RT Book, Section
T1 Decision making among alternative routes for UAVs in dynamic environments
A1 Ruz Ortíz, José Jaime
A1 Arévalo, Orlando
A1 Pajares Martinsanz, Gonzalo
A1 Cruz García, Jesús Manuel de la
AB This paper presents an approach to trajectory generation for Unmanned Aerial Vehicles (UAV)) by using Mixed Integer Linear Programming (MILP) and a modification of the A* algorithm to optimize paths in dynamic environments, particularly having pop-ups with a known future probability of appearance. Each pop-up leads to one or several possible evasion maneuvers, characterised with a set Of values used as decision making parameters in an Integer Linear Programming (ILP) model that optimizes the final route by choosing the Most suitable alternative trajectories, according to the imposed constrains such as maximum fuel consumption and spent time. The model of the system in MILP and A* algorithms is presented as well as the ILP formulation for decision making. Results and discussions are given to promote future real tiem implementations.
PB IEEE
SN 978-1-4244-0826-9
YR 2007
FD 2007
LK https://hdl.handle.net/20.500.14352/53300
UL https://hdl.handle.net/20.500.14352/53300
LA eng
NO [1] J. Bellingham, A. Richards and J. How, “Receding Horizon Control of Autonomous Aerial Vehicles”, Proc. of American Control Conference, 2002.[2] B. Berg, and M. Chain, “Monte Carlo Simulations and their Statistical Analysis”, World Scientific (ISBN 981-238-935-0), 2004.[3] S.A. Borto, “Path planning for UAVs”. Proc. of the American Control Conference, pp. 364-368, 2000.[4] M. Deloura, “Game Programming Gems”, Charles River Media, Inc., chapters 3.3 to 3.6, 2000.[5] J. How, E. King, and Y. Kuwata, “Flight Demonstrations of Cooperative Control for UAV Teams”, AIAA 3rd Unmanned Unlimited Technical Conference, Workshop and Exhibit, 2004.[6] ILOG, ILOG CPLEX 9.1 User’s guide, 2003.[7] Y. Kuwata and J. How, “Three Dimensional Receding Horizon Control for UAVs”, AIAA Guidance, Navigation, and Control Conference and Exhibit, 2004.[8] A. Richards, and J. How, “Aircraft Trajectory Planning with Collision Avoidance Using MILP”, Proc. Of the IEEE American Control Conference, pp. 1936-1941, 2002.[9] J. Ruz, O. Arévalo, J. de la Cruz and G.Pajares, “Using MILP for UAVs Trajectory Optimization under Radar Detection Risk”, Proc. of the 11th IEEE Conference on Emerging Technologies and Factory Automation, 2006.[10] T. Schouwenaars, B. De Moor, E. Feron, and J. How, “Mixed Integer Programming for Multi-Vehicle Path Planning”. Proc. of the 2001 European Control Conference, 2001.[11] T. Schouwenaars, J. How and E. Feron, “Receding Horizon Path Planning with Implicit Safety Guarantees”, Proc. of American Control Conference, 2004.[12] B. Stout, “Smart Moves: Intelligent Path-Finding”, Game Developer Magazine, 1996.[13] U. Zengin, and A. Dogan, “Probabilistic Trajectory Planning for UAVs in Dynamic Environments”. Proc. of AIAA 3rd "Unmanned Unlimited" Technical Conference, Workshop and Exhibit, pp. 1-12, 2004.
NO © 2007 IEEE.This research was funded by the Community ofMadrid, project “COSICOLOGI” S-0505/DPI-0391, bythe Spanish Ministry of Education and Science, project“Planning, simulation and control for cooperation ofmultiple UAVs and MAVs” DPI2006-15661-C02-01,and by EADS (CASA), project 353/2005.IEEE International Conference on Emerging Technologies and Factory Automation (12th. Sep 25-28, 2007. Patras, Grecia)
NO Community of Madrid
NO Ministry of Education and Science
NO EADS (CASA)
DS Docta Complutense
RD 11 dic 2023