## 4 Results
Concurrently, four samples--X-03, Y-05, Z-03, and K-05--demonstrated the highest cumulative correlations with imagery semantic descriptors across four component categories, serving as representative cases for analysis. Based on the vectorized representations of four prototypical models (X03, Y-05, Z-03, K-05), and guided by [PERSON]'s functional-structure mapping framework [32], we employed path-recognition tools (e.g., the Illustrator Path Analysis plugin or CAD contour tracing) to extract three categories of semantically significant contour paths within each functional region: (1) closed primary contours defining the overall visual boundary, (2) structural corner lines reflecting module-junction features, and (3) functional split lines indicating hatches or interface divisions. These paths, each governed by explicit geometric rules, form a coherent set of linear construction units. By aligning these units with the underlying functional modules, we identified eight key linear elements--L\({}_{1}\) (primary front contour), L\({}_{2}\) (front seam), L\({}_{3}\) (side waistline), L\({}_{4}\) (side corner), L\({}_{5}\) (top envelope), L\({}_{6}\) (sensor frame), L\({}_{7}\) (base boundary), and L\({}_{8}\) (track cover), as shown in Table 9.  
To identify the most salient regions for shape generation, our prior eye-tracking study revealed that users allocate significantly more initial attention to the front and side faces, underscoring their critical role in conveying affective imagery and reinforcing product identity. Accordingly, the shape grammar derivation focuses on four functionally meaningful vector paths from these regions (L\({}_{1}\)-L\({}_{4}\)), thereby reducing combinatorial complexity and concentrating on high-load visual areas. Eight fundamental transformation rules are defined--replacement (R1), addition/removal (R2), fine-tuning (R3), scaling (R4), rotation (R5), duplication (R6), mirroring (R7), and shearing (R8)--each capable of multi-level variation in geometry, length, angle, symmetry, and degrees of freedom, thus constituting the generative syntax of \"base form \(\rightarrow\) variant.\"The industrial cleaning robot's form features are encoded as follows: primary contour L\({}_{1}\) (a\({}_{1}\)a\({}_{2}\)a\({}_{3}\)a\({}_{4}\)), seam line L\({}_{2}\) (b\({}_{1}\)b\({}_{2}\)b\({}_{3}\)b\({}_{4}\)), waistline L\({}_{3}\) (c\({}_{1}\)c\({}_{2}\)c\({}_{3}\)), and structural corner L\({}_{4}\) (d\({}_{1}\)d\({}_{2}\), \(\theta\)). Dimensional constraints, derived from sample measurements and manufacturing requirements, are imposed: L\({}_{1}\): 520 mm \(<\) a\({}_{1}\)a\({}_{2}\)\(<\) 580 mm, 420 mm \(<\) a\({}_{3}\)a\({}_{4}\)\(<\) 480 mm; L\({}_{2}\): 300 mm \(<\) b\({}_{1}\)b\({}_{4}\)\(<\) 380 mm, 200 mm \(<\) b\({}_{2}\)b\({}_{3}\)\(<\) 260 mm, with an offset \(\geq\) 30 mm from the boundary; L\({}_{3}\): 550 mm \(<\) c\({}_{1}\)c\({}_{2}\)\(<\) 720 mm, elevation difference c\({}_{3}\)\(\in\) [50, 100] mm; L\({}_{4}\): 280 mm \(<\) d\({}_{1}\)d\({}_{2}\)\(<\) 600 mm, corner angle \(\theta\)\(\in\) [85\({}^{\circ}\), 155\({}^{\circ}\)]. All paths are constrained within their respective functional-region boundaries. The overall shape grammar derivation process is illustrated in Figure 12.  
Figure 12: Grammar deduction of the shape of the cleaning robot. \begin{table}
\begin{tabular}{c c} \hline \hline
**Typical Sample** & **Design Elements** \\ \hline Front face X03 & \\ Side face Y05 & \\ \hline Top Z03 & \\ \hline Bottom K05 & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Core styling elements of cleaning robots L1–L8. To enhance the emotional consistency of generated forms, we integrated semantic-driven rules into our shape grammar framework by mapping affective descriptors to specific transformation operations as post-generation style constraints.