AI Article Synopsis
- An improved A* algorithm is developed to enhance off-road emergency rescue path planning by considering unpaved roads and road factors for better efficiency.
- The algorithm classifies nodes based on their angles to optimize search directions, leading to quicker identification of the shortest travel time for emergency rescues in wilderness settings.
- Experimental results show this improved A* algorithm decreases travel time for off-road vehicles by over 21% and boosts search efficiency by nearly 94% compared to the traditional A* algorithm.
Article Abstract
To address the problem of ignoring unpaved roads when planning off-road emergency rescue paths, an improved A* algorithm that incorporates road factors is developed to create an off-road emergency rescue path planning model in this study. To reduce the number of search nodes and improve the efficiency of path searches, the current node is classified according to the angle between the line connecting the node and the target point and the due east direction. Additionally, the search direction is determined in real time through an optimization method to improve the path search efficiency. To identify the path with the shortest travel time suitable for emergency rescue in wilderness scenarios, a heuristic function based on the fusion of road factors and a path planning model for off-road emergency rescue is developed, and the characteristics of existing roads are weighted in the process of path searching to bias the selection process toward unpaved roads with high accessibility. The experiments show that the improved A* algorithm significantly reduces the travel time of off-road vehicles and that path selection is enhanced compared to that with the traditional A* algorithm; moreover, the improved A* algorithm reduces the number of nodes by 16.784% and improves the search efficiency by 27.18% compared with the traditional 16-direction search method. The simulation results indicate that the improved algorithm reduces the travel time of off-road vehicles by 21.298% and improves the search efficiency by 93.901% compared to the traditional A* algorithm, thus greatly enhancing off-road path planning.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11397823 | PMC |
| http://dx.doi.org/10.3390/s24175643 | DOI Listing |
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