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Reactive route selection from pre-calculated trajectories – application to micro-UAV path planning

Published online by Cambridge University Press:  27 January 2016

J. Hall*
Affiliation:
Division of Information Engineering, Engineering Department, Cambridge University, Cambridge, UK
D. Anderson*
Affiliation:
Aerospace Sciences Research Division, School of Engineering, Glasgow University, Glasgow, UK

Abstract

Operating micro-UAVs autonomously in complex urban areas requires that the guidance algorithms on-board are robust to changes in the operating environment. Limited endurance capability demands an optimal guidance algorithm, which will change as the environment does. All optimal path-planning routines are computationally intensive, with processor load a function of the environmental complexity. This paper presents a new algorithm, the reactive route selection algorithm, for storing a bank of optimal trajectories computed off-line and blending between these optimal trajectories as the operating environment changes. An example is presented using a mixed-integer linear program to generate the optimal trajectories.

Type
Technical Note
Copyright
Copyright © Royal Aeronautical Society 2011 

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References

1. Rathbun, D., Kragelund, S., Pongpunwattana, A. and Capozzi, B. An Evolution Based Path Planning Algorithm for Autonomous Motion of a UAV Through Uncertain Environments, 21st Digital Avionics Systems Conference, 2002, pp 8D2-1-8D2-12.Google Scholar
3. Bortoff, S.A. Path Planning for UAVs, IEEE American Control Conference, Chicago, Illinois, USA, 2000, pp 364368.Google Scholar
4. Ryan, A., Zennaro, M., Howell, A., Sengupta, R. and Hedrick, J.K. An Overview of Emerging Results on Cooperative UAV Control, 43rd IEEE Conference on Decision and Control, Atlantis, Paradise Island, Bahamas, 2004, pp 602607.Google Scholar
5. Geiger, B.R., Horn, J.F., Delullo, A.M., Long, L.N. and Niessner, A.F. Optimal Path Planning of UAVs Using Direct Collocation with Nonlinear Programming, AIAA Guidance, Navigation and Control Conference, Keystone, Colorado, USA, 2006, pp 801805.Google Scholar
6. Durrant-Whyte, H. and Bailey, T. Simultaneous localisation and mapping (SLAM): Part 1 The Essential Algorithms, Robotics and Automation Magazine, 13, (2), pp 99110.Google Scholar
7. Nilsson, N.J. Principles of Artificial Intelligence, Tioga Publishing Company, 1980.Google Scholar
8. Ferguson, D. and Stentz, A. The Delayed D* Algorithm for Efficient Path Replanning, IEEE International Conference on Robotics and Automation, 2005, pp 20452050.Google Scholar
9. Lavalle, S.M. Rapidly-Exploring Random Trees: A New Tool for Path Planning, Iowa State University, Iowa, USA, C. S. Dept, 98-11, 1998.Google Scholar
10. Eele, A. and Richards, A. Path-planning with avoidance using nonlinear branch-and-bound optimization, AIAA J Guidance, Control and Dynamics, March-April, 32, (2), pp 384394.Google Scholar
11. Godbole, D., Samad, T. and Gopal, V. Active Multi-Modal Control for Dynamic Maneuver Optimization of Unmanned Aerial Vehicles, IEEE International Conference on Robotics and Automation, San Francisco, CA, USA, 2000, pp 12571262.Google Scholar
12. Paul, T., Krogstad, T.R. and Gravdahl, J.T. Modelling of UAV formation flight using 3D potential fields, Simulation Modeling Practice and Theory, 16, (9), pp 14531462.Google Scholar
13. Schouwenaars, T., Demoor, B., Feron, E. and How, J.P. MixedInteger Programming for Multi-Vehicle Path Planning, European Control Conference, Porto, Portugal, 2001, pp 26032608.Google Scholar
14. Bellingham, J., Tillerson, M., Richards, A. and How, J.P. Coordination and Control of Multiple UAVs, AIAA Guidance, Navigation and Control Conference and Exhibit, Monteray, CA, USA, 2002, pp D12D18.Google Scholar
15. Kabamba, P.T., Meerkov, S.M. and Zeitz, F.H. Optimal path planning for unmanned combat aerial vehicles to defeat radar tracking, AIAA J Guidance, Control and Dynamics, March-April, 29, (2), pp 279288.Google Scholar
16. Wang, Y. and Boyd, S. Fast Model Predictive Control Using Online Optimization, 17th World Congress, Seoul, S Korea, 2008, pp 67946979.Google Scholar
17. Anderson, D., Loo, M. and Brignall, N. Fast model predictive control of the Nadar singularity in electro-optic systems, AIAA J Guidance, Control and Dynamics, March-April, 32, (2), pp 626632.Google Scholar
18. Bemporad, A., Morari, M., Dua, V. and Pistikopoulos, E.N. The explicit linear quadratic regulator for constrained systems, Automatica, 38, pp 320.Google Scholar
19. Dua, V., Bozinis, N.A. and Pistikopoulos, E.N. A New Multiparametric Mixed-Integer Quadratic Programming Algorithm, 34th European Symposium of the Working Party on Computer Aided Process Engineering, 2001, pp 979984.Google Scholar
20. Dever, C., Mettler, B., Feron, E., Popovic, J. and Mcconley, M. Nonlinear trajectory generation for autonomous vehicles via parameterized maneuver classes, AIAA J Guidance, Control and Dynamics, March-April, 29, (2), pp 289302.Google Scholar
21. CPLEX, ILOG CPLEX 9.0 User’s Manual, 2003.Google Scholar