Home⇛International Journal of Multidisciplinary: Applied Business and Education Research⇛vol. 7 no. 5 (2026)

Development of Retrievr: A Line-Of-Sight Beacon System for Autonomous Object Return in Indoor Workspaces

Kevin C. Gaza | Baron Terence S. Mijares | Christine Paula C. Rodel | Eliza B. Ayo

Discipline: software engineering

 

Abstract:

This study presents the design, development, and evaluation of Re- trierv, a wheeled autonomous device engineered to return loaded ob- jects to their designated locations using Bluetooth Low Energy (BLE) line-of-sight beacon technology. Persistent challenges of object mis- placement and spatial disorganization in indoor workspaces motivated this work. Retrierv integrates BLE modules for proximity-based posi- tioning, a Force Sensitive Resistor (FSR) sensor for occupancy detec- tion, and ultrasonic sensors for obstacle avoidance. A descriptive-devel- opmental research design guided by Agile methodology was employed, with iterative prototype development and evaluation across multiple performance dimensions. Testing revealed a mean positional error of 2.72 cm, a maximum load capacity of 11.34 kg (25 lbs), and a mean nav- igation speed of 0.26 m/s. Quality assurance evaluation demonstrated high usability (System Usability Scale score of 82), strong occupancy de- tection reliability (98% accuracy), and effective obstacle avoidance (91% detection rate). However, reliability testing showed a Mean Time Between Failures of 42 hours, below the target threshold of 100 hours-an area needing refinement. Retrierv provides a viable and cost-effective solution for automated object repositioning, performing best in controlled indoor environments with hard flooring and moder- ate obstacle density.



References:

