Improved Ant Colony Algorithm and Its Application in Path Planning for Robots

An improved ant colony algorithm is presented based on dynamic self-adaptation adjustment of alpha, beta and it's applied to the path planning for mobile robot in this paper. Simulation is done to test the algorithm. Firstly, to increase the convergence speed and avoid the local optimum, the values of alpha (pheromone heuristic factor), beta (expected heuristic factor) are modified adaptively and they are introduced to reach a balance or approximate balance in the process of ant colony search path. This method expands the search space and makes the algorithm jump from the local optimum. Secondly, to compare other improved strategies with the improved strategy in this article. Then the optimized operators are introduced to remove redundant nodes in the path and make it become a practical path. Finally, the static environment is established based on the grid method, the improved ant colony algorithm is applied to the path planning for mobile robot and the simulation experiments are finished. From the results, it can see that the improved ant colony algorithm is viable and efficient.