# Reactive Obstacle-Avoidance Systems for Wheeled Mobile Robots Based on Artificial Intelligence
## 5 Discussion  
We identified strengths and challenges of reactive Obstacle-Avoidance systems based on approaches of an ANN-BP architecture and FIS rules. We show the comparisons in Tables 4-6. We highlight in these tables differences regarding the environmental input data collected by the robots in the real world, the environmental operating conditions, and a comparison of the conditions considered for obstacle avoidance. Therefore, in the analysis, we consider aspects such as: sensor response, field of vision, distance measurement, type of objects identified, type of surface, real experimental environment, effects or noise over the sensors, dynamics of the real environments, limitations of the mobile robots that affect the reliability, and efficiency of approaches to work well in real-world deployments. In addition, we present a summary of the strengths and challenges of the approaches considered. The strengths define the main contributions of each approach to avoid obstacles. The challenges describe the problems that must be solved to measure how successful or efficient these approaches would be when implemented in the real world where real conditions exist. An important remark is that we were able to identify that, to the best of our knowledge, most of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Training Type** & **Neural Architecture** & **Epochs** & **Training Time** & **Performance** & **Training: R** & **Total Response Time (s)** \\ \hline Trainbr & 3-5-4 & 301 & 83.734 & 0.534 & 0.25 & 83.804 \\ \hline Trainbfg & 3-5-4 & 100 & 26.273 & \(8.01\times 10^{-06}\) & 0.99999 & 26.343 \\ \hline Trainscg & 3-5-4 & 225 & 49.822 & \(9.98\times 10^{-06}\) & 0.99999 & 49.892 \\ \hline Traincgb & 3-5-4 & 100 & 23.761 & \(8.87\times 10^{-06}\) & 0.99999 & 23.832 \\ \hline Traincgf & 3-5-4 & 144 & 36.303 & E1.00 \(\times\) 10\({}^{-05}\) & 0.99999 & 36.373 \\ \hline Traincgp & 3-5-4 & 80 & 18.6305 & \(9.97\times 10^{-06}\) & 0.99999 & 18.7005 \\ \hline Trainbr & 3-5-4 & 365 & 10.5156 & \(5.33\times 10^{-06}\) & 0.25 & 10.5856 \\ \hline Traincgb & 1-4-5-4 & 125 & 1.6563 & \(9.89\times 10^{-06}\) & 0.99999 & 1.7263 \\ \hline Trainscg & 7-3-4 & 100 & 2.5469 & \(9.37\times 10^{-06}\) & 0.99999 & 2.6169 \\ \hline Trainbfg & 3-5-6-4 & 83 & 38.7656 & \(9.76\times 10^{-06}\) & 0.99999 & 38.8356 \\ \hline Trainbr & 2-5-6-8-9-4 & 15 & 11.8906 & \(5.37\times 10^{-06}\) & 0.24992 & 11.9606 \\ \hline Traincgp & 2-5-6-8-9-4 & 284 & 128.9219 & \(2.88\times 10^{-05}\) & 0.99997 & 128.9919 \\ \hline Traincgp & 5-4-4-4 & 116 & 7.30 & \(9.40\times 10^{-06}\) & 0.99999 & 7.37 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation of neural architectures for mobile robots.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Equipment** & **Capture** & **Decision-Making** & **Fuzzy Logic** & **ANN** & **Total Response Time** \\ \hline Laptop Windows 7, Core i7, & & & & \\
2.3 GHz, 8 GB RAM & 10.4 & 15.6 & X & & 38.9 \\ \hline PC Windows XP, Core Duo, & & & & & \\
3.16 GHz, 3 GB RAM & 6.4 & 18.9 & X & & 43.5 \\ \hline PC Windows 10, Intel(R) & 65 ms & 10.5156 & & X & 10.5856 \\ Core(TM), 2.70 GHz, 16 GB RAM & & & & & \\ \hline Embedded System (Arduino R3), & 65 ms & 10.5156 & & X & 17.2536 \\ ARM Microprocessor, 33 MHz & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average execution times (ms).  
the approaches published elsewhere were implemented only in a simulated environment, such as [15; 16].