SWARM UAV IMPLEMENTATION USING RADIO LOCALIZATION ON GPS DENIED AREAS

Authors

  • Timothy Scott Chu Department of Mechanical Engineering, De La Salle University, 2401 Taft Ave, Malate, Manila, 1004 Philippines
  • Alvin Chua Department of Mechanical Engineering, De La Salle University, 2401 Taft Ave, Malate, Manila, 1004 Philippines
  • Marc Say Department of Electronics and Computer Engineering, De La Salle University, 2401 Taft Ave, Malate, Manila, 1004 Philippines
  • Edwin Sybingco Department of Electronics and Computer Engineering, De La Salle University, 2401 Taft Ave, Malate, Manila, 1004 Philippines
  • Maria Antonette Roque Department of Electronics and Computer Engineering, De La Salle University, 2401 Taft Ave, Malate, Manila, 1004 Philippines

DOI:

https://doi.org/10.11113/aej.v12.17841

Keywords:

Crazyflie, Loco Positioning System, Radio Localization, Swarm Drone, Unmanned Aerial Vehicle

Abstract

Swarming is a rapidly growing idea that is being implemented in UAV applications. Its effectiveness and efficiency in finding solutions or executing the desired task served as the main motivation for this study. Localization techniques are vital for swarm implementation and deployment since it one of the main determining factors in its performance. Vision systems have been widely used for localization; however, it may be costly as it requires multiple appropriate cameras. Another localization technique, which is explored in this research, is radio localization. This localization employs Ultrawide-Band radios to communicate with each other to return a target’s position with respect to several reference points.  The study presents a new collaborative UAV implementation deployed using radio localized systems for harsh or unknown environments. The study used the Loco Positioning System operating on the Time Difference of Arrival protocol to maneuver two UAVs in a workspace. The study determined how well the system can execute the desired flight path and the performance of the system in keeping the set distance between UAVs to avoid possible collisions. Results of the study showed that the proposed implementation was successful in maneuvering the UAVs flying 0.3 m apart.

References

Almeida, M., Hildmann, H., & Solmaz, G. 2017. Distributed UAV-swarm-based real-time geomatic data collection under dynamically changing resolution requirements. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 42: 5. DOI: 10.5194/isprs-archives-xlii-2-w6-5-2017

Apvrille, L., Tanzi, T., & Dugelay, J. L. 2014. Autonomous drones for assisting rescue services within the context of natural disasters. In 2014 XXXIth URSI General Assembly and Scientific Symposium (URSI GASS) 1-4. IEEE. DOI: 10.1109/URSIGASS.2014.6929384.

Montufar, D. I., Munoz, F., Espinoza, E. S., Garcia, O., & Salazar, S. 2014. Multi-UAV testbed for aerial manipulation applications. In 2014 International Conference on Unmanned Aircraft Systems (ICUAS) 830-835. IEEE. DOI: 10.1109/ICUAS.2014.6842329.

Karaca, Y., Cicek, M., Tatli, O., Sahin, A., Pasli, S., Beser, M. F., & Turedi, S. 2018. The potential use of unmanned aircraft systems (drones) in mountain search and rescue operations. The American Journal Of Emergency Medicine, 36(4): 583-588.DOI: 10.1016/j.ajem.2017.09.025.

Tahir, A., Böling, J., Haghbayan, M. H., Toivonen, H. T., & Plosila, J. 2019. Swarms of unmanned aerial vehicles—a survey. Journal of Industrial Information Integration, 16, 100106. DOI: 10.1016/j.jii.2019.100106.

Cummings, M. L., Bertucelli, L. F., Macbeth, J., & Surana, A. 2014. Task versus vehicle-based control paradigms in multiple unmanned vehicle supervision by a single operator. IEEE Transactions on Human-Machine Systems, 44(3), 353-361. DOI: 10.1109/THMS.2014.2304962.

Dim, C., Nabor, F., Santos, G., Schoeler, M., & Chua, A. 2019. Novel experiment design for unmanned aerial vehicle controller performance testing. In IOP Conference Series: Materials Science and Engineering 533(1): 012026. IOP Publishing. DOI: 10.1088/1757-899x/533/1/012026.

