## 4 Experimental Validation  
### Improved BIT* Algorithm with Adaptive Parameter Optimization for Optimal Path Planning  
#### 4.1.1 Performance of the BIT* Algorithm  
This section will elaborate on the simulation experiment design for the Batch Informed Trees (BIT*) algorithm and provide an in-depth analysis of the experimental results. The core of our research lies in how to optimize the performance of the BIT* algorithm in Unmanned Surface Vehicle (USV) path planning to meet the demands of practical applications. Despite BIT*'s theoretical asymptotic optimality and demonstrated superior performance in certain scenarios, its performance in real-world applications still necessitates experimental validation and analysis. Firstly, BIT* algorithm performance is influenced by various factors, including heuristic function selection and sampling strategies. The interaction and impact of these factors require in-depth experimental investigation. Secondly, different application scenarios have varying requirements for path planning algorithms; for example, efficiency and robustness are crucial in complex environments. Simulation experiments can simulate diverse scenarios in a controlled environment, evaluating the BIT* algorithm's performance under different conditions to guide parameter selection and performance optimization. Furthermore, comparative studies of the BIT* algorithm with other algorithms can objectively assess its strengths and weaknesses, offering references for algorithm selection and improvement. Therefore, simulation experiments are an important and necessary means to validate BIT* algorithm performance, analyze characteristics, and conduct comparative research.  
The BIT* algorithm possesses these theoretical advantages, and the original algorithm has already exhibited good performance in some benchmark tests. However, in practical applications, especially under certain specific environmental or task requirements, some issues may still arise. In complex environments with dense obstacles or narrow passages, the sampling strategy of the BIT* algorithm may require further optimization to enhance its efficiency and reliability in finding feasible paths.  
Analysis of the results obtained from the original BIT* algorithm, as depicted in Figure 4, reveals that the planned paths exhibit large angles between connected segments at several nodes, resulting in a lack of smoothness. Furthermore, issues such as complex node updates and prolonged search times are observed, leading to an increased number of algorithm iterations and consequently impacting the overall efficiency. To address these shortcomings, this study proposes a more efficient iterative logic for the algorithm to accelerate its execution, enhance its optimization efficiency, and further improve the optimality of the planned path length. To this end, we also incorporate a precise fluid dynamics model to constrain the robot's motion. The application of this model contributes to a reduction in the robot's turning time during path planning, thereby lowering energy consumption. This is of significant importance for improving the robot's operational efficiency and extending its working duration.