This study explores a novel path for designing high-quality medical waste logistics robots by leveraging the synergistic mechanism between Qualia Theory and Artificial Intelligence Generated Content (AIGC) technology. The research aims to address critical issues in the design of medical waste logistics products, such as severe homogenization and inadequate user experience, thereby advancing the development of smart hospital systems. Centered on biosafety perception and user qualia requirements, a “dual-perception” evaluation framework was established. Initially, ChatGPT was employed to extract perceptual demand vocabulary, and the Analytic Hierarchy Process (AHP) was combined to construct a design element system. Subsequently, Midjourney was utilized to generate a library of design schemes, from which target concepts were selected through evaluations by the general public and experts. Finally, Stable Diffusion and the Rhino Platform were used to refine details, culminating in a viable design strategy. The results demonstrate that the proposed “dual-perception-AIGC synergistic workflow” can integrate multi-dimensional information and facilitate rapid form generation, effectively enhancing both design efficiency and perceptual quality. This research provides an AIGC-driven innovative design approach for medical waste logistics systems and offers valuable insights for the methodological study of smart medical equipment design.
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Share and Cite
Ma, H., Yang, M., Guo, H., Wu, X., Wu, F., Xiao, C. (2025) A Design Study of Medical Waste Logistics Robots Based on AIGC and a Dual-perception Mechanism. Scientific Research Bulletin, 2(2), 22-33.