Curves and surfaces for CAGD: a practical guide
Curves and surfaces for CAGD: a practical guide
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Evolutionary algorithm based offline/online path planner for UAV navigation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multiple UAV path planning using anytime algorithms
ACC'09 Proceedings of the 2009 conference on American Control Conference
Evolutionary trajectory planner for multiple UAVs in realistic scenarios
IEEE Transactions on Robotics
Feasible UAV path planning using genetic algorithms and Bézier curves
SBIA'10 Proceedings of the 20th Brazilian conference on Advances in artificial intelligence
Waypoint tracking of unmanned aerial vehicles using robust H2 / H? controller
International Journal of Systems, Control and Communications
On the performance comparison of multi-objective evolutionary UAV path planners
Information Sciences: an International Journal
3D Path Planning for Multiple UAVs for Maximum Information Collection
Journal of Intelligent and Robotic Systems
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Military missions are turning to more complicated and advanced automation technology for maximum endurance and efficiency as well as the minimum vital risks. The path planners which generate collision-free and optimized paths are needed to give autonomous operation capability to the Unmanned Aerial Vehicles (UAVs). This paper presents an off-line path planner for UAVs. The path planner is based on Evolutionary Algorithms (EA), in order to calculate a curved path line with desired attributes in a 3-D terrain. The flight path is represented by parameterized B-Spline curves by considering four objectives: the shortest path to the destination, the feasible path without terrain collision, the path with the desired minimum and maximum distance to the terrain, and the path which provides UAV to maneuver with an angle greater than the minimum radius of curvature. The generated path is represented with the coordinates of its control points being the genes of the chromosome of the EA. The proposed method was tested in several 3-D terrains, which are generated with various terrain generator methods that differ with respect to levels of smoothness of the terrain.