改进蚁群算法在路径规划中的应用

针对传统蚁群算法在机器人路径规划时存在收敛速度慢、易陷入局部最优等问题，提出了一种基于自适应归档更新的蚁群算法。根据路径性能指标建立多目标性能评估模型，对最优路径进行多指标优化；采用路径方案归档更新策略进行路径方案的更新和筛选，提高算法的收敛速度；当搜索路径进入不可行区域时，采用自适应路径补偿策略转移不可行路径节点，构造可行路径，减少死锁蚂蚁数量；若算法无法避开障碍或者进入停滞状态，则进行种群重新初始化，增加物种多样性，避免算法陷入局部最优。仿真实验表明，改进后的算法收敛速度更快、收敛精度更高、稳定性更好。

Application of improved ant colony algorithm in path planning

This paper proposed an ant colony algorithm based on adaptive archive updating to address the problems of the traditional ant colony algorithm in path planning, such as the tendency to fall into local optimum and slow convergence speed. It established the multi-objective performance evaluation model to optimizing the optimal path based on path performance indicators. It used the archive updating strategy to update and filter path schemes, increased the convergence speed of the algorithm. It used the adaptive path compensation strategy to transfer the infeasible path nodes and constructed feasible paths to reduce the number of deadlocked ants when the search path entered the infeasible region. It performed the population reinitialization to increase species diversity and prevented the algorithm from falling into a local optimum, if the algorithm was unable to avoid obstacles or entered a stagnant state. The simulation results prove that the improved algorithm has faster convergence speed, higher convergence accuracy and better stability.