A Novel Navigation Algorithm for Mapping Indoor Environments with a Quadrotor

Year 2020, , 0 - 0, 26.06.2020

Omer Oral , Ali Emre Turgut Kutluk Bilge Arikan

https://doi.org/10.17350/HJSE19030000181

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

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

Abstract

References

• 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 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).
• S. Grzonka, G. Grisetti, and W. Burgard, “A fully autonomous
• indoor quadrotor,” IEEE Transactions on Robotics 28(1), 90–100 (2012).
• 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).
• 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). 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). B. Kempke, P. Pannuto, and P. Dutta, “SurePoint: Exploiting Ultra Wideband Flooding and Diversity to Provide Robust, Scalable, High-Fidelity Indoor Localization,” SenSys , 318–319 (2016).
• V. Barral, P. Su´arez-Casal, C. J. Escudero, et al., “Multi-sensor accurate forklift location and tracking simulation in industrial indoor environments,” Electronics (Switzerland) 8(10) (2019). J. R. B. del Rosario, J. G. Sanidad, A. M. Lim, et al., “Modelling and Characterization of a Maze-Solving Mobile Robot Using Wall Follower Algorithm,” Applied Mechanics and Materials 446- 447(July), 1245–1249 (2013).
• E. B. Küçüktabak, M. M. Pelit, Z. O¨ . Orhan, et al., “Kapalı Bir Alanda Basit Bir IHA ile Keşif Metodu Tasarımı Indoor UAV Exploration Method with UWB Localization,” TOK , 1–6 (2017). L. Freda and G. Oriolo, “Frontier-Based Probabilistic Strategies for Sensor-Based Exploration,” International Conference on Robotics and Automation (April), 3892–3898 (2005). P. E. Hart, N. J. Nilsson, and B. Raphael, “A Formal Basis for the Heuristic Determination of Minimum Cost Paths,” IEEE Transactions of Systems Science and Cybernetics 4(2), 100–107 (1968).
• M. Quigley, K. Conley, B. P. Gerkey, et al., “Ros: an open-source robot operating system,” in ICRA Workshop on Open Source Software, (2009). M. S. Güzel, V. B. Ajabshir, P. Nattharith, et al., “A novel framework 21. for multi-agent systems using a decentralized strategy,” Robotica 37(4), 691–707 (2019).
• M. S. Güzel, E. C. Gezer, V. B. Ajabshir, et al., “An adaptive pattern formation approach for swarm robots,” in 2017 4th International Conference on Electrical and Electronic Engineering (ICEEE), 194–198, IEEE (2017).
• J. Meyer, A. Sendobry, S. Kohlbrecher, et al., “Comprehensive Simulation of Quadrotor UAVs using ROS and Gazebo,” 7628(November) (2012). Hokuyo, “Scanning Rangefinder Distance Data Output/URG- 04LX-UG01 Product Details — Hokuyo Automatic Co. Ltd.”
• O. Oral, A. E. Turgut, and K. B. Arıkan, “IHA ile GPS Kullanmadan Kapalı Alanların Haritasının Çıkartılması,” ToRK 2019 - Turkiye Robotbilim Konferansı 5(1), 105–111 (2019).