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SYSTEMATIC REVIEW article

Front. Robot. AI

Sec. Industrial Robotics and Automation

Volume 12 - 2025 | doi: 10.3389/frobt.2025.1584657

This article is part of the Research TopicRobotic Applications for a Sustainable FutureView all 6 articles

The Future of Robotic Disassembly: A systematic review of techniques and applications in the age of AI

Provisionally accepted
Soufiane  AmeurSoufiane Ameur1Mohamed  TabaaMohamed Tabaa2*Zineb  HidilaZineb Hidila2Mohamed  HamlichMohamed Hamlich1Kaouter  KarboubKaouter Karboub2Richard  BeareeRichard Bearee3
  • 1University of Hassan II Casablanca, Casablanca, Morocco
  • 2Pluridisciplinary Laboratory of Research & Innovation (LPRI), EMSI, Casablanca, Morocco
  • 3LISPEN, Arts et Métiers Institute of Technology, Lille, France

The final, formatted version of the article will be published soon.

In today's era of digital transformation, industries have made a decisive leap by adopting data‑driven, robot‑assisted disassembly solutions that cut cycle time and cost relative to labor-intensive manual tear‑down. Thus, including robots not only improved production activities but also strengthened the safety measures that once the human operator was handling. Minimizing the impact of the human factor in the process means minimizing incidents related to it. The disassembly of Waste Electrical and Electronic Equipment (WEEE) poses complex technical, economic, and safety challenges that traditional manual methods struggle to meet. Thus, there is a need for a decision-making tool harmonized with human cooperation, in which Artificial Intelligence (AI) plays a pivotal role by providing financially viable solutions while ensuring a secure collaborative environment for both humans and robots. This review synthesizes recent advances in AI-enabled robotic disassembly by focusing on four main research areas: (i) optimization and strategic planning, (ii) human–robot collaboration (HRC), (iii) computer vision (CV) integration, and (iv) Safety for Collaborative Applications. A supplementary subsection is also included to briefly acknowledge emerging topics such as reinforcement learning that lie outside the main scope but represent promising future directions. By analyzing 62 peer-reviewed studies published between 2000 and 2024, the results identify how these themes converge, highlight open challenges, and map out future research directions.

Keywords: Robotic disassembly, AI approaches, Human robot collaboration, Computer Vision, Systematic review

Received: 04 Mar 2025; Accepted: 01 Sep 2025.

Copyright: © 2025 Ameur, Tabaa, Hidila, Hamlich, Karboub and Bearee. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mohamed Tabaa, Pluridisciplinary Laboratory of Research & Innovation (LPRI), EMSI, Casablanca, Morocco

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