دانلود رایگان مقاله انگلیسی طراحی جاده خاکی بر اساس الگوریتم کلونی مورچه - اشپرینگر 2018

Abstract

Optimal vehicle off-road path planning problem must consider surface physical properties of terrain and soil. In this paper, we firstly analyse the comprehensive influence of terrain slope and soil strength to vehicle’s off-road trafficability. Given off-road area, the GO or NO-GO tabu table of terrain gird is determined by slope angle and soil remolding cone index (RCI). By applying tabu table and grid weight table, the influence of terrain slope and soil RCI are coordinated to reduce the search scope of algorithm and improve search efficiency. Simulation results based on tracked vehicle M1A1 in off-road environment show that, improved ant colony path planning algorithm not only considers the influence of actual terrain and soil, but also improves computation efficiency. The time cost of optimal routing computation is much lower which is essential for real time off-road path planning scenarios.

5 Conclusions

In this paper, the actual trafficability influence of terrain slope and soil strength on vehicle mobility is considered in off-road path planning problem. By combining theoretical analysis with simulation experiment, the tabu table of slope and soil is set up which determines the GO/NO GO property of terrain-vehicle relationship. Considering the slope influence on vehicle mobility, the trafficable slope is furthermore partitioned into different grades with different weight. Based on these tabu table and grid weight table, an improved ant colony algorithm (IACA) is applied for 3-dimension path planning. Simulation results show that IACA is more efficient in path planning. Comparing the improved algorithm with traditional Dijkstra algorithm, numerical experiments result shows that improved algorithm can plan an optimal route with much lower time cost which is essential for real time off-road path planning scenarios.