Huijing Wu

Off-road path planning involves navigating vehicles through areas lacking established road networks, which is critical for emergency response in disaster events, but is limited by the complex geographical environments in natural conditions. How to model the vehicle’s off-road mobility effectively and represent environments is critical for efficient path planning in off-road environments. This paper proposed an improved A* path planning algorithm based on a Delaunay triangular NavMesh model with off-road environment representation. Firstly, a land cover off-road mobility model is constructed to determine the navigable regions by quantifying the mobility of different geographical factors. This model maps passable areas by considering factors such as slope, elevation, and vegetation density and utilizes morphological operations to minimize mapping noise. Secondly, a Delaunay triangular NavMesh model is established to represent off-road environments. This mesh leverages Delaunay triangulation’s empty circle and maximum-minimum angle properties, which accurately represent irregular obstacles without compromising computational efficiency. Finally, an improved A* path planning algorithm is developed to find the optimal off-road mobility path from a start point to an end point, and identify a path triangle chain with which to calculate the shortest path. The improved road-off path planning A* algorithm proposed in this paper, based on the Delaunay triangulation navigation mesh, uses the Euclidean distance between the midpoint of the input edge and the midpoint of the output edge as the cost function $g\left(n\right)$, and the Euclidean distance between the centroids of the current triangle and the goal as the heuristic function $h\left(n\right)$. Considering that the improved road-off path planning A* algorithm could identify a chain of path triangles for calculating the shortest path, the funnel algorithm was then introduced to transform the path planning problem into a dynamic geometric problem, iteratively approximating the optimal path by maintaining an evolving funnel region, obtaining a shortest path closer to the Euclidean shortest path. Research results indicate that the proposed algorithms yield optimal path-planning results in terms of both time and distance. The navigation mesh-based path planning algorithm saves 5~20% of path length than hexagonal and 8-directional grid algorithms used widely in previous research by using only 1~60% of the original data loading. In general, the path planning algorithm is based on a national-level navigation mesh model, validated at the national scale through four cases representing typical natural and social landscapes in China. Although the algorithms are currently constrained by the limited data accessibility reflecting real-time transportation status, these findings highlight the generalizability and efficiency of the proposed off-road path-planning algorithm, which is useful for path-planning solutions for emergency operations, wilderness adventures, and mineral exploration.

Accurate temperature prediction is essential for economic production and human society’s daily life. However, most current methods only focus on time-series temperature modeling and prediction, ignoring the complex interplay of meteorological variables in the spatial domain. In this paper, a novel temperature prediction model (D3AT-LSTM) is proposed by combining the three-dimensional convolutional neural network (3DCNN) and the attention-based gated cyclic network. Firstly, the historical meteorological series of eight surrounding pixels are combined to construct a multi-dimensional feature tensor that integrates variables from the temporal domain as the input data. Convolutional units are used to model and analyze the spatiotemporal patterns of the local sequence in CNN modules by combining them with parallel attention mechanisms. The fully connected layer finally makes the final temperature prediction. This method is subsequently compared with both classical and state-of-art prediction models such as ARIMA (AR), long short-term memory network (LSTM), and Transformer using three indices: the root mean square error (RMSE), the mean absolute error (MAE), and the coefficient of determination ($R^2)$. The results indicate that the D3AT-LSTM model can achieve good prediction accuracy compared to AR, LSTMs, and Transformer.