the same class in the object detection procedure. After constructing the ATSG, we additionally performed the error correction by checking the total number of components used for the assembly task. The effectiveness of the proposed method was verified by comparing the ATSG generated from four different error correction methods of five different chairs. The results show that the proposed method decreased the influence of wrong detection results to generate an ATSG with higher accuracy and generalized the previous ATSG for different types of chairs. Finally, we built an extended ATSG from the graphical instruction manual of a kid chair, and the chair assembly task was performed by a dual-arm robot.

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