基于改进目标检测算法的AGV避障方法研究

AGV Obstacle Avoidance Method Based on Improved Target Detection Algorithm

目的针对目前定制家具打包运输过程AGV避障方法中,对障碍物信息辨识度较低、难以做到精准避障等问题,提出一种采用视觉传感器基于改进目标检测算法的AGV避障策略。方法利用Mobilenet模型改进传统SSD目标检测算法,以AGV工作环境数据对训练后的SSD-Mobilenet模型进行迁移学习,结合视觉、超声波等多个模块实现避障原理,搭建以树莓派3B+为控制核心的实验平台进行相关实验研究。结果实验证明该方法的检测精度达到94%,能够精准辨别障碍物类型;目标检测避障方法的避障通过时间比传统方法减少了15.8%~27.3%。结论该方法有效提高了AGV避障的准确率与效率,可广泛应用在AGV避障控制中。

The work aims to propose an AGV obstacle avoidance strategy adopting vision sensor based on improved target detection algorithm aiming at the problems of low identification of environmental information and difficulty in accurate obstacle avoidance in current AGV obstacle avoidance methods.The traditional SSD target detection algorithm was improved by Mobilenet model,and the trained SSD-Mobilenet model was transferred and learned by the AGV working environment data.The obstacle avoidance principle was realized by combining vision,ultrasound and other modules,and the experimental platform with Raspberry Pie 3B+as the control core was built for relevant experimental research.Experiments showed that the detection accuracy of this method was 94%,and it could accurately identify the types of obstacles.The obstacle avoidance time of the target detection method was 15.8%to 27.3%less than that of the traditional method.This method can effectively improve the accuracy and efficiency of AGV obstacle avoidance,and can be widely used in AGV obstacle avoidance control.