## 6 Conclusions  
This paper introduces the basic principles of A* and DWA. The implementation of the algorithm is mainly divided into three steps: model building, velocity sampling, and trajectory evaluation. Through the implementation of these three aspects, the driving of the mobile robot is directly limited to the velocity space and controlled in a feasible dynamic range. The problems of A* and DWA are pointed out: timeliness and robustness are not strong, and it is easy to fall into local optimization. The DQN algorithm is also introduced in detail. DQN algorithm is the fusion of Q-earning algorithm and convolutional neural network. DQN algorithm contains target network and experience storage mechanism and can deal with large-scale state-action space. This paper proposed a fusion path planning algorithm based on DQN, DWA and A*. The fusion algorithm not only integrates the characteristics of the dynamic window method directly acting on velocity space, it can also handle large-scale state-action space pairs, so that the mobile robot can quickly adapt to the complex changing environment in the training process. The basic framework of the fusion algorithm is constructed, and the DWA algorithm and DQN algorithm are fused effectively. Then, by setting the reward function and \(\varepsilon\)-greed strategy, we can improve the action selection mechanism of mobile robots, so that the probability of the robot selecting the optimal action in the training process increases. The simulation results show that the fusion path planning algorithm has strong generalization ability and robustness, and it  
Figure 37: Path comparison map. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **Average Planning** \\ **Time (s)** \\ \end{tabular} & \
\begin{tabular}{c} **Average Path** \\ **Length (m)** \\ \end{tabular} &
\begin{tabular}{c} **Average Time** \\ **Spent (s)** \\ \end{tabular} \\ \hline A* & 3.98 & 109.73 & 859.73 \\ \hline DWA & 4.76 & 120.45 & 900.10 \\ \hline DQN & 2.73 & 100.03 & 864.03 \\ \hline Hybrid A* & 1.78 & 94.22 & 778.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Experimental data of global planning outside the factory. can quickly adapt to complex changing environment, meaning that its practical value is significantly improved. In addition, compared with the traditional algorithm, the fusion algorithm obtains more prominent and better-quality path planning performance with fewer training times, shorter computation time, and faster convergence speed. Although the fusion algorithm has achieved good performance in simulation experiments, the proposed method still needs to be further improved and optimized due to the limited research ability and time of the author. Therefore, the future research direction can be roughly divided into two aspects. First, there are many variants of algorithms in deep reinforcement learning, as well as the deep transformation of network structure, so we still need to continue learning. Deep learning algorithm fusion is an important research topic in mobile robot path planning. On the other hand, experimental verification is limited to a single robot at present, and group experiments on multiple agents are not carried out. The future value of this algorithm needs to be evaluated further. Conceptualization, Y.Z. and Q.Z. ; methodology, C.C. ; software, Y.Z. ; validation, Q.Z. and C.C. ; formal analysis, Y.Z. ; investigation, Y.Z. ; resources, Q.Z. ; data curation, Y.Z. ; writing--original draft preparation, Y.Z. ; writing--review and editing, Y.Z. and Q.Z. ; visualization, C.C. ; supervision, C.C. ; project administration, Q.Z. ; funding acquisition, Q.Z. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the Basic Scientific Research Project of Liaoning Provincial Department of Education, JYTMS20231434. The experimental data in this paper are calculated from MATLAB2020a simulation platform under different environmental conditions.