论文标题
Alpha-N:最短的路径Finder自动递送机器人,带有障碍物检测和避免系统
Alpha-N: Shortest Path Finder Automated Delivery Robot with Obstacle Detection and Avoiding System
论文作者
论文摘要
本文介绍了Alpha n自动式车轮驱动的自动交付机器人。 ADR能够通过检测和避免其路径中的物体或障碍来自主浏览。它使用路径的向量图,并通过Dijkstra算法的网格计数方法计算最短路径。具有射频标识标签的地标确定位于路径中,以识别和验证源和目的地,以及重新校准当前位置。另一方面,通过使用VGGNET16体系结构更快的RCNN构建对象检测模块,用于通过检测和识别障碍来支持路径计划。路径计划系统与GCM的输出,RFID阅读系统以及ODM的二进制结果结合使用。该PPS要求机器人的最低速度为200 rpm和75秒的持续时间,以通过读取RFID标签成功地重新安置其位置。在结果分析阶段,ODM的精度为83.75%,RRS的精度为92.3%,PPS的精度为85.3%。堆叠所有这三个模块,已建立,测试和验证ADR,与其他服务机器人相比,性能和可用性方面显示出显着改善。
Alpha N A self-powered, wheel driven Automated Delivery Robot is presented in this paper. The ADR is capable of navigating autonomously by detecting and avoiding objects or obstacles in its path. It uses a vector map of the path and calculates the shortest path by Grid Count Method of Dijkstra Algorithm. Landmark determination with Radio Frequency Identification tags are placed in the path for identification and verification of source and destination, and also for the recalibration of the current position. On the other hand, an Object Detection Module is built by Faster RCNN with VGGNet16 architecture for supporting path planning by detecting and recognizing obstacles. The Path Planning System is combined with the output of the GCM, the RFID Reading System and also by the binary results of ODM. This PPS requires a minimum speed of 200 RPM and 75 seconds duration for the robot to successfully relocate its position by reading an RFID tag. In the result analysis phase, the ODM exhibits an accuracy of 83.75 percent, RRS shows 92.3 percent accuracy and the PPS maintains an accuracy of 85.3 percent. Stacking all these 3 modules, the ADR is built, tested and validated which shows significant improvement in terms of performance and usability comparing with other service robots.