Evolutionary Trajectory Planner for Multiple UAVs in Realistic Scenarios

Dept. de Comput. y Autom., Univ. Complutense de Madrid, Madrid, Spain
IEEE Transactions on Robotics (Impact Factor: 2.43). 09/2010; 26(4):619 - 634. DOI: 10.1109/TRO.2010.2048610
Source: IEEE Xplore


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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Available from: Luis de la Torre Cubillo, Jun 14, 2015
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    • "Since the purpose of this paper is not to construct a new set of realistic evaluation functions, we directly employ or derive some existing representative functions in the literature [1], [4], [40] to include several key factors in UAV path planning. Detailed technical justifications of the chosen functions can be found in the corresponding studies, i.e., [1], [4], [40]. Generally, these factors restrict the paths in a geometric manner. "
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    • "The example in Fig. 7 shows how the proposed method can also be used successfully to follow parameterized curves. These kinds of curves appear naturally when a UAV must operate in realistic scenarios, [40]. In this case the position of the reference point, with respect to the global reference frame, is parameterized as a third order polynomial of s. "
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    • "Different ways of controlling the mobility of UAVs swarms have been studied in the literature. The first proposed approaches compute the flight plan in a centralised fashion, like in[3], where evolutionary algorithms are used to optimise the UAVs trajectories. Most of existing techniques consider such centralised computations, either offline or online. "

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