Piquero, J. L. P., Delica, V. K., Orquia, A. L., Reynaldo, E. M., Ilao, J., Roque, M. A., ... & Jayakody, H. 2019. A new sliding mode controller implementation on an autonomous quadcopter system. International Journal of Automation and Smart Technology, 9(2), 53-63.

Mueller, M. W. 2018. A dynamics-agnostic state estimator for unmanned aerial vehicles using ultra-wideband radios. In Dynamic Systems and Control Conference 51913: V003T36A002. American Society of Mechanical Engineers.

Fahandezh-Saadi, S., & Mueller, M. W. 2018. An algorithm for real-time restructuring of a ranging-based localization network. In 2018 International Conference on Unmanned Aircraft Systems (ICUAS) 236-242. IEEE. DOI: 10.1109/ICUAS.2018.8453294.

Badshah, A., Islam, N., Shahzad, D., Jan, B., Farman, H., Khan, M., ... & Ahmad, A. 2019. Vehicle navigation in GPS denied environment for smart cities using vision sensors. Computers, Environment and Urban Systems, 77: 101281.

Ahmed, H., & Glasgow, J. 2012. Swarm intelligence: concepts, models and applications. School Of Computing, Queens University Technical Report 2012-585.

Preiss, J. A., Honig, W., Sukhatme, G. S., & Ayanian, N. 2017. Crazyswarm: A large nano-quadcopter swarm. In 2017 IEEE International Conference on Robotics and Automation (ICRA). 3299-3304. IEEE.

Buffi, A., Nepa, P., & Cioni, R. 2017. SARFID on drone: Drone-based UHF-RFID tag localization. In 2017 IEEE International Conference on RFID Technology & Application (RFID-TA) 40-44. IEEE. DOI: 10.1109/RFID-TA.2017.8098872.

Chu, T., Chua, A., Sybingco, E., & Roque, M. 2019. A Performance Analysis on Swarm Drone Loco Positioning System for Time Difference of Arrival Protocol. International Journal of Engineering and Advanced Technology, 9(2): 1475-1485. DOI: 10.35940/ijeat.b3480.129219

Chen, H., Xian-Bo, W., Liu, J., Wang, J., & Ye, W. 2020. Collaborative multiple UAVs navigation with GPS/INS/UWB jammers using sigma point belief propagation. IEEE Access, 8: 193695-193707. DOI: 10.1109/ACCESS.2020.3031605.

Sivaneri, V. O., & Gross, J. N. (2017). UGV-to-UAV cooperative ranging for robust navigation in GNSS-challenged environments. Aerospace science and technology, 71: 245-255. DOI: 10.1016/j.ast.2017.09.024.

Dädeby, S., & Hesselgren, J. 2017. A system for indoor positioning using ultra-wideband technology (Master's thesis). Dept. of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden.

Loco Positioning system. Bitcraze. 2020. Retrieved June 27, 2020, from https://www.bitcraze.io/documentation/system/positioning/loco-positioning-system/.

Loco Positioning TDoA Principles. Bitcraze. 2020. Retrieved September 17, 2020, from https://www.bitcraze.io/documentation/repository/lps-node-firmware/2020.09/functional-areas/tdoa_principles/.

TDoA2 vs TDoA3. Bitcraze. 2020. Retrieved September 17, 2020, from https://www.bitcraze.io/documentation/repository/lps-node-firmware/master/functional-areas/tdoa2-vs-tdoa3/.

Crazyflie 2.1. Bitcraze. 2019. Retrieved September 10, 2019, from https://www.bitcraze.io/crazyflie-2-1/.

Loco Positioning Node. Bitcraze. 2019. Retrieved September 10, 2019, from https://www.bitcraze.io/loco-pos-node/.

Loco Positioning Deck. Bitcraze. 2019. Retrieved September 10, 2020, from https://www.bitcraze.io/loco-pos-deck/.

Downloads

Published

2022-08-31

How to Cite

Scott Chu, T., Chua, A. ., Say, M. ., Sybingco, E. ., & Roque, M. A. . (2022). SWARM UAV IMPLEMENTATION USING RADIO LOCALIZATION ON GPS DENIED AREAS. ASEAN Engineering Journal, 12(3), 111-116. https://doi.org/10.11113/aej.v12.17841

Issue

Section

Articles