Research Article
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Improvement of ultrasonic sensor-based obstacle avoidance system in drones

Year 2023, Volume: 4 Issue: 1, 9 - 35, 30.06.2023
https://doi.org/10.55212/ijaa.1261912

Abstract

Although drone users have received the necessary training, the reflexes of making decisions against a sudden natural event such as wind and avoiding a nearby obstacle may not be sufficient. Therefore, whether drones fly autonomously or under user control, they must sense and act accordingly for an uninterrupted mission. In this study, a drone design and application for obstacle detection and obstacle avoidance were carried out. In the designed drone, Pixhawk was used as the flight control board, ultrasonic sensors were used to detect obstacles, and Arduino Uno was used as a microcontroller to obtain avoidance commands. The sensors used in obstacle detection and their performance are the most decisive factors in achieving the targeted goal. Because obstacle detection sensors are affected by electrical noises, the success of detecting obstacles decreases. For this reason, first of all, the integration of these sensors into the system was investigated and the drone was developed accordingly. Then, an algorithm was developed using a software filtering method both to minimize communication instabilities and to increase the clarity of detection. Finally, the ability to evade obstacles both while the drone is suspended in the air and while it is in motion has been investigated. In the experiments carried out, it was found that the drone was able to successfully avoid obstacles up to a flight speed of 3.94 m/s.

Supporting Institution

Selçuk Üniversitesi Bilimsel Araştırma Projeleri (BAP) Koordinatörlüğü

Project Number

22201010

Thanks

This study was supported by Selcuk University Scientific Research Projects Coordination Office with project number 22201010.

