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An improved version of Alpha-N, a self-powered, wheel-driven Automated Delivery Robot (ADR), is presented in this study. Alpha-N-V2 is capable of navigating autonomously by detecting and avoiding objects or obstacles in its path. For autonomous navigation and path planning, Alpha-N uses a vector map and calculates the shortest path by Grid Count Method (GCM) of Dijkstra’s Algorithm. The RFID Reading System (RRS) is assembled in Alpha-N to read Landmark determination with Radio Frequency Identification (RFID) tags. With the help of the RFID tags, Alpha-N verifies the path for identification between source and destination and calibrates the current position. Along with the RRS, GCM, to detect and avoid obstacles, an Object Detection Module (ODM) is constructed by Faster R-CNN with VGGNet-16 architecture that builds and supports the Path Planning System (PPS). In the testing phase, the following results are acquired from the Alpha-N: ODM exhibits an accuracy of 89.25%, RRS shows 93.2% accuracy and the PPS maintains the accuracy of 87.5%. This proposed version of Alpha-N shows significant improvement in terms of performance and usability compared with the previous version of Alpha-N.

An improved version of Alpha-N, a self-powered, wheel-driven Automated Delivery Robot (ADR), is presented in this study. Alpha-N-V2 is capable of navigating autonomously by detecting and avoiding objects or obstacles in its path. For autonomous navigation and path planning, Alpha-N uses a vector map and calculates the shortest path by Grid Count Method (GCM) of Dijkstra's Algorithm. The RFID Reading System (RRS) is assembled in Alpha-N to read Landmark determination with Radio Frequency Identification (RFID) tags. With the help of the RFID tags, Alpha-N verifies the path for identification between source and destination and calibrates the current position. Along with the RRS, GCM, to detect and avoid obstacles, an Object Detection Module (ODM) is constructed by Faster R-CNN with VGGNet-16 architecture that builds and supports the Path Planning System (PPS). In the testing phase, the following results are acquired from the Alpha-N: ODM exhibits an accuracy of 8 9. 2 5 % , RRS shows 9 3. 2 % accuracy and the PPS maintains the accuracy of 8 7. 5 %. This proposed version of Alpha-N shows significant improvement in terms of performance and usability compared with the previous version of Alpha-N. [ABSTRACT FROM AUTHOR]