## 6 Conclusions and Outlook  
In order to promote the intelligent development of parks, based on the low execution efficiency of single UGV tasks, the slow convergence speed of Q-learning algorithms in multi-agent systems and complex environments, and the low learning efficiency, this paper proposes a collision avoidance cooperation mechanism among multiple UGV and an improved reinforcement learning algorithm--OWS Q-learning algorithm. Based on the idea of the Q-learning algorithm, the OWS Q-learning algorithm changes the update mode of Q function, the learning rate \(\alpha\) and the \(\varepsilon-greedy\) strategy of action selection. Finally, simulation experiments are conducted in three different scenarios, and the performance of the proposed OWS Q-learning is compared with SARSA, Q-learning, and speedy Q-learning. Compared with the experimental results of the other three algorithms, only the OWS Q-learning algorithm can converge, and the OWS Q-learning algorithm has planned the shortest collision-free path for UGVS in different experimental scenarios. In experimental scenario 1, compared with SARSA, Q-learning, and speedy Q-learning, the calculation time of OWS Q-learning was reduced by 60.44%, 53.93%, and 22.26%, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **Statistics** & **SARSA** & **Q-Learning** & **Speedy Q-Learning** \\ \hline
1 & Calculation time & 60.44\% & 53.93\% & 22.26\% \\
2 & Calculation time & 69.55\% & 67.21\% & 30.94\% \\
3 & Calculation time & 52.90\% & 53.53\% & 24.32\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The improvement in calculation time of the OWS Q-learning algorithm compared to other algorithms. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scenario** & **Number of** & **Evaluation** & **SARSA** & **Q-Learning** & **Speedy** & **OWS Q-Learning** \\ \hline
1 & 1800 &
\begin{tabular}{c} Astringency \\ Average reward \\ \end{tabular} & Divergent & Divergent & Divergent & 1010 iterations begin to converge \\
2 & 1800 &
\begin{tabular}{c} Astringency \\ Average reward \\ \end{tabular} & Divergent & Divergent & 858 iterations begin to converge \\
3 & 1800 & \
\begin{tabular}{c} Astringency \\ Average reward \\ \end{tabular} &
\begin{tabular}{c} Divergent \\ \end{tabular} & Divergent & 810 iterations begin to converge \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison of the four algorithms. respectively. In experimental scenario 2, compared with the above three algorithms, the calculation time of OWS Q-learning is reduced by 69.55%, 67.21%, and 30.94%, respectively. In experimental scenario 3, compared with the above three algorithms, the calculation time of OWS Q-learning is reduced by 52.90%, 53.53%, and 24.32%, respectively. The OWS Q-learning algorithm is verified to be superior to the comparison algorithm. The multi-UGV collision avoidance coordination mechanism proposed in this manuscript is based on the static environment of the park, but in real life, emergencies may occur in the park, such as sudden pedestrians, vehicles, etc., which will involve some dynamically changing obstacles, and the environment will become more complex. Therefore, in the face of a random environment, this collision avoidance coordination mechanism needs to be further improved, and the OWS Q-learning algorithm needs to be further improved to better handle a larger number of UGVs. In future research work, on the one hand, we will consider the dynamic complex environment in the park, consider the existence of dynamic obstacles in the designed multi-UGV collision avoidance coordination mechanism, on the other hand, in order to further improve the performance of the OWS Q-learning algorithm, we will consider the research and improvement from the structural framework of the algorithm to better solve the path planning problem of multiple UGVs in more complex environments. Conceptualization, Y.C. ; methodology, Y.C. ; software, Y.C. ; validation, Y.C. ; writing--original draft, Y.C. ; conceptualization, X.F. ; supervision, X.F.