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A concept of laser scanner designed to realize 3D obstacle avoidance for a fixed-wing UAV

Published online by Cambridge University Press:  09 June 2014

Cezary Kownacki
Cezary Kownacki*
Affiliation:
Faculty of Mechanical Engineering, Bialystok University of Technology, ul. Wiejska 45C, 15-351 Bialystok, Poland
*
*Corresponding author. E-mail: [email protected]
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Summary

This paper presents a concept of a laser scanner framework designed for obstacle avoidance used on mini fixed-wing unmanned aerial vehicles (UAVs) flying in outdoor environments. The innovation is a conical field of view that guarantees tri-dimensional (3D) obstacle detection and localization at any pitch or roll angle. This advantage is very important for the case of fixed-wing UAV flights where the attitude is changing rapidly. Measurement sequences create a map that is represented by a circular grid with the center fitted to the x-axis of the UAV's body, lying in the plane normal to the velocity vector and projected in the front of UAV. This means that the map cells contain differences between the safety zone radius and distances acquired from area in close proximity to the flight path. Actual UAV attitude can be compensated by rotation and shift of two masks of gains that are applied to the map to determine pitch and roll commands. Results of the simulation research conducted on the designed concept are very promising, as they present a combination of lateral and vertical obstacle avoidance. Based on the experience with laser rangefinders operating on a real UAV, it can be convincingly determined that the concept of the laser scanner is able to be brought into reality.

Keywords

Laser scannerLIDARUnmanned aerial vehicleObstacle avoidanceMappingSLAM
Type
Articles
Information
Robotica , Volume 34 , Issue 2 , February 2016 , pp. 243 - 257
Copyright
Copyright © Cambridge University Press 2014 

