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Velocity field path-planning for single and multiple unmanned aerial vehicles

Published online by Cambridge University Press:  04 July 2016

C. R. McInnes
C. R. McInnes*
Affiliation:
Department of Aerospace Engineering, University of Glasgow, Glasgow, UK
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Abstract

Unmanned aerial vehicles (UAV) have seen a rapid growth in utilisation for reconnaissance, mostly using single UAVs. However, future utilisation of UAVs for applications such as bistatic synthetic aperture radar and stereoscopic imaging, will require the use of multiple UAVs acting cooperatively to achieve mission goals. In addition, to de-skill the operation of UAVs for certain applications will require the migration of path-planning functions from the ground to the UAV. This paper details a computationally efficient algorithm to enable path-planning for single UAVs and to form and re-form UAV formations with active collision avoidance. The algorithm presented extends classical potential field methods used in other domains for the UAV path-planning problem. It is demonstrated that a range of tasks can be executed autonomously, allowing high level tasking of single and multiple UAVs in formation, with the formation commanded as a single entity.

Type
Research Article
Information
The Aeronautical Journal , Volume 107 , Issue 1073 , July 2003 , pp. 419 - 426
Copyright
Copyright © Royal Aeronautical Society 2003 

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References

1. Khatib, O., Real-time obstacle avoidance for manipulators and mobile robots, Int J of Robotics Research, 5, (1), 1986, pp 90–98.Google Scholar
2. Rimon, E. and Koditschek, D., Exact robot navigation using artificial potential functions, 1992, IEEE transactions on robotics and automation, 8, (5), pp 501–518.Google Scholar
3. Sato, K., Dead-Lock motion path planning using the Laplace potential field, Advances in Robotics, 1993, 17, (5), pp 449–461.Google Scholar
4. Guldner, J. and Utkin, V. Sliding mode control for gradient tracking and robot navigation using artificial potential fields, 1995, IEEE Transactions on Robotics and Automation, 11, (2), pp 247–254.Google Scholar
5. De Medio, C., Nicolo, F. and Oriolo, G.. Robot motion planning using vortex fields, Progress in Systems and Control Theory, 1991, 7, pp 237–244.Google Scholar
6. Masoud, A. and Bayoumi, M., Robot navigation using the vector potential approach, 1050–4729/93, 1993, Proceedings of the IEE international conference on robotics and automation, April 1993, Minneapolis.Google Scholar
7. Singh, L., Stephanou, H. and Wen, J., Real-time robot motion control with circulatory fields, 1996, Proceedings of the IEE international conference on robotics and automation, April 1996, Minneapolis.Google Scholar
8. Masoud, A., Using hybrid vector-harmonic potential fields for multi-robot, multi-target navigation in a stationary field, 1996, Proceedings of the IEE international conference on robotics and automation, April 1996, Minneapolis.Google Scholar
9. Zeghal, K., A Review of different approaches based on force fields for airborne conflict resolution, 1998, AIAA Guidance, navigation and control conference, August 1998, Boston.Google Scholar
10. Shapira, I. and Ben-Asher, J., Near-optimal horizontal trajectories for autonomous air vehicles, J Guidance, Control and Dynamics, 1997, 20, (4), pp 735–741.Google Scholar
11. Mcinnes, C., Potential function methods for autonomous spacecraft guidance and control, 1995, AAS 95–447, AAS/AIAA astrodynamics specialist conference, August 1995, Halifax, Nova Scotia.Google Scholar
12. Ghosh, R. and Tomlin, C., Maneuver design for multiple aircraft conflict resolution, 2000, Proceedings of the American control conference, June 2000, Chicago.Google Scholar
13. Volpe, R. and Khosla, P., Manipulator control with superquadratic artificial potential functions: theory and experiments, 1990, IEEE transactions on systems, man and cybernetics, 20, (6), pp 1423–1436.Google Scholar
14. Lorrain, P. and Carson, D. Electromagnetic Fields and Waves, 1970, pp 303–308, Freeman, New York.Google Scholar
15. Arfken, G., Mathematical Methods for Physicists, 1985, pp 78–83, Academic Press, San Diego.Google Scholar
16. Pachter, M., D’Azzo, J. and Dargan, J., Automatic formation flight control, J Guidance, Control and Dynamics, 1994, 17, (6), pp 1380–1383.Google Scholar
17. Wolfe, J., Chichka, D. and Speyer, J., Decentralized controllers for unmanned aerial vehicle formation flight, 1996, Proceedings of the AIAA guidance, navigation and control conference, August 1996, San Diego.Google Scholar
18. Richards, A., Bellingham, J., Tillerson, M. and How, J., Coordination of multiple UAVs, 2002, AIAA-2002-4588, AIAA guidance, navigation and control conference, August 2002, Monterey.Google Scholar