KaydedilenlerDerginizi ekleyin

Dergiler / Hittite Journal of Science and Engineering / 2020 / Cilt: 7 - Sayı: 2

R ecently, quadrotors gained popularity due to the- ir high maneuverability, cost and vertical take

A Novel Navigation Algorithm for Mapping Indoor Environments with a Quadrotor

Omer ORAL
Ali Emre TURGUT
Kutluk Bilge ARİKAN
Sayfa
1–1
Yayın Tarihi
2020-06-26
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The target-based navigation algorithm outperforms the other two algorithms especially in environments with obstacles

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:I n the last decade, unmanned aerial vehicle gained popularity and started to be used in different tasks most of which are performed in outdoor environments.
📊
Temel Bulgular & Veri:In this paper, we developed a framework and a novel target-based navigation algorithm for mapping of an unknown 2D environment with a quadrotor using an ultrawideband system.
💡
Sonuç & Katkı:The target-based navigation algorithm outperforms the other two algorithms especially in environments with obstacles
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Abstract

I n the last decade, unmanned aerial vehicle gained popularity and started to be used in different tasks most of which are performed in outdoor environments. Still, there is a great potential to use quadrotors in indoor tasks such as urban relief and disaster operations. In this paper, we developed a framework and a novel target-based navigation algorithm for mapping of an unknown 2D environment with a quadrotor using an ultrawideband system. The target-based navigation algorithm aims to explore map of the environment by moving the border between the discovered and undiscovered areas. It uses A* search algorithm for path planning if there is an obstacle present in the environment. The target-based navigation algorithm is implemented on Gazebo simulator and its performance is compared with the well-known wall following algorithm and exploration algorithm in terms of task completion time and distance travelled. The target-based navigation algorithm outperforms the other two algorithms especially in environments with obstacles

🔖

Bu Makaleye Atıf Yap

ORAL, O., TURGUT, A. E., ARİKAN, K. B. (2020). R ecently, quadrotors gained popularity due to the- ir high maneuverability, cost and vertical take. Hittite Journal of Science and Engineering, Cilt: 7 - Sayı: 2, 1–1. https://ajindex.com/articles/671603

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (7)

  1. Y. Khosiawan and I. Nielsen, “A system of uav application in indoor environment,” Production & Manufacturing Research 4(1), 2–22 (2016). 2. C. Hegde and N. S. Guptha, “Implementation of Mapping Algorithm for SLAM Operation,” Ijetae 3(9), 235–238 (2013). 3. A. Araujo, D. Portugal, M. S. Couceiro, et al., “Integrating Arduino Based Educational Mobile Robots in ROS,” Journal of Intelligent and Robotic Systems: Theory and Applications 77(2), 281–298 (2014). 4. H. I. M. A. Omara and K. S. M. Sahari, “Indoor mapping using kinect and ROS,” 2015
  2. International Symposium on Agents, Multi Agent Systems and Robotics, ISAMSR 2015, 110–116 (2016). 5. N. Johnson, “Vision-Assisted Control of a Hovering Air Vehicle in an Indoor Setting,” Engineering and Technology (August) (2008). 6. S. Ahrens, D. Levine, G. Andrews, et al., “Vision-based guidance and control of a hovering vehicle in unknown, gps-denied environments,” Proceedings - IEEE International Conference on Robotics and Automation, 2643–2648 (2009). 7. J. F. Roberts, T. S. Stirling, J.-C. Zufferey, et al., “Quadrotor Using Minimal Sensing For Autonomous Indoor Flight,” European Micro Air Vehicle Conference and Flight Competition (EMAV2007) (September), 17–21 (2007). 8. M. Kara Mohamed, S. Patra, and A. Lanzon, “Designing simple indoor navigation system for UAVs,” 2011 19th Mediterranean Conference on Control and Automation, MED 2011, 1223–1228 (2011). 9. M. Achtelik, J. Williams, M. J. Owen, et al., “Autonomous Navigation and Exploration of a Quadrotor Helicopter in GPS denied Indoor Environments,” First Symposium on Indoor Flight (2009).
  3. S. Grzonka, G. Grisetti, and W. Burgard, “A fully autonomous indoor quadrotor,” IEEE Transactions on Robotics 28(1), 90–100 (2012).
  4. F. Wang, J. Cui, S. K. Phang, et al., “A mono-camera and scanning laser range finder based UAV indoor navigation system,” 2013 International Conference on Unmanned Aircraft Systems, ICUAS Conference Proceedings, 694–701 (2013).
  5. O. Oguejiofor, A. Aniedu, H. Ejiofor, et al., “Trilateration Based localization Algorithm for Wireless Sensor Network,” Int. J. Sci. Mod. Eng (10), 21–27 (2013).
  6. H. Liu, J. Liu, P. Banerjee, et al., “Survey of Wireless Indoor Positioning Techniques and Systems,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Bucharest, Romania: 1990) 35(1), 39–42 (1991).
  7. B. Kempke, P. Pannuto, and P. Dutta, “SurePoint: Exploiting Ultra Wideband Flooding and Diversity to Provide Robust, Scalable, High-Fidelity Indoor Localization