Abstract With the growing challenge of e-waste and end-of-life phones, a clear demand is required for efficient and scalable recycling solutions. This study proposes a realistic simulation of a collaborative robotic disassembly system that consists of six specialized stations designed for recycling and disassembling 100% of the end-of-life phone components. This study integrates human-robot collaboration (HRC) and human-robot interaction (HRI) using voice and gesture commands to enable adaptive task reassignment and real-time error correction with reduced downtime. This framework combines real-world simulation, RoboDK modeling, linear programming optimization, and HRC scenarios to assess system responsiveness. Over 30 operational and collaborative constraints were mathematically formulated. A total processing time of 955.37 seconds has been achieved in a test carried out on disassembling 10 phones with error rates ranging from 3.3–4.1%. The hand-sign recognition module based on YOLOv12n accurately detected gestures with confidence scores in the range of 0.91 to 0.94, thus improving safety and non-verbal communication in HR centers. The execution of voice commands for actions such as “start” and “you take” showed inference times of 2.1 to 2.6 seconds, thus validating the feasibility of real-time HRI with two methods. With scenarios such as a human abandoning a task, a robot misclassifying a device or switching tasks based on screen conditions, HRC reduced downtime and improved task continuity by enabling robots to autonomously handle failed or delayed human operations. A brief discussion highlighted our solution in comparison with rigid systems such as Apple's. The proposed architecture offers greater flexibility when working on variant inputs, combining robotic precision with human adaptability. The results confirm the potential of constraint-driven, voice-assisted collaborative systems as a scalable and sustainable solution for next-generation e-waste disassembly.
Ameur et al. (Tue,) studied this question.