## 5 Conclusions  
By integrating the enhanced BIT* algorithm with the TD3 deep reinforcement learning algorithm, this research constructed a path planning model tailored for intelligent inland waterway vessels. The model first leverages the BIT* algorithm to generate initial paths in static environments and subsequently employs the TD3 algorithm for dynamic optimization. This dynamic optimization incorporates twin Critic networks and delayed policy update mechanisms to enhance learning efficiency and stability in continuous control tasks. Furthermore, the model introduces a deep reinforcement learning-based reward mechanism designed to minimize path turning angles, smooth speed variations, and shorten path lengths, thereby resulting in paths that better align with actual navigation requirements. In the experimental design, the paper adopted a grid-based method to construct the map model and innovatively incorporated hydrodynamic calculations to optimize the energy equation for underwater vehicles. A weighting function was designed to evaluate path selection, considering both distance and energy consumption. In addition, multi-threaded parallel computation and a multi-seed-based adaptive parameter optimization method were employed to enhance algorithm performance. In conclusion, the hybrid algorithm model proposed in this research theoretically demonstrates the potential for performing autonomous vessel navigation tasks and exhibits significant advantages in practical applications. In the complex scenarios of inland waterway navigation, this model can effectively handle crowded, narrow waterways with dynamic obstacles (such as other vessels, buoys, etc. ), plan safer and more feasible routes more reliably, reduce collision risks, and improve navigation efficiency. This is crucial for the automation and intelligitization of inland waterway shipping. Simultaneously, by optimizing path length and the number of turns, it significantly reduces the energy consumption of the USV, which not only lowers operating costs but also meets environmental protection requirements, strongly promoting the development of green shipping. Moreover, the online learning capability conferred by the TD3 algorithm enables the USV to adjust its navigation plan in real-time according to environmental changes such as weather and currents, as well as unexpected events (such as temporary traffic control), ensuring the safety and punctuality of navigation. While ensuring safety, the model can also plan shorter routes to reduce navigation time and improve transportation efficiency, bringing significant economic benefits to commercial shipping. A high degree of autonomy also means reducing the demand on manual operations, which reduces risks brought by human factors and increases the safety of the shipping. These advantages collectively indicate that the model has broad application prospects in the field of intelligent inland waterway navigation. Figure 14: Reward function variation over 2000 episodes. The research findings of this paper are entirely consistent with the proposed evidence and arguments. Through experimental verification, our proposed BIT*+TD3 hybrid algorithm significantly outperforms traditional path planning algorithms (such as A*, RRT*, and the original BIT* algorithm) in terms of path planning success rate, path quality, environmental adaptability, and computational efficiency, fully demonstrating the effectiveness and superiority of the algorithm. These findings not only support the research hypothesis we proposed in the introduction, that is, the combination of BIT* and TD3 algorithms can effectively solve the path planning problem of Unmanned Surface Vehicles in complex inland river environments, but also provide a practical and feasible solution to this problem.