Many-objective-optimized semi-automated robotic disassembly sequences

Kiyokawa, Takuya; Harada, Kensuke; Wan, Weiwei; Ishikura, Tomoki; Miyaji, Naoya; Matsuda, Genichiro

doi:https://doi.org/10.1016/j.robot.2025.105301

Many-objective-optimized semi-automated robotic disassembly sequences

Kiyokawa, Takuya; Harada, Kensuke; Wan, Weiwei et al.

Robotics and Autonomous Systems, 2025, 197, 105301

アクセス数:5件（2026-02-15 21:48 集計）

固定URL: https://hdl.handle.net/11094/103704

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タイトル	Many-objective-optimized semi-automated robotic disassembly sequences
著者	NRID 1000030909403 1000030909403 ORCID 0000-0002-8555-8489 0000-0002-8555-8489 researchmap permalink takuya-ki takuya-ki Scopus Author ID 57207579369 57207579369 著者名 Kiyokawa, Takuya
	NRID 1000050294533 1000050294533 ORCID 0000-0002-7576-756X 0000-0002-7576-756X researchmap permalink kharada kharada Scopus Author ID 35446109900 35446109900 著者名 Harada, Kensuke
	NRID 1000020760002 1000020760002 ORCID 0000-0002-0058-2819 0000-0002-0058-2819 researchmap permalink weiweiwan weiweiwan Scopus Author ID 56238867200 56238867200 著者名 Wan, Weiwei
	著者名 Ishikura, Tomoki
	著者名 Miyaji, Naoya
	著者名 Matsuda, Genichiro
キーワード等	Disassembly sequence
	Many-objective optimization
	NSGA-III
	Robotic disassembly
内容	Kiyokawa T., Harada K., Wan W., et al. Many-objective-optimized semi-automated robotic disassembly sequences. Robotics and Autonomous Systems 197, 105301 (2026); https://doi.org/10.1016/j.robot.2025.105301.
内容	This article is part of a Special issue entitled: ‘Planning & Learning’ published in Robotics and Autonomous Systems.
抄録	This study tackles the problem of many-objective sequence optimization for semi-automated robotic disassembly operations. To this end, we employ a many-objective genetic algorithm (MaOGA) inspired by the non-dominated sorting genetic algorithm (NSGA)-III, along with robotic-disassembly-oriented constraints and objective functions derived from geometrical and robot simulations using three-dimensional (3D) geometrical information stored in a 3D computer-aided design (CAD) model of the target product. The MaOGA begins by generating a set of initial chromosomes based on a contact and connection graph (CCG), rather than random chromosomes, to avoid falling into a local minimum and yield repeatable convergence. The optimization imposes constraints on feasibility and stability as well as objective functions regarding difficulty, efficiency, prioritization, and allocability to generate a sequence that satisfies many preferred conditions under mandatory requirements for semi-automated robotic disassembly. The NSGA-III-inspired MaOGA also utilizes non-dominated sorting and niching with reference lines to further encourage steady and stable exploration and uniformly lower the overall evaluation values. Our sequence generation experiments for a complex product (36 parts) demonstrated that the proposed method can consistently produce feasible and stable sequences with a 100% success rate, bringing the multiple preferred conditions closer to the optimal solution required for semi-automated robotic disassembly operations.
出版者	Elsevier B.V.
収録物名	Robotics and Autonomous Systems
巻(号)	197
ページ	105301
刊行年月	2025-12-19
言語	英語
ハンドルURL	https://hdl.handle.net/11094/103704
PISSN	09218890
関連情報 (isIdenticalTo)	DOI (出版社版) https://doi.org/10.1016/j.robot.2025.105301
アクセス権	open access
権利情報	This article is licensed under a Creative Commons Attribution 4.0 International License.
出版タイプ	Version of Record
カテゴリ	学術雑誌論文 Journal Article

資源タイプ	学術雑誌論文
ローカル資源タイプ	学術雑誌論文
DCMI資源タイプ	text
DCTERMS.bibliographicCitation	Robotics and Autonomous Systems. 2025, 197, p. 105301
DC.title	Many-objective-optimized semi-automated robotic disassembly sequences
DC.creator	Kiyokawa, Takuya
	Harada, Kensuke
	Wan, Weiwei
	Ishikura, Tomoki
	Miyaji, Naoya
	Matsuda, Genichiro
DC.publisher	Elsevier B.V.
DC.language' scheme='DCTERMS.RFC1766	英語
DC.type' scheme='DCTERMS.DCMIType	text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2025-12-19
DC.identifier	https://doi.org/10.1016/j.robot.2025.105301
DC.subject	Disassembly sequence
	Many-objective optimization
	NSGA-III
	Robotic disassembly
DC.description	Kiyokawa T., Harada K., Wan W., et al. Many-objective-optimized semi-automated robotic disassembly sequences. Robotics and Autonomous Systems 197, 105301 (2026); https://doi.org/10.1016/j.robot.2025.105301.
DC.description	This article is part of a Special issue entitled: ‘Planning & Learning’ published in Robotics and Autonomous Systems.
DCTERMS.abstract	This study tackles the problem of many-objective sequence optimization for semi-automated robotic disassembly operations. To this end, we employ a many-objective genetic algorithm (MaOGA) inspired by the non-dominated sorting genetic algorithm (NSGA)-III, along with robotic-disassembly-oriented constraints and objective functions derived from geometrical and robot simulations using three-dimensional (3D) geometrical information stored in a 3D computer-aided design (CAD) model of the target product. The MaOGA begins by generating a set of initial chromosomes based on a contact and connection graph (CCG), rather than random chromosomes, to avoid falling into a local minimum and yield repeatable convergence. The optimization imposes constraints on feasibility and stability as well as objective functions regarding difficulty, efficiency, prioritization, and allocability to generate a sequence that satisfies many preferred conditions under mandatory requirements for semi-automated robotic disassembly. The NSGA-III-inspired MaOGA also utilizes non-dominated sorting and niching with reference lines to further encourage steady and stable exploration and uniformly lower the overall evaluation values. Our sequence generation experiments for a complex product (36 parts) demonstrated that the proposed method can consistently produce feasible and stable sequences with a 100% success rate, bringing the multiple preferred conditions closer to the optimal solution required for semi-automated robotic disassembly operations.
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.format	103704
citation_title	Many-objective-optimized semi-automated robotic disassembly sequences
citation_author	Kiyokawa, Takuya
	Harada, Kensuke
	Wan, Weiwei
	Ishikura, Tomoki
	Miyaji, Naoya
	Matsuda, Genichiro
citation_publisher	Elsevier B.V.
citation_language	英語
citation_date	2025-12-19
citation_journal_title	Robotics and Autonomous Systems
citation_volume	197
citation_firstpage	105301
citation_doi	https://doi.org/10.1016/j.robot.2025.105301
citation_public_url	https://hdl.handle.net/11094/103704

Many-objective-optimized semi-automated robotic disassembly sequences

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