# Reactive Obstacle-Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence
## 4 Results and Comparisons
### Case I: Mobile Robot and Fuzzy Logic
The processing time between both formats may differ depending on the hardware and software available. However, the point clouds suggest an average 30-s processing time per capture, based on experimental data shown in [13; 14], ranging from 13.6 s to 41.1 s. We compare resource consumption regarding space among the algorithms mostly used by point clouds, when they are coupled to a reactive architecture. The comparison shows that the response time does not present any improvement related to depth images. The second aspect, the 8-bit versus 16-bit values, was analyzed, and, due to the definition of three linguistic variables, the division of the different ranges does not cause any loss in the evaluations. Consequently, there is a better computational time, which is generated by using smaller data bit size. The third aspect considers the definition of the ranges for the type of membership function, where the extremes of the value close to 0 and 255 are defined in such a way that the function does not allow the membership value to decrease, resulting in soft turns or straight-line navigation (in case the objects in the three directions are too far away). For external values, the trapezoidal membership function is used. For intermediate values, the triangular function is implemented since the turns should not be so pronounced, and the system must have a midpoint where it allows a constant output. The \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Ruleset** & **No.** & \multicolumn{2}{c}{**If**} & \multicolumn{2}{c}{**Then**} \\ \hline \multirow{8}{*}{Sharp Turns} & 1 & Near & Near & Near & VeryNegative \\  & 2 & Near & Near & Medium & VeryPositive \\  & 3 & Near & Near & Far & VeryPositive \\  & 4 & Medium & Near & Near & VeryNegative \\  & 5 & Medium & Near & Medium & VeryNegative \\  & 6 & Medium & Near & Far & VeryPositive \\  & 7 & Far & Near & Near & VeryNegative \\  & 8 & Far & Near & Medium & VeryNegative \\  & 9 & Far & Near & Far & VeryNegative \\ \hline \multirow{8}{*}{Predictive Rules} & 10 & Near & Medium & Medium & LittlePositive \\  & 11 & Medium & Medium & Near & LittleNegative \\  & 12 & Medium & Medium & Far & LittlePositive \\  & 13 & Far & Medium & Medium & LittleNegative \\  & 14 & Far & Medium & Far & LittleNegative \\  & 15 & Near & Far & Far & LittlePositive \\  & 16 & Medium & Far & Far & LittlePositive \\  & 17 & Far & Far & Near & LittleNegative \\  & 18 & Far & Far & Medium & LittleNegative \\ \hline \multirow{8}{*}{Straight Line} & 19 & Near & Medium & Near & Zero \\  & 20 & Medium & Medium & Medium & Zero \\  & 21 & Near & Far & Near & Zero \\  & 22 & Near & Far & Medium & Zero \\  & 23 & Medium & Far & Near & Zero \\  & 24 & Medium & Far & Medium & Zero \\  & 25 & Far & Far & Far & Zero \\ \hline \multirow{2}{*}{Smooth Turns} & 26 & Near & Medium & Far & Positive \\  & 27 & Far & Medium & Near & Negative \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Fuzzy Inference Matrix. trapezoidal and triangular functions do not require complex operations, as compared to the Gaussian function, which is expressed using exponentiation and potentiation operations. The proposed architecture focuses on the least amount of calculations and preserves enough information for decision-making. Membership degree for each value \(x\) is defined by evaluating it on its corresponding membership function (fuzzification). Figure 13 shows an example of fuzzification where x = 1.6 with triangular function, obtaining a membership degree of \(\mu\) = 0.4.