基于混合遗传模拟退火算法的矢量场机器人导航

摘要

提出了一种解析形式的机器人矢量场导航模型,模型中场矢量的指向就是机器人的理想移动方向.模型假设工作空间中的障碍物为多边形,通过对障碍边界上电场的积分得到了排斥场的封闭解.导航必须考虑路径对长度、平滑度及安全性的要求,因此,一种混合遗传模拟退火优化算法被用来对导航模型的参数进行搜索,以寻找最优路径解.仿真结果验证了本文模型的有效性,优化所获路径的比较说明此混合算法要优于遗传算法和模拟退火算法.%An analytical vector field model of robot workspace was presented. In the model, the vector of resultant fielddescribed the most promising direction of robot motion. The model assumed that the edges of every obstacle, which was polygo-nal, were uniformly charged. It was shown that the resulting repulsive force, which pushing the robot away from the obstacles,could be calculated in closed form. Several factors including the length, the smoothness and the safety of the path require con-sidering in robot navigation. Thus, a hybrid optimization algorithm, HGSA, which incorporated the simulated annealing algo-rithm (SA) into the genetic algorithm (GA), was proposed to optimize the path through searching the model parameters. Theeffectiveness of the proposed model was verified by computer simulation in three workspaces with different obstacle distribution.Comparisons between the optimized results show that the hybrid algorithm obtains better path solutions than either GA or SA.