## 5 Conclusions
Specifically, the introduction of the BIT* algorithm solves the problem of quickly generating initial paths in static environments, the TD3 algorithm solves the problem of optimizing paths and adapting to changes in dynamic environments, and the introduction of the energy consumption equation further improves the practicality and economy of path planning. The combination of the three successfully solves the main problem raised in the introduction, that is, how to plan a safe, efficient, and energy-saving navigation path for Unmanned Surface Vehicles in complex and dynamic inland river environments. Although the proposed BIT*+TD3 hybrid algorithm has achieved remarkable results, it is important to acknowledge its advantages and disadvantages in comparison with other published procedures. On the one hand, the hybrid approach offers a higher planning success rate, particularly in complex and dynamic environments, outperforming traditional sampling-based or search-based methods. The TD3 component contributes to superior path quality, generating smoother, shorter, and more energy-efficient trajectories. The online learning capability of TD3 provides strong environmental adaptability. Furthermore, computational efficiency is improved by leveraging multi-threading and parameter optimization strategies. On the other hand, the hybrid algorithm introduces a higher level of complexity in terms of implementation and parameter tuning compared to simpler methods. The TD3 algorithm, as a deep reinforcement learning technique, inherently requires a longer training time to reach optimal performance, and it also places demands on computing resources. The performance is also limited by the accuracy of the simulation environment. Future research can improve these shortcomings, such as exploring more lightweight deep reinforcement learning algorithms or using methods such as transfer learning to shorten training time. In conclusion, the hybrid algorithm model proposed in this research theoretically demonstrates the potential for performing autonomous vessel navigation tasks and exhibits advantages in practical applications. As illustrated in Figure 15, the combination of BIT* and TD3 has potential applications beyond inland waterway vessels, spanning domains such as mobile robotics, autonomous vehicles, industrial automation, and virtual environments. This breadth of potential applications underscores the versatility and adaptability of the proposed hybrid approach. This study provides new insights and technical support for the development of intelligent shipping technology and identifies future research directions, including further algorithm optimization to adapt to more complex environmental conditions and the validation of algorithm stability and reliability in real-world applications. Future work could also explore the implementation of BIT*+TD3 in the aforementioned application areas, tailoring the approach to the specific challenges and requirements of each domain. **Author Contributions:** Conceptualization, Y.X. ; Methodology, Y.X. ; Formal analysis, Y.M. and Y.C. ; Investigation, Y.C. ; Resources, Y.M. and Z.L. ; Visualization, Z.L. and X.L. ; Supervision, X.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This study is funded by the Shanghai Maritime University Research Fund \"Development and Application of New Technologies for Intelligent Shipping and Safety Management of Shipping Companies\" (H20230487). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data is contained within the article. **Conflicts of Interest:** The authors declare no conflict of interest.