CAJ | 학술논문

针对传统的蚁群算法设计机器人避障路径规划，自适应能力差，全局优化能力和搜索速度不好的问题，在传统算法的基础上，提出一种采用奖惩规则格栅建模的机器人避障规划算法。提出构建模型主体的行为规则和避障规则，通过在栅格环境中设置量子遗传进化的多个有效的行为规则，设计了信息素更新的奖惩规则，修改其路径上的信息素，改变量子本身携带的信息素，得到优化避障最小距离。最终获得了复杂环境下的最优路径。仿真实验表明采用该算法进行机器人避障路径规划，在未知复杂环境下能够快速地规划出安全的优化路径，机器人避障路径规划具有很好的自适应性，相比传统的蚁群算法，其全局优化能力和搜索速度都得到了显著提高。
침대전통적의군산법설계궤기인피장로경규화，자괄응능력차，전국우화능력화수색속도불호적문제，재전통산법적기출상，제출일충채용장징규칙격책건모적궤기인피장규화산법。제출구건모형주체적행위규칙화피장규칙，통과재책격배경중설치양자유전진화적다개유효적행위규칙，설계료신식소경신적장징규칙，수개기로경상적신식소，개변양자본신휴대적신식소，득도우화피장최소거리。최종획득료복잡배경하적최우로경。방진실험표명채용해산법진행궤기인피장로경규화，재미지복잡배경하능구쾌속지규화출안전적우화로경，궤기인피장로경규화구유흔호적자괄응성，상비전통적의군산법，기전국우화능력화수색속도도득도료현저제고。
In view of the traditional ant colony algorithm design of the robot obstacle avoidance path planning, the adaptive ability is poor, bad and search speed of global optimization ability, on the basis of the traditional algorithm, put forward a kind of use rewards and punishment rules grid model of robot obstacle avoidance planning algorithm. Model is put forward the main body of the behavior and obstacle avoidance rules, through setting quantum genetic evolution in grid environment more effectively behavior rules, the design of the pheromone updating rule of rewards and punishments to modify its path pheromone, change quantum itself carries a pheromone, optimized minimum distance of obstacle avoidance. Finally ob-tained the optimal path in a complicated environment. Simulation results show that using the algorithm to robot obstacle avoidance path planning, able to quickly plan in unknown environment safety optimization path, robot obstacle avoidance path planning has the very good adaptability, compared with the traditional ant colony algorithm, the global optimization ability and to improve the search speed significantly.