基于改进蚁群算法的智能车路径优化

用于自动泊车领域的AGV小车载质量大，对移动轨迹的平滑性与行走距离有更高要求。针对传统蚁群算法易死锁、囤余节点多与转向幅度不可控等问题，提出了一种改进蚁群算法。首先，在算法正式开始迭代前使用地图补偿函数对地图进行优化，降低死锁概率；其次，在对地图优化处理后，对地图进行了信息素浓度初始化，加快了算法收敛速度；最后，通过调整路径生成逻辑，实现算法自适应调整步长，提高了路径的平滑性，减少转向摆动。仿真结果表明：改进后的算法死锁现象减少，收敛速度更快，所生成的路径转向平滑，囤余节点数与总路径长度降低。

Intelligent Vehicle Path Optimization Based on an Improved Ant Colony Algorithm

Due to the fact that AGVs used in the field of automatic parking are of great importance and have higher requirements for the smoothness of movement trajectory and walking distance, an improved ant colony algorithm has thus been proposed in view of such flaws as propensity to deadlock, redundance of idle nodes, and uncontrollability of the steering amplitude found in traditional ant colony algorithms. Firstly, by using a map compensation function the map is to be optimized prior to the formal iteration of the algorithm, thus reducing the probability of deadlock. Secondly, the pheromone concentration of the map can be initialized after a map optimization, which helps to accelerate the convergence speed of the algorithm. Finally, by adjusting the path generation logic, the step size can be adaptively adjusted by the proposed algorithm, with the path smoothness improved and the steering swinging reduced. The simulation results show that the improved algorithm reduces deadlock occurrence with a faster convergence speed, a smoother generated path steering, a smaller number of idle nodes and a shorter total path length.