References

  • Ayaz, M. Y., 2021."Dron için RF karıştırıcı tasarımı ve gerçekleştirilmesi," Yüksek Lisans, Konya Teknik Üniversitesi, Lisansüstü Eğitim Enstitüsü, Konya, Türkiye.
  • Özdemir, Ş., 2013."İnsansız hava araçlarında kullanılan fırçasız DC motorların kontrolü," Yüksek Lisans, Marmara Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul, Türkiye.
  • Sivritaş, Ş., 2020."Fast 3D Tracking Algorithm İmplementations On Mini Quard-copters," Yüksek Lisans, Özyeğin Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul, Türkiye.
  • Çakıcı, E., 2019."Mini insansız hava araçları için bir fırçasız motor test sistemi geliştirilmesi," Yüksek Lisans, ESOGÜ, Fen Bilimleri Enstitüsü, Eskişehir, Türkiye.
  • Al-Tekreeti, M. M. A., 2019."Unmanned surface vehicle (USV) with obstacle avoidance system," Yüksek Lisans, Çankaya Üniversitesi, Fen Bilimleri Enstitüsü, Ankara, Türkiye.
  • Çalış, M., 2019."Ultrasonik mesafe ölçümü ve doğrulanması," Yüksek Lisans, Gazi Üniversitesi, Fen Bilimleri Enstitüsü, Ankara, Türkiye.
  • Kale, A. A. 2021. Drone Technology and Its Applications, SSRN, doi: http://dx.doi.org/10.2139/ssrn.3922787
  • Venkatesh, S., Kundan, S., and Srija, A. 2021. Obsatacle Avoidance Robotic Vehicle Using Hc-Sr04 Ultrasonic Sensor, Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(12), 2358-2362.
  • Tayebi, A. and McGilvray, S. 2006. Attitude stabilization of a VTOL quadrotor aircraft, IEEE Transactions on control systems technology, 14(3), 562-571.
  • Abd Rahman, Y. A., Hajibeigy, M. T., Al-Obaidi, A. S. M., and Cheah, K. H. 2018. Design and fabrication of small vertical-take-off-landing unmanned aerial vehicle. MATEC Web of Conferences, 152(2018), 26 February, Selangor, Malaysia.
  • Wubben, J., Fabra, F., Calafate, C. T., Krzeszowski, T., Marquez-Barja, J. M., Cano, J.-C., and Manzoni, P. 2019. Accurate landing of unmanned aerial vehicles using ground pattern recognition, Electronics, 8(12), 1532.
  • Ekmen, M. İ., 2021."Drone sürüsü ile hedef takip optimizasyonu," Yüksek Lisans, Konya Teknik Üniversitesi, Lisansüstü Eğitim Enstitüsü, Konya, Türkiye.
  • Yiğit, A., 2020."Adaptive Large Neighbourhood Search Heuristic On Vehicle Routing Problem With Drones And Time Windows," Yüksek Lisans, Boğaziçi Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul, Türkiye.
  • Önler, E., 2012."Ultrasonik sensör yardımıyla belirlenen yaprak yoğunluğuna göre püskürtme miktarını ayarlayan sistemin tasarımı (akıllı ilaçlama makinası)," Yüksek Lisans, Namık Kemal Üniversitesi, Fen Bilimleri Enstitüsü, Tekirdağ, Türkiye.
  • Selim, E., Uyar, E., and Alcı, M. 2013. Quadrocopterin matematiksel modeli ve kontrolü, TOK2013-Otomatik Kontrol Ulusal Toplantısı, 548-553.
  • Köz, S., 2019."Yangın Topu Kullanılarak Yangın Söndüren Quadrocopter Tasarımı Ve Prototip İmalatı," Yüksek Lisans, Trakya Üniversitesi, Fen Bilimleri Enstitüsü, Edirne, Türkiye.
  • Kim, J., Choi, Y., Jeon, S., Kang, J., and Cha, H. 2020. Optrone: Maximizing performance and energy resources of drone batteries, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 39(11), 3931-3943.
  • Abdilla, A., Richards, A., and Burrow, S. 2015. Power and endurance modelling of battery-powered rotorcraft. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)): IEEE, 28 September - 02 October, Hamburg, Germany, 675-680.
  • Maekawa, K., Negoro, S., Taniguchi, I., and Tomiyama, H. 2017. Power measurement and modeling of quadcopters on horizontal flight. Fifth International Symposium on Computing and Networking (CANDAR)): IEEE, 326-329.
  • Chen, Y., Baek, D., Bocca, A., Macii, A., Macii, E., and Poncino, M. 2018. A case for a battery-aware model of drone energy consumption. IEEE International Telecommunications Energy Conference (INTELEC)): IEEE, 07-11 October, Turino, Italy, 1-8.
  • He, L. and Pace, J. 2020. Estimating Altitude of Drones Using Batteries. IEEE/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI)): IEEE, 21-24 April, Sydney, NSW, Australia, 264-265.
  • Park, S., Zhang, L., and Chakraborty, S. 2017. Battery assignment and scheduling for drone delivery businesses. 2017 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)): IEEE, 1-6.
  • Qaisar, S. M. and AbdelGawad, A. E. E. 2021. Prediction of the Li-Ion Battery Capacity by Using Event-Driven Acquisition and Machine Learning. 7th International Conference on Event-Based Control, Communication, and Signal Processing (EBCCSP)): IEEE, 22-25 June, Krakow, Poland, 1-6.
  • Kanarachos, S. A. 2009. A new method for computing optimal obstacle avoidance steering manoeuvres of vehicles, International Journal of Vehicle Autonomous Systems, 7(1-2), 73-95.
  • Zohaib, M., Pasha, M., Riaz, R., Javaid, N., Ilahi, M., and Khan, R. 2013. Control strategies for mobile robot with obstacle avoidance, Journal of Basic and Applied Scientific Research, 3(4), 1027-1036.
  • Gökçe, Ş., 2019."Dronlar için otomatik rota tayini ve takibi," Yüksek Lisans, Gazi Üniversitesi, Fen Bilimleri Enstitüsü, Ankara, Türkiye.
  • Janardhan, K. S., Baba, S. S., Joe, P. A., and Babu, A. V. 2019. Design and fabrication of UAV for defence applications, J. Mech. Contin. Math. Sci, 14(6).
  • Suherman, S., Putra, R. A., and Pinem, M. 2020. Ultrasonic Sensor Assessment for Obstacle Avoidance in Quadcopter-based Drone System. 3rd International Conference on Mechanical, Electronics, Computer, and Industrial Technology (MECnIT)): IEEE, 25-27 June, Medan, Indonesia, 50-53.
  • Rambabu, R., Bahiki, M. R., and Ali, S. A. M. 2015. Relative position-based collision avoidance system for swarming uavs using multi-sensor fusion, ARPN J. Eng. Appl. Sci, 10(21), 10012-10017.
  • ArduPilot, MAVLink Basics, 2022. https://ardupilot.org/dev/docs/mavlink-basics.html (15 June 2022).
  • Graff, K. F., 1981. A history of ultrasonics. Physical acoustics, 15, 1-97.
  • Morgan, E. J. 2014. HC­SR04 Ultrasonic Sensor [Online] Available: https://datasheetspdf.com/pdf-file/1380136/ETC/HC-SR04/1

