Journal
IEEE TRANSACTIONS ON ROBOTICS
Volume 26, Issue 4, Pages 619-634Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TRO.2010.2048610
Keywords
Aerial robotics; multiobjective evolutionary algorithms (EAs); path planning for multiple mobile robot systems
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Funding
- Community of Madrid [S-0505/DPI-0391]
- Spanish Ministry of Education and Science [DPI2006-15661-C02-01, DPI2009-14552-C02-01]
- European Aeronautic Defense and Space Company (Construcciones Aeronauticas Sociedad Anonima) [353/2005]
- Spanish Postdoctoral Grant [EX-2007-0915]
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This paper presents a path planner for multiple unmanned aerial vehicles (UAVs) based on evolutionary algorithms (EAs) for realistic scenarios. The paths returned by the algorithm fulfill and optimize multiple criteria that 1) are calculated based on the properties of real UAVs, terrains, radars, and missiles and 2) are structured in different levels of priority according to the selected mission. The paths of all the UAVs are obtained with the multiple coordinated agents coevolution EA (MCACEA), which is a general framework that uses an EA per agent (i.e., UAV) that share their optimal solutions to coordinate the evolutions of the EAs populations using cooperation objectives. This planner works offline and online by means of recalculating parts of the original path to avoid unexpected risks while the UAV is flying. Its search space and computation time have been reduced using some special operators in the EAs. The successful results of the paths obtained in multiple scenarios, which are statistically analyzed in the paper, and tested against a simulator that incorporates complex models of the UAVs, radars, and missiles, make us believe that this planner could be used for real-flight missions.
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