基于改进蚁群算法的移动机器人路径规划研究

针对蚁群算法在复杂环境下收敛速度慢且易陷入局部最优值的问题,提出一种改进的蚁群优化算法。该方法依据起始点和目标点位置信息选择全局有利区域增加初始信息素浓度,提高前期蚂蚁搜索效率;增加避障策略,避免蚂蚁盲目搜索产生大量交叉路径并有效减少蚂蚁死锁数量;采用动态参数控制的伪随机转移策略,提出优质蚂蚁信息素更新原则,自适应调整挥发系数,提高算法全局性;进行二次路径规划,优化路径并降低移动机器人能耗的损失。实验结果表明,该算法有较高的全局搜索能力,收敛速度明显加快,并且可以有效提高移动机器人工作效率,验证了该算法的有效性和优越性。

Research on path planning of mobile robot based on improved ant colony algorithm

The ant colony algorithm is slow in convergence and easy to fall into local optimal value in complex environment. To solve these problems, an improved ant colony optimization algorithm is proposed. The position information of the starting point and the target point are utilized to select the global favorable region. In this way, the initial pheromone concentration is increased and the efficiency of early ant search is improved. The obstacle avoidance strategy is added to avoid ant blind search. A large number of cross paths are generated and the number of ant deadlocks is effectively reduced. Based on the pseudorandom transfer strategy of dynamic parameter control, the global performance of the algorithm is improved. The updating principle of high quality ant pheromone and adjusting the volatility coefficient adaptively are proposed. The second path planning is carried out to optimize the path and reduce the loss of energy consumption of mobile robots. Experimental results show that the algorithm has the feature of higher global searching ability, faster convergence speed and higher working efficiency of mobile robot. The proposed algorithm is verified to be effective and superior.