基于多种障碍环境建模的机器人路径规划

移动机器人路径规划问题一直受到人们的广泛关注.针对不同复杂程度的障碍物环境分别采用传统算法和智能化算法进行控制.针对环境简单、障碍物较少且分散的情况,使用人工势场法进行局部路径规划.当环境复杂、障碍物数量较多且密集时,采用蚁群算法进行路径规划.首先使用栅格法对全局环境建立数学模型,然后模拟蚂蚁群体觅食过程,通过多次信息交流和数据更新最终在全局环境中规划出一条最优路径.在蚁群算法的模拟实验中,通过大量对比实验和仿真结果确定最佳组合参数设置,然后将这组参数用在不同的障碍物环境进行实验,均得出了最优路径.结果证实人工势场法和蚁群算法分别用于较简单和较复杂的路径规划环境是可行的且参数选取是合理的.

Robot Path Planning Based on Different Obstacle Environment Modeling

The problem of path planning for mobile robots has been widely concerned. The traditional algorithm and intelligent algorithm are used to control the obstacle environment which has different complexity. In view of the simple environment, less obstacles and scattered situation, the artificial potential field method is used for local path planning. When the environment is complex, the number of obstacles is large and dense, ant colony algorithm is used for path planning. Firstly, the grid method is used to establish a mathematical model of the global environment, and then the process of ant colony foraging is simulated. Finally, an optimal path is drawn in the global environment through multiple information exchanges and data updates. In the simulation experiment of ant colony algorithm, through a large number of comparative experiments and simulation results to determine the best combination parameter settings, and then this group of parameters are used in different obstacle environments for experiments, and the optimal path is obtained. The results show that the artificial potential field method and ant colony algorithm are feasible and the parameter selection is reasonable for the simpler and more complex path planning environment, respectively.