## 2 Related Studies
### Large Language Models  
AI-based textual models like LLMs are designed to process and generate human-like text based on text-based input prompts. These models are increasingly being integrated into various fields, including design and robotics. For instance, [PERSON] et al. introduced Mani-GPT, an interactive robotics model that interprets human instructions and generates appropriate plans for robotic manipulation, achieving 84.6% accuracy in intent recognition and task execution for pick-and-place robots [12]. Similarly, [PERSON] et al. [13] explored the use of NLP and GPT technologies to automate the early design stages of computer-aided design (CAD), where textual prompts are converted into new design concepts through a three-step process: domain understanding, problem-driven synthesis, and evaluation-driven synthesis. Meanwhile, [PERSON] et al. applied Gen-AI in human-robot collaboration within industrial settings, utilizing behavior trees and LLM technologies to enable real-time adaptability in assembly tasks, integrating blockchain instructions for enhanced reliability [14]. Likewise, [PERSON] et al. used ChatGPT and multimodal generative AI (Gen-AI) for architectural design, incorporating the Ethereum blockchain to enhance the authenticity, traceability, and ownership of the data used in the design process [16]. Lastly, [PERSON] et al. developed PromptCraft, an open-source tool designed to generate prompts for various robotic applications, integrating ChatGPT to improve simulations and task performance in free-form dialogues, geometric navigation, and aerial tasks [15].  
While numerous studies concentrate on nuclear decontamination methods, research into robots for decontamination tasks is sparse. Moreover, a substantial gap exists in studies that focus on the design of decontamination robots, particularly concerning locomotion systems tailored to specific environments and tasks. Current research on locomotion for decontamination robots is insufficient, with few studies addressing the challenges of navigating complex environments while maintaining efficiency. Furthermore, no studies investigate the integration of LLMs to personalize robot locomotion based on task requirements and environmental conditions.  
The research objectives considered in this study are categorized into three segments:  
* Identifying the optimal locomotion system for decontamination robots based on task requirements and environmental conditions to ensure efficiency in various settings.
* Integrating with LLMs to generate a best-fit design for the decontamination robot based on user inputs and environmental factors.
* Developing the decontamination robot based on the best-fit design produced by the LLM integration, implementing the optimal locomotion system, decontamination methods, and safety features for autonomous operation in complex environments.  
This integration has the potential to enhance the adaptability of robotic systems, thereby facilitating the dynamic optimization of locomotion systems tailored for various decontamination tasks. This, in turn, significantly improves their effectiveness and operational flexibility across diverse settings.