CAJ | 학술논문

如何保证在未知复杂环境下规划出的机器人路径全局最优或较优一直是这一领域的一个研究难题,将混沌理论和遗传算法相结合,提出了一种新颖的基于自适应混沌遗传算法的机器人路径规划算法.利用信息熵产生初始群体,增加初始群体的多样性,将混沌优化的遍历特性引入遗传算法,以防止和克服进化过程中的“早熟”现象.仿真实验表明,即使在复杂的未知环境下,利用该算法也可以规划出一条全局优化路径,且能安全避碰.
여하보증재미지복잡배경하규화출적궤기인로경전국최우혹교우일직시저일영역적일개연구난제,장혼돈이론화유전산법상결합,제출료일충신영적기우자괄응혼돈유전산법적궤기인로경규화산법.이용신식적산생초시군체,증가초시군체적다양성,장혼돈우화적편력특성인입유전산법,이방지화극복진화과정중적“조숙”현상.방진실험표명,즉사재복잡적미지배경하,이용해산법야가이규화출일조전국우화로경,차능안전피팽.
A difficult issue of robot path planning in a cluttered environment is that planned path is global optimal. A new robot path planning based on adaptive chaotic genetic algorithm is presented by integrating chaotic and genetic algorithm. This algo-rithm produces the initial colony by information entropy to increase the variety of the initial colony, and introduces the traversal characteristic of chaos optimization to the integrated genetic algorithm to prevent and overcome premature phenomena in the evolutionary process. Computer experimental results demonstrate that the proposed algorithm can be used to solve the path plan-ning for mobile robot even in the complex unknown environment, and the successful obstacle avoidance is also achieved.