CAJ | 학술논문

目前,虽然有多种智能计算方法用于移动机器人路径规划问题,但在复杂环境下,多数智能计算方法表现出效率低下,结果较差的问题. 提出一种结合基于有效顶点的栅格编码法和改进的生物地理学优化算法的移动机器人路径规划方法,以解决该类问题. 结合已知的环境信息,从精英策略、降维机制和基于惯性算子的迁移操作3方面改进了生物地理学优化算法. 改进算法用于机器人移动路径,与人工蜂群算法、粒子群算法和人工鱼群算法等智能算法进行比较,实验的结果证实改进算法能够更有效地解决复杂环境下机器人路径规划问题.
목전,수연유다충지능계산방법용우이동궤기인로경규화문제,단재복잡배경하,다수지능계산방법표현출효솔저하,결과교차적문제. 제출일충결합기우유효정점적책격편마법화개진적생물지이학우화산법적이동궤기인로경규화방법,이해결해류문제. 결합이지적배경신식,종정영책략、강유궤제화기우관성산자적천이조작3방면개진료생물지이학우화산법. 개진산법용우궤기인이동로경,여인공봉군산법、입자군산법화인공어군산법등지능산법진행비교,실험적결과증실개진산법능구경유효지해결복잡배경하궤기인로경규화문제.
At present, there are many intelligent computing methods used in mobile robot path planning;however, in complex environments, most of them have low efficiency and poor results. In order to solve such problems, this paper proposes a new method for mobile robot path planning, which combines the grid coding method based on the effective vertex with the improved biogeography-based optimization ( BBO) . On the basis of the environmental infor-mation that has been learned, the BBO is improved in three aspects:elite strategies, dimension reduction mecha-nisms and migration based on inertial operator. The improved BBO is applied in path planning. The method is com-pared with artificial bee colony ( ABC) , particle swarm optimization ( PSO) and artificial fish algorithm ( AFA) . Experiment results show that the improved method can solve the problem of mobile robot path planning in a complex environment more efficiently.