基于双向汇聚引导蚁群算法的机器人路径规划

路径规划是自主移动机器人技术的核心理论问题之一，论文采用网格法建立路径规划问题的环境模型，提出了基于先验知识的优势方位角，建立了主优势网格和次优网格的改进网格模型，并采用基于子路径认知方法的信息素释放策略，提出了起始点与目标点互换的交替双向引导策略，实现了一种汇聚融合的信息素结构，实现了基于改进网格模型的双向汇聚斑迹信息素蚁群算法。实验表明，该方法在求解具有复杂障碍物分布的大规模地图规划问题时，具有空间复杂度小和效率高的优点，大大提升了构建初始解及收敛的速度，具有很好的求解性能。

Robot Path Planning Based on Bidirectional Aggregation Ant Colony Optimization

Path planning is a key theoretical issue of the autonomous mobile robot technology. This paper utilizes an improved grid method to establish environment model, which involves a new priori advantage azimuth structure that includes two parts of the primary dominant grid cell and the subprime grid cell. It improves the pheromone mark ant colony optimization algorithm by putting forward a novel pheromone update strategy based on secondary path cognitive method, which is called bidirectional guidance strategies. It repeats an alternation of the starting point and the target point in each new round of iteration. The experimental results show that the improved algorithm has the advantages of low spatial complexity and high efficiency in solving large-scale planning problems especially with complex obstacles, and greatly improves the speed of initial solution construction and convergence, and has good solving performance.