引用本文: 王岩韬, 赵昕颐. 基于危险天气不确定性的最小风险路径规划方法[J]. 工程科学学报, 2024, 46(5): 887-896.
WANG Yantao, ZHAO Xinyi. Minimum-risk path planning based on hazardous weather uncertainty[J]. Chinese Journal of Engineering, 2024, 46(5): 887-896.
 Citation: WANG Yantao, ZHAO Xinyi. Minimum-risk path planning based on hazardous weather uncertainty[J]. Chinese Journal of Engineering, 2024, 46(5): 887-896.

## Minimum-risk path planning based on hazardous weather uncertainty

• 摘要: 为降低飞行过程中遭遇危险天气的概率，同时避免大范围绕飞导致的路径与耗油增加，针对航路中的雷暴、积冰和颠簸天气，使用数值预报和概率预报，面向航前飞行计划，提出一种基于危险天气不确定性的最小风险路径规划方法. 首先，基于概率预报数据使用配料法和C-F模型计算雷暴发生概率，基于数值预报数据计算积冰预测指数和颠簸预测指数；然后，融合多类型危险天气，提出一种具备风险标识的栅格化地图；在此基础上，改进传统路径最短的规划算法，构建以风险最小化为目标的Dijkstra和A*算法；最后，使用2023年4月3日华中地区强对流天气预测数据建立风险地图，使用上述改进算法与传统Dijkstra、A*和RRT算法进行路径规划并对比分析. 结果表明，传统Dijkstra和A*算法可计算得到最短飞行路径，而改进的A*算法可计算得到总风险最小路径；若综合考虑飞行风险与路径长度，改进的Dijkstra算法最为适合.

Abstract: Hazardous weather, such as thunderstorms, icing, and turbulence, is one of the main causes of flight accidents. To avoid possible risks caused by temporary diversion of hazardous weather during flight, numerical and probabilistic forecasting are employed to predict en route hazardous weather at the flight planning level. Considering the uncertainty of hazardous weather, a flight path planning method for uncertain weather is developed that can guarantee flight operation safety to the greatest extent. First, using probabilistic meteorological forecast data, we establish a mapping relationship between the diagnostic elements of thunderstorms and the probability of their occurrence. This is realized by an ingredients-based methodology that considers the three occurrence conditions of thunderstorms: water vapor, instability, and lifting trigger conditions. Afterward, the C-F model is applied to integrate the probability data of thunderstorm diagnostic elements. This fusion process enables the calculation of thunderstorm probabilities and the establishment of a comprehensive thunderstorm area map. The icing prediction index and turbulence prediction index are then measured based on numerical prediction data, and the icing and turbulence regions are defined. To solve the problem that current studies only consider a single hazardous weather event, a rasterized risk map is crafted to determine the risk of hazardous weather by integrating regions prone to thunderstorms, icing, and turbulence. Based on this, traditional path planning algorithms are enhanced to maximize flying safety. For example, we propose a risk minimization Dijkstra algorithm and a risk minimization A* algorithm considering the risk of nonobstacle environments. Finally, using the forecast data of severe convective weather in Central China on April 3, 2023, a real risk map is created. Using this map, both enhanced risk minimization algorithms and traditional A*, Dijkstra, and RRT(Rapid-exploration random tree) algorithms are applied independently for path planning. The risk and length of each planned route are then calculated to evaluate the algorithm’s performance. The results show that the risk minimization A* algorithm minimizes the total risk of the path, the traditional algorithm minimizes the path length, and the risk minimization Dijkstra algorithm exhibits the best overall performance. Therefore, if the purpose of path planning is to minimize flight risk, the flight path based on the risk minimization A* algorithm should be selected because of its superior safety. When considering both path length and risk comprehensively, the flight path under the risk minimization Dijkstra algorithm is preferred because this approach proves to be more economical than the risk minimization A* algorithm. The RRT algorithm is more suitable for path planning in high-dimensional complex environments and has no evident advantage in solving the problems described in this work.

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