| Mobile robot technology is extensively studied in recent years, and path planning is an important part in navigation of mobile robot. Based on instruction and environment information, a collision-free path is employed by mobile robot to achieve the goal. Path planning is a guarantee for mobile robot to complete a task, and is an important symbol for intelligence degree of mobile robot. Ant colony algorithm (ACA) is a novel bio-inspired optimization algorithm, which simulates the foraging behavior of ants. ACA has the character of robustness, parallel computation and positive feedback, and it is currently a hot research in intelligence domain.Path planning has an essential relation with the behavior of real ants searching for food, so it is logical to apply ACA to path planning. To solve the path planning problem, ants shift from current node to next node in the way that random choice is combined with determination choice to effectively process the former path information, the phenomena of path that optimal ants crawled is updated to make ants search behavior close to better path, and the problem that ants may have no node to shift in some situation is solved to improve the algorithm stabilization. The method of auto control, location feedback, and direction forecast is applied to modify the former path in dynamic obstacle environment. The algorithm is simulated in both static and dynamic obstacle environment with the tool of MATLAB.Simulation results show that the method can quickly find an optimal collision-free path in different obstacle environment, and the algorithm validity is proved. |
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