Dronlarda ultrasonik sensör tabanlı engelden kaçınma sisteminin iyileştirilmesi

Year 2023, Volume: 4 Issue: 1, 9 - 35, 30.06.2023
https://doi.org/10.55212/ijaa.1261912

Abstract

Dron kullanıcıları her ne kadar gerekli eğitimleri almış olsalar da rüzgâr gibi ansızın ortaya çıkabilecek bir doğa olayına karşı karar verme ve yakınındaki bir engelden kaçınma refleksleri yeterli olmayabilir. Dolayısıyla dronlar ister otonom isterse kullanıcı kontrolünde uçsunlar, kesintisiz bir görev için çevresindeki engelleri algılamalı ve buna uygun hareket etmelidirler. Yapılan bu çalışmada engelin algılanması ve engelden kaçılmasına yönelik bir dron tasarımı ve uygulaması gerçekleştirilmiştir. Tasarlanan dronda uçuş kontrol kartı olarak Pixhawk, engellerin algılanması için ultrasonik sensörler, kaçınma komutlarının elde edilmesi için de mikrodenetleyici olarak Arduino Uno kullanılmıştır. Engel algılamada kullanılan sensörler ve performansları, hedeflenen amaca ulaşmada en belirleyici unsurdur. Çünkü engel algılama sensörlerinin elektriksel gürültülerden çok fazla etkilenmesi, başarıyı düşürmektedir. Bu nedenle öncelikle bu sensörlerin sisteme entegrasyonları araştırılmış ve dron buna uygun olarak geliştirilmiştir. Ardından hem haberleşme kararsızlıklarını en aza indirmek hem de algılama netliğini artırmak için yazılım filtrelemesi yöntemi kullanılarak bir algoritma geliştirilmiştir. Son olarak, dron hem havada asılı dururken hem de hareket halindeyken engelden kaçma becerisi araştırılmıştır. Yapılan deneylerde dronun 3,94 m/s’lik uçuş hızına kadar engelden başarılı bir şekilde kaçabildiği görülmüştür.

