Abstract
Unmanned aerial vehicles have shown great potential in fast shipping and delivery, including delivering emergency support and services to the disaster (natural or manmade) hit areas where manual reach is infeasible. For accurate and effective emergency service delivery at the adverse sites, the UAVs need to fly close to the ground. Due to the low-altitude flight, there may be many stationary obstacles (e.g., trees and buildings) on the path of a UAV. Detecting these obstacles is crucial for successful mission accomplishment and evading crash. The existing obstacle detection methods limit the flying speed of a UAV due to the latency in processing and analysing the in-flight sensed data. To mitigate this, we propose to equip the UAV with the prior information of the obstacles on its trajectory. In case of an obstacle, the UAV slows down to avoid the obstacle; otherwise, it travels with a much higher speed. As experiment, we fed the UAVs with the satellite images from the Google Maps. It is observed that the proposed approach improves the overall flying speed of the UAVs to a great extent.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Erdelj M, Król M, Natalizio E (2017) Wireless sensor networks and multi-UAV systems for natural disaster management. Comput Netw 124:72–86
Martin P, Payton O, Fardoulis J, Richards D, Yamashiki Y, Scott T (2016) Low altitude unmanned aerial vehicle for characterising remediation effectiveness following the FDNPP accident. J Environ Radioact 151(Part 1):58–63
Djimantoro MI, Suhardjanto G (2017) The advantage by using low-altitude UAV for sustainable urban development control. IOP Conf Ser Earth Environ Sci 109(012014)
Chen J, Zhou Y, Lv Q, Deveerasetty KK, Dike HU (2018) A review of autonomous obstacle avoidance technology for multi-rotor UAVs. In: IEEE international conference on information and automation (ICIA), Wuyishan, China
Pham H, Smolka SA, Stoller SD, Phan D, Yang J (2015) A survey on unmanned aerial vehicle collision. arXiv: 1508.07723
Yasin JN, Mohamed SAS, Haghbayan M-H, Heikkonen J, Tenhunen H, Plosila J (2020) Unmanned aerial vehicles (UAVs): collision avoidance systems and approaches. IEEE Access 8:105139–105155
Sampedro C, Bavle H, Sanchez-Lopez JL, Fernández RAS, RodrÃguez-Ramos A, Molina M, Campoy P (2016) A flexible and dynamic mission planning architecture for uav swarm coordination. In: International conference on unmanned aircraft systems (ICUAS), Arlington, USA
Hadi G, Varianto R, Trilaksono B, Budiyono A (2014) Autonomous UAV system development for payload dropping mission. J Instrum Autom Syst 1(2):72–22
Ramirez-Atencia C, Camacho D (2018) Extending QGroundControl for automated mission planning of UAVs. Sensors 7(18):23–39
Ryan A, Hedrick J (2005) A mode-switching path planner for UAV-assisted search and rescue. In: 44th IEEE conference on decision and control, Seville, Spain
Castelli T, Sharghi A, Harper D, Tremeau A (2016) Autonomous navigation for low-altitude UAVs in urban areas. arXiv: 1602.08141v1
Birk A, Wiggerich B, Bülow H, Pfingsthorn M (2011) Safety, security, and rescue missions with an unmanned aerial vehicle (UAV). J Intell Rob Syst 64(1):57–76
Bielecki A, Buratowski T, Śmigielski P (2013) Recognition of two-dimensional representation of urban environment for autonomous flying agents. Expert Syst Appl 40(9):3623–3633
Haque M, Muhammad M, Swarnaker D, Arifuzzaman M (2014) Autonomous quadcopter for product home delivery. In: International Conference on Electrical Engineering and Information & Communication Technology, Dhaka, Bangladesh
Gageik N, Benz P, Montenegro S (2015) Obstacle detection and collision avoidance for a UAV with complementary low-cost sensors. IEEE Access 3:599–609
McGee TG, Sengupta R, Hedrick K (2005) Obstacle detection for small autonomous aircraft using sky segmentation. In: IEEE international conference on robotics and automation, Barcelona, Spain
Odelga M, Stegagno P, Bülthoff HH (2016) Obstacle detection, tracking and avoidance for a teleoperated UAV. In: IEEE international conference on robotics and automation (ICRA), Stockholm
Saha S, Natraj A, Waharte S (2014) A real-time monocular vision-based frontal obstacle detection and avoidance for low cost UAVs in GPS denied environment. In: IEEE international conference on aerospace electronics and remote sensing technology, Yogyakarta, Indonesia
Zheng L, Zhang P, Tan J, Li F (2019) The obstacle detection method of UAV based on 2D lidar. IEEE Access 7:163437–163448
Hrabar S (2008) 3D path planning and stereo-based obstacle avoidance for rotorcraft UAVs. In: IEEE/RSJ international conference on intelligent robots and systems, Nice, France
Branson S, Wegner J, Hall D, Lang N, Schindler K, Perona P (2018) From Google maps to a fine-grained catalog of street trees. ISPRS J Photogramm Remote Sens 135:13–30
Wegner JD, Branson S, Hall D, Schindler K, Perona P (2016) Cataloging public objects using aerial and street-level images-urban trees. In: The IEEE conference on computer vision and pattern recognition, Las Vegas, USA
Li X, Ratti C, Seiferling I (2018) Quantifying the shade provision of street trees in urban landscape: a case study in Boston, USA, using Google street view. Landsc Urban Plan 169:81–91
Prasetia AS, Wai RJ, Wen YL, Wang Y (2019) Mission-based energy consumption prediction of multirotor UAV. IEEE Access 7:33055–33063
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sinhababu, N., Pramanik, P.K.D. (2022). An Efficient Obstacle Detection Scheme for Low-Altitude UAVs Using Google Maps. In: Sharma, N., Chakrabarti, A., Balas, V.E., Bruckstein, A.M. (eds) Data Management, Analytics and Innovation. Lecture Notes on Data Engineering and Communications Technologies, vol 71. Springer, Singapore. https://doi.org/10.1007/978-981-16-2937-2_28
Download citation
DOI: https://doi.org/10.1007/978-981-16-2937-2_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2936-5
Online ISBN: 978-981-16-2937-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)