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Autonomous obstacle avoidance for fixed-wing unmanned aerial vehicles

Published online by Cambridge University Press:  27 January 2016

A. H. J. de Ruiter  and
S. Owlia
A. H. J. de Ruiter*
Affiliation:
Ryerson University, Toronto, Canada
S. Owlia
Affiliation:
Ryerson University, Toronto, Canada
*
aderuiter@ryerson.ca
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Abstract

This paper investigates a method for autonomous obstacle avoidance for fixed-wing unmanned aerial vehicles (UAVs), utilising potential fluid flow theory. The obstacle avoidance algorithm needs only compute the instantaneous local potential velocity vector, which is passed to the flight control laws as a direction command. The approach is reactive, and can readily accommodate real-time changes in obstacle information. UAV manoeuvring constraints on turning or pull-up radii, are accounted for by approximating obstacles by bounding rectangles, with wedges added to their front and back to shape the resulting fluid pathlines. It is shown that the resulting potential flow velocity field is completely determined by the obstacle field geometry, allowing one to determine a non-dimensional relationship between obstacle added wedge-length and the corresponding minimum pathline radius of curvature, which can then be readily scaled in on-board implementation. The efficacy of the proposed approach has been demonstrated numerically with an Aerosonde UAV model.

Type
Research Article
Information
The Aeronautical Journal , Volume 119 , Issue 1221 , November 2015 , pp. 1415 - 1436
Copyright
Copyright © Royal Aeronautical Society 2015

