关键词: 量子进化算法, 蚁群算法, 复杂环境, 栅格法, 路径规划

Abstract:

For mobile robot path planning in complex environments, an ant colony algorithm based on quantum (QACA) is proposed to plan the optimal collision-free path. The core of the algorithm is the introduction of the quantum state vector of quantum algorithm to respect the pheromone and quantum revolving gate to update the pheromone in ant colony system (ACS), in order to increase the diversity of position and to speed up the convergence speed of the algorithm. Simulation comparison shows that the algorithm can increase the randomness of the algorithm with better population diversity, higher convergence speed and better global optimization ability than the traditional ant colony algorithm, and can quickly find a optimal path planning even in the very complex environment.

Key words: quantum evolutionary algorithm, ant colony algorithm, complex environment, grids, path planning