## 4 Incorporation of Proposed Model in Urban Landscape Fumigation Service Robot
### Design Parameter Generation Using Proposed Model  
The design specification generation highlights the model's capability of producing accurate, dataset-driven outputs that significantly influence design refinement. In developing the fumigation service robot for urban landscape terrains, the generated specifications should align with the targeted requirements, ensuring both functionality and practicality with real-time deployment. One of the primary design considerations is maintaining compact physical dimensions of less than 1000 mm \(\times\) 1000 mm, enabling the robot to navigate through tight spaces commonly found in urban environments. An example of the terrain is provided as an input to the design model. Additionally, the robot is designed to be lightweight, ensuring that it can be easily transported and handled by the operator for portability. In addition, the robot should incorporate advanced computational capabilities to support the fusion of multiple sensors. This ensures the seamless integration of technologies such as LiDAR, depth cameras, and inertial measurement units (IMUs) for autonomous navigation and identifying mosquito hotspots. The robot should have  
Figure 7: Process flowchart of designing a fumigation robot using the proposed model. a precision spraying system to minimize chemical usage, ensure controlled and efficient application, and reduce wastage while targeting specific areas. To meet operational demands, the robot should support a 4-h running time with a robust power management system with precise control and a smooth torque drive mechanism that ensures efficient navigation, making it particularly suited for urban terrains. This design generation process, with multimodal inputs and iterative refinement, ensures that the fumigation robot meets targeted requirements while addressing challenges unique to urban landscapes. From the existing literature [24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39] on various spraying robots used for urban fumigation, agricultural fumigation, and similar applications, a readily available spraying robot dataset was prepared and provided as input to the proposed model for generating detailed design inputs. The dataset outlined in Figure 8 and Table 1 compiles specifications of existing spraying robots, including their physical dimensions, power management systems, spraying technologies, tank capacities, navigation types, user interface designs, intended terrains, performance metrics, and terrain of applications. Only terrain-based mobile robots for fumigation services are considered in this study. By incorporating this dataset into the proposed model, the design generation process benefits from a unique design based on the robust foundation of existing knowledge, enabling the proposed model to refine and produce specifications tailored to the targeted fumigation applications. Once the design parameters for the fumigation robot are provided to the proposed MMF-VAE model, the model is integrated with a custom-made GPT to facilitate interactive customization. ChatGPT/Ollama, being a conversational AI, allows users to refine and adjust the desired parameters by providing real-time feedback. By leveraging this interactive capability, targeted design parameters for the fumigation robot can be generated based on  
Figure 8: Recent spraying/agricultural robot dataset used in training the proposed model, sources from [24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39] sequentially. user inputs and preferences. This integration ensures a more personalized and efficient design process, enabling users to iteratively refine specifications. Table 2 outlines the design outputs generated without a dataset, highlighting several limitations in meeting practical deployment requirements. In this case, the model suggests specifications such as an oversized battery, increased weight, a four-wheel drive system, and RTK GPS-based point-to-point navigation, leading to increased costs and inefficiencies.