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References

1.Amin, J., Boskovic, J. and Mehra, R.A fast and efficient approach to path planning for unmanned vehicles, 2006, AIAA Guidance, Navigation, and Control Conference and Exhibition, Keystone, Colorado, USA, AIAA 2006-6103.CrossRefGoogle Scholar
2.Anderson, J.D.Introduction to Flight, Third edition, 1989, McGraw-Hill, New York, USA.Google Scholar
3.Anderson, J.D.Fundamentals of Aerodynamics, Fourth edition, 2007, McGraw-Hill, Boston, MA, USA.Google Scholar
4.Barnard, J.A.The use of unmanned aircraft in oil, gas and mineral E+P activities, 2008, Society of Exploration Geophysicists, p 1132, Las Vegas, Nevada, USA.Google Scholar
5.Barnard, J.A. Use of unmanned air vehicles in oil, gas and mineral exploration activities, 2010, AUVSI Unmanned Systems North America.Google Scholar
6.Caron, R.M.Aeromagnetic Surveying Using a Simulated Unmanned Aircraft System, Master’s thesis, 2011, Carleton University.CrossRefGoogle Scholar
7.Casbeer, D.W., Li, S.-M., Beard, R.W., Mehra, R.K. and McLain, T.W. Forest fire monitoring with multiple small UAVs, 2005 American Control Conference, IEEE, pp 3530–3535.Google Scholar
8.Chandler, P., Rasmussen, S. and Pachter, M.UAV cooperative path planning, 2000, AIAA Guidance, Navigation and Control Conference, American Institute of Aeronautics and Astronautics.CrossRefGoogle Scholar
9.Chapman, B.L. and Perreira, N.Algorithm for intelligent control of a robot manipulator, 1983, International MOTORCON Conference, Orlando, FL, USA, Intertec Communications, pp 334–344.Google Scholar
10.De Biasio, M., Arnold, T., Leitner, R. and Meestert, R.UAV based multi-spectral imaging system for environmental monitoring, 2011, 20th IMEKO TC2 Symposium on Photonics in Measurement, 20th ISPM 2011, Shaker Verlag, pp 69–73.Google Scholar
11.Doty, K.L. and Govindaraj, S.Robot obstacle detection and avoidance determined by actuator torques and joint positions, 1982, IIEEE Southeastcon ‘82, IEEE, pp 470–473.Google Scholar
12.Fahimi, F., Ashrafiuon, H. and Nataraj, C.Obstacle avoidance for spatial hyper-redundant manipulators using harmonic potential functions and the mode shape technique, J Robotic Systems, 2003, 20, (1), pp 23–33.CrossRefGoogle Scholar
13.Fahimi, F., Nataraj, C. and Ashrafiuon, H.Real-time obstacle avoidance for multiple mobile robots, Robotica, 2008, 27, (2), pp 189–198.CrossRefGoogle Scholar
14.Ge, S. and Cui, Y.New potential functions for mobile robot path planning, IEEE Transactions on Robotics and Automation, 2000, 16, (5), pp 615–620.CrossRefGoogle Scholar
15.Ge, S. and Cui, Y.Dynamic motion planning for mobile robots using potential field method, Autonomous Robots, 2002, 13, (3), pp 207–222.CrossRefGoogle Scholar
16.Girard, A.R., Howell, A.S. and Hedrick, J.K.Border patrol and surveillance missions using multiple unmanned air vehicles, 2004, 43rd IEEE Conference on Decision and Control (CDC), 1, IEEE, pp 620–625.Google Scholar
17.Hausamann, D., Zirnig, W., Schreier, G. and Strobl, P.Monitoring of gas pipelines — a civil UAV application, Aircraft Eng and Aerospace Tech, 2005, 77, (5), pp 352–360.CrossRefGoogle Scholar
18.Hwangbo, M., Kuffner, J. and Kanade, T.Efficient two-phase 3D motion planning for small fixed-wing UAVs, 2007, IEEE International Conference on Robotics and Automation, Rome, Italy, pp 1035–1041.Google Scholar
19.Jones, G.P., Pearlstine, L.G. and Percival, H.F.An assessment of small unmanned aerial vehicles for wildlife research, Wildlife Society Bulletin, 2006, 34, (3), pp 750–758.CrossRefGoogle Scholar
20.Khatib, O.Real-time obstacle avoidance for manipulators and mobile robots, Int J of Robotics Research, March 1986, 5, (1), pp 90–98.CrossRefGoogle Scholar
21.Khosla, P. and Volpe, R. Superquadric artifcial potentials for obstacle avoidance and approach, 1988 IEEE International Conference on Robotics and Automation, IEEE Comput Soc Press, pp 1778–1784.Google Scholar
22.Kim, J.-O. and Khosla, P.Real-time obstacle avoidance using harmonic potential functions, IEEE Transactions on Robotics and Automation, 1992, 8, (3), pp 338–349.CrossRefGoogle Scholar
23.Kurnaz, S., Cetin, O. and Kaynak, O.Fuzzy logic based approach to design of flight control and navigation tasks for autonomous unmanned aerial vehicles, J Intelligent and Robotic Systems, Oct 2008 54, (1-3), pp 229–244.CrossRefGoogle Scholar
24.LaValle, S. and Kuffner, J.Randomized kinodynamic planning, Int J of Robotics Research, 2001, 20, (5), pp 378–400.CrossRefGoogle Scholar
25.Lavalle, S.M.Rapidly-exploring random trees: A new tool for path planning, 1998, Tech Report, Department of Computer Science, Iowa State University, Ames, IA, USA.Google Scholar
26.Lavalle, S.M.Planning Algorithms, 2006, Cambridge University Press, UK.CrossRefGoogle Scholar
27.Li, X. and Yang, L.Design and implementation of U AV intelligent aerial photography system BT, 2012, Foutth International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMS, 2, IEEE Computer Society, pp 200–203.Google Scholar
28.Loeff, L.A. and Soni, A.H.An algorithm for computer guidance of a manipulator in between obstacles, Transactions of the ASME, Series B, J Eng for Industry, 1975, 97, (3), pp 836–842.CrossRefGoogle Scholar
29.Marce, L., Julliere, M. and Place, H.Strategy of obstacle avoidance for a mobile robot, RAIRO Automatique, 1981, 15, (1), pp 5–18.Google Scholar
30.McInnes, C.Velocity field path-planning for single and multiple unmanned aerial vehicles, Aeronaut J, 2003, 107, pp 419–426.CrossRefGoogle Scholar
31.Mujumdar, A. and Padhi, R.Evolving philosophies on autonomous obstacle/collision avoidance of unmanned aerial vehicles, J Aerospace Computing, Information, and Communication, 2011, 8, pp 17–41.CrossRefGoogle Scholar
32. Museum of Flight. Insitu Aerosonde Laima, http://www.museumofflight.org/aircraft/insitu-areosonde-laima, accessed: 12 November 2012.Google Scholar
33.Owlia, S.Real-time Autonomous Obstacle Avoidance for Low-altitude Fixed-wing Aircraft, Master’s thesis, 2013, Carleton University.CrossRefGoogle Scholar
34.Richalet, J.Industrial applications of model based predictive control, Automatica, 1993, 29, (5), pp 1251–1274.CrossRefGoogle Scholar
35.Saunders, J., Call, B. and Curtis, A.Static and dynamic obstacle avoidance in miniature air vehicles, 2005, Infotech@Aerospace, Arlington, VA, USA, AIAA, 2005, pp 2005–6950.Google Scholar
36.Shames, I.H.Mechanics of Fluids, Second edition, 1982, McGraw-Hill, New York, USA.Google Scholar
37.Shim, D., Chung, H., Kim, H. and Sastry, S. Autonomous exploration in unknown urban environments for unmanned aerial vehicles, 2005, AIAA GN&C Conference.CrossRefGoogle Scholar
38.Shim, D. and Sastry, S.An evasive maneuvering algorithm for UAVs in see-and-avoid situations, 2007 American Control Conference, New York City, USA, IEEE, July 2007, pp 3886–3891.Google Scholar
39.Tsourdos, A., White, B. and Shanmugavel, M.Cooperative path planning of unmanned aerial vehicles, 2011, American Institute of Aeronautics and Astronautics, Wiley, West Sussex, UK.Google Scholar
40. Unmanned Dynamics, Aerosim Aeronautical Simulation Blockset Version 1.2 Users’s Guide. Hood River, Or, USA.Google Scholar
41.Waydo, S. and Murray, R.Vehicle motion planning using stream functions, 2003 IEEE International Conference on Robotics and Automation, Taipei, Taiwan, IEEE, 2003, Pp 2484–2491.CrossRefGoogle Scholar