poses long-term health risks of skin irritation and respiratory issues to workers, leading to neurological disorders and even cancer [3]. To tackle the health risks linked to manual fumigation, using an autonomous fumigation robot in an urban landscape presents a highly effective alternative. These fumigation robots eliminate the need for human involvement in hazardous environments, ensuring safer operations while delivering precise, uniform, and efficient fumigation services. Robot fumigation provides minimal human exposure and precise area coverage in complex urban structures, adheres to safety protocols, and improves overall effectiveness in pest control.  
The design and development of a fumigation robot requires careful consideration of various functionalities and aesthetic-based parameters, including the robot's size, payload capacity, mobility system, autonomy level, and battery capacity. These factors must align with the specific application, such as the terrain conditions, spray type, fumigation duration, weather patterns, and fumigation location. Researchers and designers rely on external sources like artificial intelligence (AI) to address these complex requirements and accelerate the design process in various stages. AI-driven solutions for smart data collection, supervisory control data analysis, and predictive models are utilized in the early design phases of identifying user needs, brainstorming, and generating design concepts. By combining human creativity with AI technologies, designers can explore a broader range of design possibilities and uncover innovative solutions. With the advancements in AI, generative AI (Gen-AI) designs have become a popular method for creating unique and creative designs in numerical, textual, or pictorial data. This method analyzes and learns patterns from existing datasets, incorporating randomness to produce unique and innovative outputs using both explicit programming and implicit learning. Early methods relied on explicit programming based on human design expertise with predefined rules and frameworks. However, these methods lack the ability to leverage the vast knowledge embedded in the existing design datasets.  
Gen-AI design tools use various algorithms with iterative learning to process designer-defined input specifications, generating and improving solutions that satisfy functional requirements or applications. The resulting Gen-AI designs often contain unique geometries or configurations that challenge and outperform the traditional human design creativity and workflow [4; 5]. In engineering design, data-driven approaches like deep generative models (DGMs) are adopted in Gen-AI. DGMs are capable of approximating complex, high-dimensional probability distributions using large datasets and are uniquely suited for creating new design samples by synthesizing features learned from existing data [6]. Generative adversarial networks (GANs) and variational autoencoders (VAEs) are two prominent classes of DGMs that generate images, text, and structured data across multiple domains.  
This paper explores the Gen-AI design models and the development of an autonomous fumigation service robot. Section 2 delves into the related studies and demonstrates case studies on design generation using Gen-AI models. Section 3 elaborates on the proposed machine-learned multimodal and feedback-based variational autoencoders (MMF-VAE) model approach to design generation. Section 4 outlines the design fixation, generation, and development of fumigation robots based on the Gen-AI design, emphasizing the importance of the proposed Gen-AI model in this process. Lastly, Section 5 concludes the paper with a summary of key insights.