中文摘要：

针对在动态环境下移动机器人用传统人工势场法导航所存在的缺陷,在改进传统人工势场的基础上,引入协调向量,利用子目标点构建局部势场,并通过窗口滚动刷新子目标点实现全局优化,对运动过程中可能遇到的陷阱、抖动、实时避障等问题,提出了解决方案,最后利用自适应遗传算法对参数进行的多目标优化,经过仿真,证明了该策略的可行性和有效性。

英文摘要：

To overcome the problems during navigation of mobile robots in dynamic environment using the traditional artificial potential field （APF） method, a novel improved method called coordinating potentialfield （CPF） was proposed. The local potential field was constructed by using local subgoals, which was obtained by updating dynamic rolling window. The questions of local minima, oscillation between multiple obstacles and real-time dynamic obstacles avoidance were solved. At last multi-objective parameter optimization was implemented by using adaptive genetic algorithm. Simulation results indicate that the strategy is feasible and practicable.