Alpha-N：最短路径机器人自动送货机器人与障碍物检测和规避系统
Alpha-N: Shortest Path Finder Automated Delivery Robot with Obstacle Detection and Avoiding System
摘要：
本文介绍了一种自供电轮式自动送货机器人Alpha N A。ADR能够通过检测和避开路径中的物体或障碍物来自主导航，它使用路径的矢量地图，并通过Dijkstra算法的网格计数方法来计算最短路径。具有射频识别标签的地标确定被放置在路径中，用于源和目的地的识别和验证，并且还用于当前位置的重新校准。另一方面，一个目标检测模块是由VGGNet 16架构的Faster RCNN构建的，用于通过检测和识别障碍物来支持路径规划。路径规划系统与GCM的输出、RFID读取系统以及ODM的二进制结果相结合。该PPS要求机器人的最低速度为200 RPM，持续时间为75秒，以便通过读取RFID标签成功重新定位其位置。在结果分析阶段，ODM的准确率为83.75%，RRS的准确率为92.3%，PPS的准确率为85.3%。将所有这3个模块堆叠起来，构建、测试和验证了ADR，与其他服务机器人相比，ADR在性能和可用性方面都有显着提高。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
