# Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling
## 5 Conclusions  
In this paper, we have introduced our cognitive science inspired navigation framework and its three composing parts: semantic modeling framework (SMF), semantic information processing (SIP) module, and semantic autonomous navigation (SAN) module. We also showed how we modeled the environment employing the triplet ontological semantic model (TOSM), which allowed the three modules mentioned above to utilize the same modeled data. The on-demand database allowed different robots to share a common database by selectively querying the environment data based on each robot's intrinsic capabilities. The SIP module, performed object detection and localization using the deep learning-based approach, while, inferred the robot's relationship information based on the semantic properties of recognized objects derived from the on-demand database. Moreover, the SAN structure showed that a hybrid planning approach, which combines classical and sampling-based planners with reinforcement learning algorithms, is able to mitigate each planner's shortcomings leading to a more robust planning algorithm. The performed experiment showed that our framework can be applied to real-world scenarios, and navigation tasks on large dynamic environments can be performed seamlessly with the aid of TOSM data.  
Immediate future work is focused on expanding the framework to other environments, more specifically outdoor environments. A modeled outdoor environment can also be used on different platforms, such as self-driving vehicles. Moreover, a large variety of TOSM semantic information shall be used by the SIP module when performing object recognition and localization. In addition, we are  
Figure 15: Autonomous navigation experiment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Navigation** & **Average Time** & **Success** \\
**Method** & **to Complete** & **Rate** \\ \hline RL & 8m47s & 80\% \\ PRM-RL & 5m20s & 80\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison between RL and PRM-RL navigation.  
planning to add a large variety of possible behaviors to enable higher-level missions to be performed. A more robust sampling algorithm can also be used to improve both the graph build time and the success rate of navigation tasks. Finally, another line of research is using the mental simulation to check the plan validity before its execution. For example, the feasibility of following a sequence of points generated by the PRM algorithm can be tested on simulation. In case of failure, a new sampled graph can be generated.  
Conceptualization, S.-H.J., S.M., Y.G.R., and T.-Y.K.; methodology, S.-H.J., S.M., and Y.G.R.; software, S.-H.J., S.M., and Y.G.R.; validation, S.-H.J.; formal analysis, S.-H.J.; investigation, S.-H.B. and K.-H.L.; data curation, S.-H.B. and K.-H.L.; writing--original draft preparation, S.-H.J., S.M., and Y.G.R.; writing--review and editing, S.-H.J., S.M., and M.K.; visualization, S.-H.J. and S.M. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Korea Evaluation Institute of Industrial Technology (KEIT) funded by the Ministry of Trade, Industry and Energy (MOTIE) (No. 1415162366).  
The authors declare no conflict of interest.