Project Number

22201010

References

  • Ayaz, M. Y., 2021."Dron için RF karıştırıcı tasarımı ve gerçekleştirilmesi," Yüksek Lisans, Konya Teknik Üniversitesi, Lisansüstü Eğitim Enstitüsü, Konya, Türkiye.
  • Özdemir, Ş., 2013."İnsansız hava araçlarında kullanılan fırçasız DC motorların kontrolü," Yüksek Lisans, Marmara Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul, Türkiye.
  • Sivritaş, Ş., 2020."Fast 3D Tracking Algorithm İmplementations On Mini Quard-copters," Yüksek Lisans, Özyeğin Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul, Türkiye.
  • Çakıcı, E., 2019."Mini insansız hava araçları için bir fırçasız motor test sistemi geliştirilmesi," Yüksek Lisans, ESOGÜ, Fen Bilimleri Enstitüsü, Eskişehir, Türkiye.
  • Al-Tekreeti, M. M. A., 2019."Unmanned surface vehicle (USV) with obstacle avoidance system," Yüksek Lisans, Çankaya Üniversitesi, Fen Bilimleri Enstitüsü, Ankara, Türkiye.
  • Çalış, M., 2019."Ultrasonik mesafe ölçümü ve doğrulanması," Yüksek Lisans, Gazi Üniversitesi, Fen Bilimleri Enstitüsü, Ankara, Türkiye.
  • Kale, A. A. 2021. Drone Technology and Its Applications, SSRN, doi: http://dx.doi.org/10.2139/ssrn.3922787
  • Venkatesh, S., Kundan, S., and Srija, A. 2021. Obsatacle Avoidance Robotic Vehicle Using Hc-Sr04 Ultrasonic Sensor, Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(12), 2358-2362.
  • Tayebi, A. and McGilvray, S. 2006. Attitude stabilization of a VTOL quadrotor aircraft, IEEE Transactions on control systems technology, 14(3), 562-571.
  • Abd Rahman, Y. A., Hajibeigy, M. T., Al-Obaidi, A. S. M., and Cheah, K. H. 2018. Design and fabrication of small vertical-take-off-landing unmanned aerial vehicle. MATEC Web of Conferences, 152(2018), 26 February, Selangor, Malaysia.
  • Wubben, J., Fabra, F., Calafate, C. T., Krzeszowski, T., Marquez-Barja, J. M., Cano, J.-C., and Manzoni, P. 2019. Accurate landing of unmanned aerial vehicles using ground pattern recognition, Electronics, 8(12), 1532.
  • Ekmen, M. İ., 2021."Drone sürüsü ile hedef takip optimizasyonu," Yüksek Lisans, Konya Teknik Üniversitesi, Lisansüstü Eğitim Enstitüsü, Konya, Türkiye.
  • Yiğit, A., 2020."Adaptive Large Neighbourhood Search Heuristic On Vehicle Routing Problem With Drones And Time Windows," Yüksek Lisans, Boğaziçi Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul, Türkiye.
  • Önler, E., 2012."Ultrasonik sensör yardımıyla belirlenen yaprak yoğunluğuna göre püskürtme miktarını ayarlayan sistemin tasarımı (akıllı ilaçlama makinası)," Yüksek Lisans, Namık Kemal Üniversitesi, Fen Bilimleri Enstitüsü, Tekirdağ, Türkiye.
  • Selim, E., Uyar, E., and Alcı, M. 2013. Quadrocopterin matematiksel modeli ve kontrolü, TOK2013-Otomatik Kontrol Ulusal Toplantısı, 548-553.
  • Köz, S., 2019."Yangın Topu Kullanılarak Yangın Söndüren Quadrocopter Tasarımı Ve Prototip İmalatı," Yüksek Lisans, Trakya Üniversitesi, Fen Bilimleri Enstitüsü, Edirne, Türkiye.
  • Kim, J., Choi, Y., Jeon, S., Kang, J., and Cha, H. 2020. Optrone: Maximizing performance and energy resources of drone batteries, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 39(11), 3931-3943.
  • Abdilla, A., Richards, A., and Burrow, S. 2015. Power and endurance modelling of battery-powered rotorcraft. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)): IEEE, 28 September - 02 October, Hamburg, Germany, 675-680.
  • Maekawa, K., Negoro, S., Taniguchi, I., and Tomiyama, H. 2017. Power measurement and modeling of quadcopters on horizontal flight. Fifth International Symposium on Computing and Networking (CANDAR)): IEEE, 326-329.
  • Chen, Y., Baek, D., Bocca, A., Macii, A., Macii, E., and Poncino, M. 2018. A case for a battery-aware model of drone energy consumption. IEEE International Telecommunications Energy Conference (INTELEC)): IEEE, 07-11 October, Turino, Italy, 1-8.
  • He, L. and Pace, J. 2020. Estimating Altitude of Drones Using Batteries. IEEE/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI)): IEEE, 21-24 April, Sydney, NSW, Australia, 264-265.
  • Park, S., Zhang, L., and Chakraborty, S. 2017. Battery assignment and scheduling for drone delivery businesses. 2017 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)): IEEE, 1-6.
  • Qaisar, S. M. and AbdelGawad, A. E. E. 2021. Prediction of the Li-Ion Battery Capacity by Using Event-Driven Acquisition and Machine Learning. 7th International Conference on Event-Based Control, Communication, and Signal Processing (EBCCSP)): IEEE, 22-25 June, Krakow, Poland, 1-6.
  • Kanarachos, S. A. 2009. A new method for computing optimal obstacle avoidance steering manoeuvres of vehicles, International Journal of Vehicle Autonomous Systems, 7(1-2), 73-95.
  • Zohaib, M., Pasha, M., Riaz, R., Javaid, N., Ilahi, M., and Khan, R. 2013. Control strategies for mobile robot with obstacle avoidance, Journal of Basic and Applied Scientific Research, 3(4), 1027-1036.
  • Gökçe, Ş., 2019."Dronlar için otomatik rota tayini ve takibi," Yüksek Lisans, Gazi Üniversitesi, Fen Bilimleri Enstitüsü, Ankara, Türkiye.
  • Janardhan, K. S., Baba, S. S., Joe, P. A., and Babu, A. V. 2019. Design and fabrication of UAV for defence applications, J. Mech. Contin. Math. Sci, 14(6).
  • Suherman, S., Putra, R. A., and Pinem, M. 2020. Ultrasonic Sensor Assessment for Obstacle Avoidance in Quadcopter-based Drone System. 3rd International Conference on Mechanical, Electronics, Computer, and Industrial Technology (MECnIT)): IEEE, 25-27 June, Medan, Indonesia, 50-53.
  • Rambabu, R., Bahiki, M. R., and Ali, S. A. M. 2015. Relative position-based collision avoidance system for swarming uavs using multi-sensor fusion, ARPN J. Eng. Appl. Sci, 10(21), 10012-10017.
  • ArduPilot, MAVLink Basics, 2022. https://ardupilot.org/dev/docs/mavlink-basics.html (15 June 2022).
  • Graff, K. F., 1981. A history of ultrasonics. Physical acoustics, 15, 1-97.
  • Morgan, E. J. 2014. HC­SR04 Ultrasonic Sensor [Online] Available: https://datasheetspdf.com/pdf-file/1380136/ETC/HC-SR04/1
There are 32 citations in total.

