Alpha-N: Shortest Path Finder Automated Delivery Robot with Obstacle Detection and Avoiding System

  • Asif Ahmed NeloyEmail author
  • Rafia Alif Bindu
  • Sazid Alam
  • Ridwanul Haque
  • Md. Saif Ahammod Khan
  • Nasim Mahmud Mishu
  • Shahnewaz Siddique
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12033)


Alpha-N - A self-powered, wheel-driven Automated Delivery Robot (ADR) 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 (GCM) of Dijkstra’s Algorithm. Landmark determination with Radio Frequency Identification (RFID) tags are placed in the path for identification and verification of source and destination, and also for the re-calibration of the current position. On the other hand, an Object Detection Module (ODM) is built by Faster R-CNN with VGGNet-16 architecture for supporting path planning by detecting and recognizing obstacles. The Path Planning System (PPS) is combined with the output of the GCM, the RFID Reading System (RRS) and also by the binary results of ODM. This PPS requires a minimum speed of 200 RPM and 75 s 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%, RRS shows 92.3% accuracy and the PPS maintains an accuracy of 85.3%. 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.


Mobile robot Obstacle avoiding system RFID Automated Delivery Robot Dijkstra’s Algorithm Grid Count Method Faster R-CNN VGGNet-16 


  1. 1.
  2. 2.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1137–1149 (2017). Scholar
  3. 3.
    Pillai, S., Leonard, J.: Monocular SLAM supported object recognition.
  4. 4.
    Morales, Y., Carballo, A., Takeuchi, E., Aburadani, A., Tsubouchi, T.: Autonomous robot navigation in outdoor cluttered pedestrian walkways. J. Field Robot. 26, 609–635 (2009). Scholar
  5. 5.
    Neloy, A.A., Arman, A., Islam, M.S., Motahar, T.: Automated mobile robot with RFID scanner and self obstacle avoiding system. Int. J. Pure Appl. Math. 118(18), 3139–3150 (2018)Google Scholar
  6. 6.
    Wang, L., et al.: Multi-channel convolutional neural network based 3D object detection for indoor robot environmental perception. Sensors 19(4), 893 (2019). Scholar
  7. 7.
    Choi, J.-H., Choi, B.-J.: Indoor moving and implementation of a mobile robot using hall sensor and Dijkstra algorithm. IEMEK J. Embed. Syst. Appl. 14(3), 151–156 (2019). Scholar
  8. 8.
    Santos, D.H.D., Goncalves, L.M.G.: A gain-scheduling control strategy and short-term path optimization with genetic algorithm for autonomous navigation of a sailboat robot. Int. J. Adv. Rob. Syst. 16, 172988141882183 (2019). Scholar
  9. 9.
    Oishi, S., Inoue, Y., Miura, J., Tanaka, S.: SeqSLAM: view-based robot localization and navigation. Robot. Auton. Syst. 112, 13–21 (2019). Scholar
  10. 10.
    Kato, Y., Morioka, K.: Autonomous robot navigation system without grid maps based on double deep Q-network and RTK-GNSS localization in outdoor environments. In: 2019 IEEE/SICE International Symposium on System Integration (SII) (2019).
  11. 11.
    Wang, L.: Automatic control of mobile robot based on autonomous navigation algorithm. Artif. Life Robot. (2019). Scholar
  12. 12.
    Chou, J.-S., Cheng, M.-Y., Hsieh, Y.-M., Yang, I.-T., Hsu, H.-T.: Optimal path planning in real time for dynamic building fire rescue operations using wireless sensors and visual guidance. Autom. Constr. 99, 1–17 (2019). Scholar
  13. 13.
    Murtra, A.C., Tur, J.M.M., Sanfeliu, A.: Action evaluation for mobile robot global localization in cooperative environments. Robot. Auton. Syst. 56, 807–818 (2008). Scholar
  14. 14.
    Mac, T.T., Copot, C., Tran, D.T., Keyser, R.D.: Heuristic approaches in robot path planning: a survey. Robot. Auton. Syst. 86, 13–28 (2016). Scholar
  15. 15.
    Nazarahari, M., Khanmirza, E., Doostie, S.: Multi-objective multi-robot path planning in continuous environment using an enhanced genetic algorithm. Expert Syst. Appl. 115, 106–120 (2019). Scholar
  16. 16.
    Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014).
  17. 17.
    Gonzalez, E.M.A., Hermosilla, D.M.: Object detection algorithm for a mobile robot using Android. ITEGAM- J. Eng. Technol. Ind. Appl. (ITEGAM-JETIA) 4 (2018).
  18. 18.
    Holz, D., Topalidou-Kyniazopoulou, A., Rovida, F., Pedersen, M.R., Kruger, V., Behnke, S.: A skill-based system for object perception and manipulation for automating kitting tasks. In: 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA) (2015).
  19. 19.
    Wang, H., Yu, Y., Yuan, Q.: Application of Dijkstra algorithm in robot path-planning. In: 2011 Second International Conference on Mechanic Automation and Control Engineering (2011).
  20. 20.
    Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: You only look once: unified, real-time object detection. CoRR, abs/1506.02640 (2015)Google Scholar
  21. 21.
    Komar, M., Yakobchuk, P., Golovko, V., Dorosh, V., Sachenko, A.: Deep neural network for image recognition based on the Caffe framework. In: 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP) (2018).
  22. 22.
    Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012). Scholar
  23. 23.
    Song, S., Lichtenberg, S.P., Xiao, J.: SUN RGB-D: a RGB-D scene understanding benchmark suite. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015).
  24. 24.
    Van Beers, F., Lindström, A., Okafor, E., Wiering, M.: Deep neural networks with inter-section over union loss for binary image segmentation. In: Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods (2019).
  25. 25.
    Cho, S., Baek, N., Kim, M., Koo, J., Kim, J., Park, K.: Face detection in nighttime images using visible-light camera sensors with two-step faster region-based convolutional neural network. Sensors 18, 2995 (2018). Scholar
  26. 26.
    Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: a metric and a loss for bounding box regression. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 658–666 (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringNorth South UniversityDhakaBangladesh

Personalised recommendations