2维图像的道路检测是移动机器人视觉导航中的难点问题。基于图像分割问题的能量最小化函数,推导出一种适用于群集优化迭代求解的视觉能量最小化模型,并提出一种基于多蚁群动态协作的优化策略,实现道路图像检测的优化算法。该方法根据"分而治之"的原则,先由各蚁群独立优化各自子问题,然后针对约束条件动态变化的特点采用一种新的动态自适应信息交换策略,实现一种近似全局最优的2维道路图像检测。与基于Graph Cut的道路图像检测算法相比,动态多蚁群协作优化算法不仅具有更好的检测性能,并且可实现任意类数的检测,适用于包含多种类型的复杂道路场景。

Road detection of 2-dimension images is a key problem in vision navigation of mobile robots.Based on the energy minimization function of image segmentation,a new vision energy minimization model is derived which can be solved conveniently by the iterative optimization of swarm intelligence.And a multi-colony ant based dynamic cooperation optimization strategy is proposed to implement the optimized road detection.According to the divide-and-conquer principle,each colony optimizes a sub-problem independently.Then,a set of information exchange strategies are proposed for adaptive dynamic cooperation between neighboring colonies to realize the global optimization of road detection.Compared with the Graph cut based road detection method,the proposed dynamic multi-colony ant cooperative optimization method not only has better detection performance,but also can detect arbitrary number of clusters.It can be applied to detect complex road scenes that obtain multiple types of road.