自适应蚁群算法的移动机器人路径规划

针对传统蚁群算法在路径规划中存在收敛速度和寻优能力不平衡,算法易陷入局部最优等问题,提出一种自适应改进蚁群算法。为了提高算法收敛速度,在栅格环境下,根据最优路径的特点以及实际环境地图的基本参数,对初始信息素进行差异化分配;为了提高蚂蚁搜索效率,在状态转移概率中引入转角启发信息并对路径启发信息进行改进;重新制定信息素更新策略,设定迭代阈值,调整信息素挥发系数和信息素浓度,使算法在迭代后期依然具有较强的搜索最优解能力;采用分段三阶贝塞尔曲线对最优路径进行平滑处理以满足机器人实际运动要求。通过实验仿真与其他算法进行对比分析,验证了改进算法的可行性、有效性和优越性。

Mobile Robot Path Planning Based on Adaptive Ant Colony Algorithm

As for original ant colony algorithm using in mobile robot path planning, there are problems such as the convergence speed and searching ability existing unbalance, falling into local optimum easily and so on. In this paper, an adaptive improved ant colony algorithm is proposed. Firstly, in order to improve convergence speed of the algorithm, in the grid environment the initial pheromone is differentiate distributed according to the characteristics of the optimal path and the basic parameters of the actual environment. Secondly, in order to raise the efficiency of ants search, the turn-angle heuristic information is introduced into the state transition probability and the path heuristic information is improved. Thirdly, it reformulates the strategy of pheromone renewal, sets the iteration threshold, and the pheromone volatilization coefficients and pheromone concentration are adjusted, so that at the later stage of iteration, the algorithm still has a strong ability to search for the optimal solution. Finally, the piecewise third-order Besizer curve is used to smooth the optimal path to meet the actual motion requirements of the robot. The feasibility, effectiveness and superiority of the improved algorithm are verified by comparing with other algorithms.