The future of robotic disassembly: a systematic review of techniques and applications in the age of AI - Ameur, Soufiane; Tabaa, Mohamed; Hidila, Zineb; Hamlich, Mohamed; Karboub, Kaouter; Bearee, Richard (Frontiers in Robotics and AI, 2025)
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.