Research on Improved Ant Colony Algorithm for Mountain Hiking Emergency Rescue Path Planning

被引：0
|
作者：
Wu Y. [1 ,2 ]
Li J. [1 ,2 ]
Bi S. [1 ,2 ]
Zhu X. [1 ,2 ]
Wang Q. [3 ]
机构：
[1] College of Physics and Information Engineering, Fuzhou University, Fuzhou
[2] The Academy of Digital China (Fujian), Fuzhou University, Fuzhou
[3] College of Environment and Safety Engineering, Fuzhou University, Fuzhou
来源：
Journal of Geo-Information Science | 2023年 / 25卷 / 01期
基金：
中国国家自然科学基金;
关键词：
3D mountain environment; Ant Colony Optimization(ACO); DEM; field emergency rescue; Genetic Algorithm(GA); grid method; hiking path planning; mountain hiking accessibility;
D O I：
10.12082/dqxxkx.2023.220535
中图分类号：
学科分类号：
摘要：
When a firefighting incident occurs in a wild complex mountain with no obvious roads or sparse roads, it is crucial to plan a safe and fast route through the complex mountain environment. Aiming at the problem that Ant Colony Optimization (ACO) is easy to fall into local optimum and the search time is long for complex mountain path planning, our study proposes an ACO algorithm for hiking emergency rescue path planning, which is suitable for fine-grained wild mountain environments. Firstly, our study analyzed the relationship between surface information and human movement speed based on existing literature and designed the objective function and heuristic function of the optimization algorithm considering two factors: surface shrub cover and terrain slope. Then, we used a combination of plane and field of view ant search combined with heuristic function and pheromone concentration to determine the next grid to be selected in the optimization process of the improved algorithm. Finally, the improved algorithm used a Laplace distribution to adjust the initial pheromone to improve the quality of the algorithm's initial solution. For the deadlock problem, the improved algorithm added isolated pheromones to prevent the next ant from falling into a deadlock dilemma. The improved algorithm used a genetic operator with grouping to update the global regular pheromone to avoid the ant colony from falling into a local optimum dilemma. In our study, we applied four ACO to the wild mountain environment of 400×400 grids, 1000 grids×1000 grids, 5000 grids×5000 grids, and 10 000 grids×10 000 grids for comparison, and set different starting and ending points for each environment. The experimental results show that each ACO using a combined planar and visual field search approach can obtain feasible paths in all four experiments, which verified the feasibility of the method. The quality of the paths using the improved algorithms was better than the other three algorithms, with improvements of 0.52%~4.95%, 4.71%~5.39%, 2.26%~13.11%, and 3.84%~9.16% in the four experiments, respectively, and the improved algorithm had shorter search time and convergence time. In addition, the combined planar and visual field search approach reduced the search space and improved the computational efficiency of the algorithm in the field 3D mountain environment. This search method was faster than the 8-connected method and reduced the average time consumption by more than 90%. Our algorithm is suitable for hiking path planning research in large 3D mountain scenes, with reduced planning time and improved path quality, providing technical support for the work of finding the best 3D mountain hiking paths without road networks. © 2023 Journal of Geo-Information Science. All rights reserved.
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页码：90 / 101
页数：11
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