## 6 Conclusions and Future Work  
This paper studies the online path planning of mine SAR. Based on simulating an SAR environment using the characteristics of a mining cave field, a path-planning model for POMDP-based online search areas is proposed. The gray objective decision function guides this model and plans the optimal search path through information analysis, making it more flexible and intelligent. The comparative experimental results show that the model can complete target search tasks online in different maps of the same region and generate search paths to achieve faster convergence to dynamic target positions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Solution** & **Parameter** & **Value** \\ \hline \multirow{5}{*}{Improved Solution} & learning rate & 0.8 \\  & reward decay & 0.9 \\  & \(\epsilon\) greedy & 0.9 \\  & \(\Phi(t)\) & 5 \\  & \(\beta(t)\) & \(-1\) \\ \hline \multirow{5}{*}{Unimproved Solution} & learning rate & 0.4 \\  & reward decay & 0.8 \\  & \(\epsilon\) greedy & 0.9 \\  & \(\Phi(t)\) & 1 \\ \cline{1-1}  & \(\Phi(t)\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The principle parameters of the target-tracking algorithm.  
Figure 8: The results of path planning for different solutions. The orange arrow in the figure represents the search path. The green arrow represents the movement of the target. The yellow star represents the final capture position.  
Figure 7: A comparison of different solutions in Table 2 in terms of training effectiveness. (**a**) The change process of training steps in the unimproved scheme. (**b**) The change process of training steps in the improvement plan.  
The future potential trends that can be integrated with the planning issues of UGVs will include, but are not limited to, the following:  
* The interaction of UGVs with Cyber-Physical systems could enhance UGV's adaptability to dynamic environments and validate the effectiveness and feasibility of path-planning algorithms through the use of 3D virtual reality models [39].
* The strategic application of human-machine interaction technology is not just a possibility, but a promising avenue that is poised to play a pivotal role in the rapid advancement of UGV intelligence [40].
* Swarm Intelligence technology can expand UGVs' application scope and significantly improve its efficiency in completing tasks [41].
* Another important research direction to enhance the autonomy of UGVs is to combine them with Internet of Things technology to improve their flexibility in different locations [42; 43].  
Given the aforementioned topics, further in-depth research will be carried out within the above-specified fields.  
Conceptualization, S.Z.; Methodology, S.Z.; Software, S.Z.; Validation, S.Z.; Writing--original draft, S.Z.; Writing--review & editing, Q.Z.; Supervision, Q.Z.; Project administration, Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China under Grant 62188101 and 62303135, and in part by the Heilongjiang Touyan Team Program, and in part by the Harbin Institute of Technology.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.