关键词: 路径规划, 改进蚁群算法, 奖惩因子, 参数优化

Abstract:

The traditional ant colony optimization in path planning is easy to fall into local optimization and poor early path effective solution, in order to solve this, an improved algorithm of the traditional ant colony optimization is proposed and applied to AGV（Automated Guided Vehicle） path planning. Firstly, the grid map is used to build the car workspace model, and the improved head and tail search mechanism is used to improve and accelerate the global search ability and early convergence speed of the algorithm. The maximum and minimum thresholds of reward and punishment factors and pheromones are introduced to reward the pheromones on the optimal path of each generation, and the punishment on the worst path is introduced to improve the global search ability. Genetic algorithm variation is introduced. The genetic algorithm is used to optimize the parameters of the improved ant colony optimization to reduce the influence of parameters on the algorithm. And the algorithm simulation is carried out on VS2017 and MATLAB software platform. The results show that the algorithm has great improvement in avoiding local optimization and accelerating convergence speed.

Key words: path planning, improved ant colony optimization, reward and punishment factor, parameter optimization