Details

Primary Language English
Subjects Engineering, Aerospace Engineering
Journal Section Research Articles
Authors

Fatih Alpaslan Kazan 0000-0002-5461-0117

Haydar Solak 0000-0001-6789-8255

Project Number 22201010
Publication Date June 30, 2023
Submission Date March 8, 2023
Published in Issue Year 2023 Volume: 4 Issue: 1

Cite

APA Kazan, F. A., & Solak, H. (2023). Improvement of ultrasonic sensor-based obstacle avoidance system in drones. International Journal of Aeronautics and Astronautics, 4(1), 9-35. https://doi.org/10.55212/ijaa.1261912
AMA Kazan FA, Solak H. Improvement of ultrasonic sensor-based obstacle avoidance system in drones. International Journal of Aeronautics and Astronautics. June 2023;4(1):9-35. doi:10.55212/ijaa.1261912
Chicago Kazan, Fatih Alpaslan, and Haydar Solak. “Improvement of Ultrasonic Sensor-Based Obstacle Avoidance System in Drones”. International Journal of Aeronautics and Astronautics 4, no. 1 (June 2023): 9-35. https://doi.org/10.55212/ijaa.1261912.
EndNote Kazan FA, Solak H (June 1, 2023) Improvement of ultrasonic sensor-based obstacle avoidance system in drones. International Journal of Aeronautics and Astronautics 4 1 9–35.
IEEE F. A. Kazan and H. Solak, “Improvement of ultrasonic sensor-based obstacle avoidance system in drones”, International Journal of Aeronautics and Astronautics, vol. 4, no. 1, pp. 9–35, 2023, doi: 10.55212/ijaa.1261912.
ISNAD Kazan, Fatih Alpaslan - Solak, Haydar. “Improvement of Ultrasonic Sensor-Based Obstacle Avoidance System in Drones”. International Journal of Aeronautics and Astronautics 4/1 (June 2023), 9-35. https://doi.org/10.55212/ijaa.1261912.
JAMA Kazan FA, Solak H. Improvement of ultrasonic sensor-based obstacle avoidance system in drones. International Journal of Aeronautics and Astronautics. 2023;4:9–35.
MLA Kazan, Fatih Alpaslan and Haydar Solak. “Improvement of Ultrasonic Sensor-Based Obstacle Avoidance System in Drones”. International Journal of Aeronautics and Astronautics, vol. 4, no. 1, 2023, pp. 9-35, doi:10.55212/ijaa.1261912.
Vancouver Kazan FA, Solak H. Improvement of ultrasonic sensor-based obstacle avoidance system in drones. International Journal of Aeronautics and Astronautics. 2023;4(1):9-35.

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