## 6 Conclusions and Future Work  
This paper employed a neural network model to address the class of autonomous mobile robot navigation problems in mobile robot systems with the existence of obstacles. It focused on the scenario where the mobile robot only had a finite number of motion types and no global environmental information. Human-made decisions on these motion types can be collected together with the corresponding environmental information on various occasions including random generated obstacles. In this sense, the supervised learning method was employed to yield an algorithm to train a neural network model to replicate the  
Figure 9: The paths of the mobile robot while performing navigation tasks using the trained neural network model in the simulator. (**a**) APF; (**b**) proposed; (**c**) APF; (**d**) proposed. human decision process using recorded data. To validate the performance of the proposed algorithm and the trained model, a mobile robot simulator was developed to collect the data from the human decision process as well as perform a representative navigation task in a 2D plane. The simulation results illustrated that a high estimation accuracy rate of nearly 90% with respect to the human decision-making process was obtained using the proposed algorithm, and the trained model successfully completed the given tasks. Compared with the APF method, the neural network approach was able to handle the navigation task with obstacles without the local minimum problem, which proves the effectiveness of neural network decision. For future work, the trained model will be applied to an experimental test platform to further examine its practical performance, and the iterative learning control method in [35; 36; 37] can be implemented to guarantee the practical performance of the repetitive motion types. It is undeniable that the neural network only considers the current state of the robot for decision making, which leads to the certainty of global judgment. A navigation algorithm with a short memory will be considered to be applied to further improve its performance to handle the circular motion problem as shown in Figure 8c. Meanwhile, reinforcement learning methods can be employed to let the robot train itself by gaining a reward when approaching the destination or receiving punishment when colliding with an obstacle. Last but not least, the proposed method can be extended to a distributed version to handle the navigation task consisting of a number of mobile robots. Conceptualization, Y.C., Y.Z. and X.L. ; methodology, Y.C. and C.C. ; software, Y.C. and C.C. ; validation, Y.C. and C.C. ; formal analysis, Y.C., Y.Z. and X.L. ; investigation, C.C. ; resources, Y.C., Y.Z. and X.L. ; data curation, Y.C. ; writing--original draft preparation, Y.C. ; writing--review and editing, Y.Z. and X.L. ; visualization, C.C. ; supervision, L.S. ; project administration, Y.C. ; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China grant number 62103293, the Natural Science Foundation of Jiangsu Province grant number BK20210709, the Suzhou Municipal Science and Technology Bureau grant number SYG202138, and the Entrepreneurship and Innovation Plan of Jiangsu Province grant number JSSCBS20210641. Not applicable. The authors declare no conflict of interest. The following abbreviations are used in this manuscript.