基于改进蚁群算法的机器人路径规划研究

在二维静态环境下的机器人路径规划中，采用基本蚁群算法寻优存在搜索时间较长、效率较低、容易陷入局部最优等问题。针对这些问题对基本蚁群算法进行改进，改进的蚁群算法使用不同的期望值机制，采用挥发系数自适应方式更新信息激素，并加入拐点参数作为路径的评价标准之一。对这两种算法进行仿真分析，可得改进后的蚁群算法比基本蚁群算法搜索能力更强，算法效率更高，所寻路径更短。结果表明，该改进算法提高了算法效率，抑制了算法陷入局部最优并实现了机器人最优路径搜索，使机器人可以快速地避开障碍物安全到达目标点。

Research on path planning of robot based on improved ant colony algorithm

The basic ant colony algorithm applied to robot path planning in the two-dimensional static environment has problems of long search time, inefficiency and easy to fall into local optimization and so on. It makes improvements on the algorithm for these problems. It uses different expectation mechanism, updates the pheromone by taking evaporation coefficient adaptive approach, and joins the inflection point parameter as one evaluation criteria of the path. Simulation of the two algorithms shows the improved ant colony algorithm is stronger of searching ability and more efficient than the basic ant colony algorithm and gets a shorter path. The results show that the improved algorithm improves the efficiency of the algorithm and inhibits algorithm into local optimum and achieves search of robot’s optimal path. Robot can avoid obstacles to reach the target point safely and quickly.