Abstract
Nowadays, path planning has become an important field of research focus. Considering that the ant colony algorithm has numerous advantages such as the distributed computing and the characteristics of heuristic search, how to combine the algorithm with two-dimension path planning effectively is much important. In this paper, an improved ant colony algorithm is used in resolving this path planning problem, which can improve convergence rate by using this improved algorithm. MAKLINK graph is adopted to establish the two-dimensional space model at first, after that the Dijkstra algorithm is selected as the initial planning algorithm to get an initial path, immediately following, optimizing the select parameters relating on the ant colony algorithm and its improved algorithm. After making the initial parameter, the authors plan out an optimal path from start to finish in a known environment through ant colony algorithm and its improved algorithm. Finally, Matlab is applied as software tool for coding and simulation validation. Numerical experiments show that the improved algorithm can play a more appropriate path planning than the origin algorithm in the completely observable.
Highlights
The essence of road traffic navigation problem [1] is the optimal path planning problem [2] [3] that is planning out a reasonable path from start to finish under the known road network topological structure
The results demonstrate that ant colony algorithm is a good solution for path planning problem
Improved ant colony algorithm can plan out a better path than the original algorithm
Summary
The essence of road traffic navigation problem [1] is the optimal path planning problem [2] [3] that is planning out a reasonable path from start to finish under the known road network topological structure. How to cite this paper: Wang, R. and Jiang, H. (2015) Two-Dimension Path Planning Method Based on Improved Ant Colony Algorithm. How to plan out a reasonable path in time is very practical significance of the research question. Zhu [9] applied ant colony algorithm to path planning by modeling the environment with grid method. The results demonstrate that ant colony algorithm is a good solution for path planning problem. Planning experimental results are presenting through ant colony algorithm and its improved algorithm.
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