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References

1.Grzonka, S., Grisetti, G. and Burgard, W., “Towards a Navigation System for Autonomous Indoor Flying,” Proceedings of the IEEE International Conference on Robotics and Automation, Kobc, Japan (May 12–17, 2009) pp. 2878–2883.Google Scholar
2.Sanfourche, M., Besnerais, G. L., Fabiani, P., Piquereau, A. and Whalley, M. S., “Comparison of Terrain Characterization Methods for Autonomous UAVs,” Proceedings of the 65th Annual Forum of the American Helicopter Society, Grapevine, Texas, USA (May 27–29, 2009) pp. 1–14.Google Scholar
3.Whalley, M., Takahashi, M., Tsenkov, P. and Schulein, G., “Field-Testing of a Helicopter UAV Obstacle Field Navigation and Landing System,” Proceedings of the 65th Annual Forum of the American Helicopter Society, see ref. 2.Google Scholar
4.Lin, Y., Hyyppa, J. and Jaakola, A., “Mini-UAV-Borne LIDAR for fine-scale mapping,” IEEE GeoSci. Remote Sens. Lett. 8 (3), 426–430 (2011).CrossRefGoogle Scholar
5.Geyer, M. S. and Johnson, E. N., “3D Obstacle Avoidance in Adversarial Environments for Unmanned Aerial Vehicles,” Proceedings of AIAA Guidance, Navigation, and Control Conference and Exhibit, Keystone, USA (Aug. 21–24, 2006) (chapter AIAA 2006-6542).Google Scholar
6.Wallace, L. O., Lucieer, A. and Watson, C. S., “Assessing the feasibility of UAV-based LIDAR for high resolution, forest change detection, international archives of the photogrammetry,” Remote Sens. Spat. Inf. Sci. XXXIX–B7, 499–504 (2012).Google Scholar
7.Wallace, L. O., Lucieer, A., Watson, C. S. and Turner, D., “Development of a UAV-LiDAR system with application to forest inventory,” Remote Sens. 4, 1519–1543 (2012).CrossRefGoogle Scholar
8.Hrabar, S. E., “3D Path Planning and Stereo-based Obstacle Avoidance for Rotorcraft UAVs,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France (Sep. 22–26, 2008) pp. 807–814.Google Scholar
9.Harbar, S., “Reactive Obstacle Avoidance for Rotorcraft UAVs,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, USA (Sep. 25–30, 2011) pp. 4967–4974.Google Scholar
10.Hrabar, S. E. and Sukhatme, G. S., “Vision-based navigation through urban canyons,” J. Field Robot. 26 (5), 431–452 (2009).CrossRefGoogle Scholar
11.Beyeler, A., Zufferey, J. C., Floreano, D., “Vision based control of near-obstacle flight,” Auton. Robots 27 (3), 201–219 (2009).CrossRefGoogle Scholar
12.Kownacki, C., “Guidance and Obstacle Avoidance of MAV in Uncertain Urban Environment,” Proceedings of the 2009 European Micro Aerial Vehicle Conference and Flight Competition, Delft, the Netherlands (Sep. 14–17, 2009), online only.Google Scholar
13.Kownacki, C., “Obstacle Avoidance Strategy for Micro Aerial Vehicle,” In: Advances in Aerospace Guidance, Navigation and Control (Holzapfel, F. and Theil, S., eds.) (Springer-Verlag, Berlin, 2011) pp. 117–135.CrossRefGoogle Scholar
14.Kownacki, C., “Successful application of miniature laser rangefinders in obstacle avoidance method for fixed wing UAV,” Int. J. Robot. Autom. 28 (3), 292–298 (2013).Google Scholar
15.Todorovic, S. and Nechyba, M. C., “A vision system for intelligent mission profiles of micro air vehicles,” IEEE Trans. Veh. Technol. 53 (6), 1713–1725 (2004).CrossRefGoogle Scholar
16.Griffiths, S., Saunders, J., Curtis, A., Barber, B., McLain, T. and Beard, R., “Obstacle and Terrain Avoidance for Miniature Aerial Vehicles,” In: Advances in Unmanned Aerial Vehicles, State of the Art and the Road to Autonomy (Valavanis, K. P., ed.) (Spinger, 2007) pp. 213–244.CrossRefGoogle Scholar
17.Campoy, P., Correa, J. F., Mondragón, I., Martínez, C., Olivares, M., Mejías, L., Artieda, J., Valavanis, K., Oh, P. and Piegl, L., “Computer Vision Onboard UAVs for Civilian Tasks,” In: Unmanned Aircraft Systems (Valavanis, K. P., Oh, P., Piegl, L. A., eds.) (Springer, Netherlands 2009) pp. 105–135.CrossRefGoogle Scholar
18.Andert, F., Adolf, F., Goormann, L. and Dittrich, J., “Autonomous vision-based helicopter flights through obstacle gates,” J. Intell. Robot. Syst. 259–280 (2010).Google Scholar
19.Watanabe, Y., Calise, A. and Johnson, E. N., “Vision-Based Obstacle Avoidance for UAVs,” Proceedings of AIAA Guidance, Navigation and Control Conference and Exhibit, Hilton Head, USA (Aug. 20–23, 2007) (Chapter AIAA 2007-6829).Google Scholar
20.Kowacki, C., “Successful application of miniature laser rangefinders in obstacle avoidance method for fixed wing micro UAV,” Int. J. Robot. Autom., 28 (3), 292–298 (2013).Google Scholar
21.Achtelik, M., Bachrach, A., Heb, R., Prentice, S. and Roy, N., “Stereo Vision and Laser Odometry for Autonomous Helicopters in GPS-denied Indoor Environments,” Proceedings of the 11th SPIE Unmanned Systems Technology, vol. 7332 733219, Orlando, Florida, USA (Apr. 30, 2009) pp. 1–10.Google Scholar
22.Scherer, S., Singh, S., Chamberlain, L. and Elgersma, M., “Flying fast and low among obstacles: Methodology and experiments,” Int. J. Robot. Res. 27 (5), 549–574 (2008).CrossRefGoogle Scholar
23.Call, B., Randy, B. R., Clark, T. C., “Obstacle Avoidance For Unmanned Air Vehicles Using Image Feature Tracking,” Proceedings of AIAA Guidance, Navigation, and Control Conference and Exhibit, Keystone, USA (Aug. 21–24, 2006) (chapter AIAA 2006-6541).Google Scholar