## 5 Developed Decontamination Robot Prototype
### Limitations and Future Scope  
Although the proposed algorithm has demonstrated effectiveness across the majority of use cases, there exist minor errors that prevent the algorithm from functioning optimally. Some of the limitations are as follows:  
* LLM Output Deviation: The LLM is currently used in a prompt-based inference mode without fine-tuning. As such, it may return outputs with missing fields, non-JSON formatting, or unexpected data types. A possible solution is to address this by implementing robust validation schemas and retry mechanisms, as well as refining prompt engineering techniques. Fine-tuning the LLM with more structured examples may also reduce inconsistency.
* Approximate Terrain Matching: The use of partial string matching (e.g., substring or fuzzy match) between user input and pre-defined terrain types can yield suboptimal results, especially for ambiguous or uncommon terrain descriptions. A possible solution is to use a semantic similarity model, which can improve contextual matching. Additionally, expanding the terrain dataset with synonyms and example scenarios will enhance accuracy.
* Fixed Thresholds: Hard-coded thresholds for payload, slope, and friction make the system rigid and less responsive to user-specific priorities. A possible solution is to add adaptive thresholding using rule-based scaling or reinforcement learning, based on user feedback and mission history, which can enable more dynamic, context-aware decision-making.
* No Multi-Terrain Aggregation: The current system architecture evaluates one terrain type at a time, which limits its utility in multi-environment deployments. A possible solution is to incorporate a multi-label terrain evaluation system, which can assess the feasibility across multiple terrains simultaneously and recommend hybrid locomotion solutions accordingly.
* Lack of Adaptability to Unstructured Terrain: The model is primarily trained on structured or semi-structured environments found in existing robot datasets. A possible solution is to incorporate data from field robotics and search-and-rescue robots operating in dynamic terrains, combined with few-shot learning, which can improve generalization to novel terrains. The future scope of the proposed work includes the following:
* Cross-Platform Compatibility: Design an algorithm that is capable of functioning across various robotic platforms with minimal modifications.
* Explainable Recommendation Reports: Automatically generate comprehensive explanation reports that summarize the rationale behind each locomotion or track recommendation, thereby enhancing transparency for engineers and operators.
* Environment Clustering and Unseen Terrain Generalization: Utilize unsupervised learning techniques to cluster similar terrains and extrapolate recommendations, even when encountering terrains not included in the original terrain data list.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **Specifications** & **Parameters** & **Specifications** \\ \hline Track Material & Rubber & Driving Motor (Each) & 2 kW \\ \hline Track width & 230 mm & Maximum Speed & 2 km/h \\ \hline Dimensions & 2000 \(\times\) 1300 \(\times\) 710 mm & Maximum Slope & \(\leq\)30\({}^{\circ}\) \\ \hline Self-Weight & 450 kg (Excluding battery) & Battery & LiFePO\({}_{4}\) 48 V, 200 Ah \\ \hline Loading Capacity & 1200 kg (Including robot weight) & Brush Type & Nylon bristles \\ \hline \end{tabular}
\end{table}
Table 2: Developed Robot Specification Table.  
Dynamic Learning with Feedback: Following deployment, gather operational data on the success or failure of recommendations and subsequently retrain the scoring system or recalibrate thresholds automatically.
* Reconfigurable Locomotion: Designing modular robots with transformable locomotion enables real-time terrain adaptation, enhancing versatility in complex decontamination environments.
* Hybrid Intelligence: Integrating ontology and LLMs enables adaptive, structured robot design for diverse decontamination tasks and dynamic environments.