  1. Bhagat, M., Khedkar, C., Chakole, K., Choudhari, H., & Mungale, S. (2022). Arduino based self-parking chair. International Journal of Scientific Research in Science and Technol-ogy, 137–139. https://doi.org/10.32628/ijsrst229326">https://doi.org/10.32628/ijsrst229326">https://doi.org/10.32628/ijsrst229326
  2. De Jong Yeong, D., Velasco-Hernandez, G., Barry, J., & Walsh, J. (2021). Sensor and sensor fusion technology in autonomous vehicles: A review. Sensors, 21(6), 2140. https://doi.org/10.3390/s21062140">https://doi.org/10.3390/s21062140">https://doi.org/10.3390/s21062140
  3. García, J. M., & Duarte, F. G. (2024). Mobile roll-ing robots designed to overcome obsta-cles: A review. Forces in Mechanics, 16, 100283. https://doi.org/10.1016/j.finmec.2024.100283">https://doi.org/10.1016/j.finmec.2024.100283">https://doi.org/10.1016/j.finmec.2024.100283
  4. Gholami, F., Khanmirza, E., & Riahi, M. (2022). Real-time obstacle detection by stereo vi-sion and ultrasonic data fusion. Measure-ment, 190, 110718. https://doi.org/10.1016/j.measure-ment.2022.110718">https://doi.org/10.1016/j.measure-ment.2022.110718">https://doi.org/10.1016/j.measure-ment.2022.110718
  5. Haris, M., & Hou, J. (2020). Obstacle detection and safely navigate the autonomous vehi-cle from unexpected obstacles on the driv-ing lane. Sensors, 20(17), 4719. https://doi.org/10.3390/s20174719">https://doi.org/10.3390/s20174719">https://doi.org/10.3390/s20174719
  6. He, T., Zhang, Y.-P., Yan, M.-Z., Luo, W.-P., & Ma, S.-B. (2025). A hierarchical DV-Hop locali-zation algorithm based on RSSI with self-localization weighted offset correction. Ad Hoc Networks, 173, 103832. https://doi.org/10.1016/j.adhoc.2025.103832">https://doi.org/10.1016/j.adhoc.2025.103832">https://doi.org/10.1016/j.adhoc.2025.103832
  7. Hu, J., Zheng, B., Wang, C., Zhao, C., Hou, X., Pan, Q., & Xu, Z. (2020). A survey on multi-sen-sor fusion based obstacle detection for in-telligent ground vehicles in off-road envi-ronments. Frontiers of Information Tech-nology & Electronic Engineering, 21(5), 675–692. https://doi.org/10.1631/fitee.1900518">https://doi.org/10.1631/fitee.1900518">https://doi.org/10.1631/fitee.1900518
  8. Janczak, D., Walendziuk, W., Sadowski, M., Zankiewicz, A., Konopko, K., & Idzkowski, A. (2022). Accuracy analysis of the indoor location system based on Bluetooth Low-Energy RSSI measurements. Energies, 15(23), 8832. https://doi.org/10.3390/en15238832">https://doi.org/10.3390/en15238832">https://doi.org/10.3390/en15238832
  9. Kan, X., Teng, H., & Karydis, K. (2020). Online exploration and coverage planning in un-known obstacle-cluttered environments. IEEE Robotics and Automation Letters, 5(4), 5969–5976. https://doi.org/10.1109/lra.2020.3010455">https://doi.org/10.1109/lra.2020.3010455">https://doi.org/10.1109/lra.2020.3010455
  10. Liao, M., Wang, D., & Yang, H. (2019). Deploy in-door 2D laser SLAM on a Raspberry Pi-based mobile robot. In 2019 11th Interna-tional Conference on Intelligent Human-Machine Systems and Cybernetics (Vol. 35, pp. 7–10). IEEE. https://doi.org/10.1109/ihmsc.2019.10097">https://doi.org/10.1109/ihmsc.2019.10097">https://doi.org/10.1109/ihmsc.2019.10097
  11. Liu, H., Zhang, S., & Yang, X. (2024). Overview of path planning algorithms. Recent Patents on Engineering, 18(7). https://doi.org/10.2174/1872212118666230828150857">https://doi.org/10.2174/1872212118666230828150857">https://doi.org/10.2174/1872212118666230828150857
  12. Panpan, X., Zhicheng, W., Hongbin, R., & Yang, W. (2023). Vehicle autonomous return based on 2D grid map. In 2023 IEEE 18th Conference on Industrial Electronics and Applications (pp. 1803–1808). IEEE. https://doi.org/10.1109/iciea58696.2023.10241482">https://doi.org/10.1109/iciea58696.2023.10241482">https://doi.org/10.1109/iciea58696.2023.10241482
  13. Poveda-García, M., Gómez-Alcaraz, A., Cañete-Rebenaque, D., Martínez-Sala, A. S., & Gómez-Tornero, J. L. (2020). RSSI-based direction-of-departure estimation in Bluetooth Low Energy using an array of frequency-steered leaky-wave antennas. IEEE Access, 8, 9380–9394. https://doi.org/10.1109/ac-cess.2020.2965233">https://doi.org/10.1109/ac-cess.2020.2965233">https://doi.org/10.1109/ac-cess.2020.2965233
  14. Ramirez, R., Huang, C.-Y., Liao, C.-A., Lin, P.-T., Lin, H.-W., & Liang, S.-H. (2021). A practice of BLE RSSI measurement for indoor posi-tioning. Sensors, 21(15), 5181. https://doi.org/10.3390/s21155181">https://doi.org/10.3390/s21155181">https://doi.org/10.3390/s21155181
