## 3 Proposed Data-Driven Locomotion Recommendation Algorithm  
The proposed system is developed by collecting data from 20 robots whose capabilities are closely related to decontamination tasks, including fumigation robots, agricultural robots, and floor-cleaning robots. These robots share comparable operational environments and design requirements, including payload capacity, terrain type, slope navigation, and decontamination mechanisms. A complete summary of the dataset used is included in the Supplementary File. An LLM then trains on this dataset to learn the relationships between tasks, environmental conditions, and optimal locomotion types. The LLM used in this study was not fine-tuned in the conventional supervised-learning sense. Instead, it was employedin a prompt-based inference mode using the robot dataset provided in the Supplementary File. This allowed rapid customization and specification matching based on user-input task requirements. While the LLM selection was based on its broad generalization capability and context understanding, this work focuses on demonstrating its utility in design inference rather than benchmarking its training pipeline. When users provide task specifications, such as environment type, decontamination method, and robot size, the LLM processes these inputs to find the best-fit locomotion. Automating the selection process with the LLM significantly reduces design time, removing the need for manual iteration. The robot is developed using LLM-generated specifications and validated through real-world testing to ensure effectiveness in diverse environments. In order to systematically ascertain the most appropriate locomotion and track configuration for a robot, a methodical evaluation process is proposed, contingent upon environmental conditions. As depicted in the flowchart presented in Figure 8, the procedure commences by extracting essential operational parameters from the user's prompt utilizing a language model. These parameters are subsequently employed in a two-stage evaluation: initially, validating general locomotion types (wheeled or tracked) across various terrains; and subsequently, assessing specific track designs based on their mobility characteristics and environmental adaptability. Compatibility assessments and score assignments are executed iteratively, with the final recommendations being visualized through an annotated score matrix and bar charts to facilitate informed decision-making. The proposed Algorithm 1 starts by taking a natural language prompt from the user (user_prompt) that details the terrain, expected payload, friction, and slope. It uses the Deepseek-R1 model to parse this prompt into an LLM_Response, extracting a structured Parsed_JSON object with relevant fields. The LLM model breaks down the user's instructions into terrain_data, which includes Terrain Type, Payload capacity, and Slope. These details are categorized into terrains with properties like \(\mu\), roughness, slope, and weight, alongside locomotion_specs that define the operational limits for each locomotion type. The first sub-routine of Algorithm 1 is dedicated to validating various locomotion types (such as wheeled or tracked bases) by comparing their operational limits with a set of terrain parameters specified in the dataset. Compatibility for each terrain-locomotion pairing is assessed (score[locomotion]), and reasons for any failures or successes are recorded in a matrix that includes \(\mu\), roughness, and slope. Scores are determined based on successful matches, with higher importance terrains receiving greater weights. In the second sub-routine, during the Track-Type Recommendation phase, the track_dataset is loaded with various track designs and specifications. A new score[track_type] dictionary is created to assess each track's performance.