  15. Reda, M., Onsy, A., Haikal, A. Y., & Ghanbari, A. (2024). Path planning algorithms in the autonomous driving system: A compre-hensive review. Robotics and Autono-mous Systems, 174, 104630. https://doi.org/10.1016/j.ro-bot.2024.104630">https://doi.org/10.1016/j.ro-bot.2024.104630">https://doi.org/10.1016/j.ro-bot.2024.104630
  16. Ren, Q., Zhang, Y., Nikolaidis, I., Li, J., & Pan, Y. (2020). RSSI quantization and genetic al-gorithm based localization in wireless sensor networks. Ad Hoc Networks, 107, 102255. https://doi.org/10.1016/j.adhoc.2020.102255">https://doi.org/10.1016/j.adhoc.2020.102255">https://doi.org/10.1016/j.adhoc.2020.102255
  17. Sadowski, S., & Spachos, P. (2019). Optimiza-tion of BLE beacon density for RSSI-based indoor localization. In 2019 IEEE Interna-tional Conference on Communications Workshops (pp. 1–6). IEEE. https://doi.org/10.1109/iccw.2019.8756989">https://doi.org/10.1109/iccw.2019.8756989">https://doi.org/10.1109/iccw.2019.8756989
  18. Sonali, V., Gawali, G., & Wanare, A. L. (2019). Smart self-parking chair. International Journal of Research in Engineering, Sci-ence and Management, 2(6). https://www.ijr-esm.com/Vol.2_2019/Vol2_Iss6_June19/IJRESM_V2_I6_15.pdf">https://www.ijr-esm.com/Vol.2_2019/Vol2_Iss6_June19/IJRESM_V2_I6_15.pdf">https://www.ijr-esm.com/Vol.2_2019/Vol2_Iss6_June19/IJRESM_V2_I6_15.pdf
  19. Vinubharathi, R., et al. (2019). Raspberry Pi based self alignment chair. IOSR Journal of Electronics and Communication Engi-neering, 14(3), 1–8.
  20. Volden, Ø., Stahl, A., & Fossen, T. I. (2023). De-velopment and experimental validation of visual-inertial navigation for auto-dock-ing of unmanned surface vehicles. IEEE Access, 11, 45688–45710. https://doi.org/10.1109/ac-cess.2023.3274597">https://doi.org/10.1109/ac-cess.2023.3274597">https://doi.org/10.1109/ac-cess.2023.3274597
  21. Xu, Z., Liu, B., Xiao, X., Nair, A., & Stone, P. (2023). Benchmarking reinforcement learning techniques for autonomous navi-gation. In 2023 IEEE International Confer-ence on Robotics and Automation. IEEE. https://doi.org/10.1109/icra48891.2023.10160583">https://doi.org/10.1109/icra48891.2023.10160583">https://doi.org/10.1109/icra48891.2023.10160583
  22. Yao, H., Shu, H., Liang, X., Yan, H., & Sun, H. (2020). Integrity monitoring for Blue-tooth Low Energy beacons RSSI based in-door positioning. IEEE Access, 8, 215173– 215191. https://doi.org/10.1109/ac-cess.2020.3038894">https://doi.org/10.1109/ac-cess.2020.3038894">https://doi.org/10.1109/ac-cess.2020.3038894
  23. Yedilkhan, D., Kyzyrkanov, A. E., Kutpanova, Z. A., Aljawarneh, S., & Atanov, S. K. (2024). Intelligent obstacle avoidance algorithm for safe urban monitoring with autono-mous mobile drones. Journal of Electronic Science and Technology, 22(4), 100277. https://doi.org/10.1016/j.jnlest.2024.100277">https://doi.org/10.1016/j.jnlest.2024.100277">https://doi.org/10.1016/j.jnlest.2024.100277
  24. Yu, X., & Marinov, M. (2020). A study on recent developments and issues with obstacle detection systems for automated vehicles. Sustainability, 12(8), 3281. https://doi.org/10.3390/su12083281">https://doi.org/10.3390/su12083281">https://doi.org/10.3390/su12083281
  25. Zhang, J., Wu, J., Shen, X., & Li, Y. (2021). Auton-omous land vehicle path planning algo-rithm based on improved heuristic func-tion of A-Star. International Journal of Ad-vanced Robotic Systems, 18(5). https://doi.org/10.1177/17298814211042730">https://doi.org/10.1177/17298814211042730">https://doi.org/10.1177/17298814211042730
  26. Zhang, R., Guo, H., Andriukaitis, D., Li, Y., Kró-lczyk, G., & Li, Z. (2024). Intelligent path planning by an improved RRT algorithm with dual grid map. Alexandria Engineer-ing Journal, 88, 91–104. https://doi.org/10.1016/j.aej.2023.12.044">https://doi.org/10.1016/j.aej.2023.12.044">https://doi.org/10.1016/j.aej.2023.12.044
  27. Zhao, J., Liu, S., & Li, J. (2022). Research and im-plementation of autonomous navigation for mobile robots based on SLAM algo-rithm under ROS. Sensors, 22(11), 4172. https://doi.org/10.3390/s22114172">https://doi.org/10.3390/s22114172">https://doi.org/10.3390/s22114172
  28. Zhuang, Y., Sun, X., Li, Y., Huai, J., Hua, L., Yang, X., Cao, X., Zhang, P., Cao, Y., Qi, L., Yang, J., El-Bendary, N., El-Sheimy, N., Thompson, J., & Chen, R. (2023). Multi-sensor inte-grated navigation/positioning systems using data fusion: From analytics-based to learning-based approaches. Information Fusion, 95, 62–90. https://doi.org/10.1016/j.inffus.2023.01.025

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