Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008
DOI: 10.1145/1389095.1389386
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3-D path planning for the navigation of unmanned aerial vehicles by using evolutionary algorithms

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Cited by 72 publications
(39 citation statements)
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“…Usually, a common practice in the context of UAV is to integrate different objectives. In the literature, some previous work solve this problem by using weighted sum [4], [9], [19]- [21]. However, those weighted parameters appear very difficult to fine-tune as different objectives are in different scales.…”
Section: Selection Of the Final Solutionmentioning
confidence: 99%
See 1 more Smart Citation
“…Usually, a common practice in the context of UAV is to integrate different objectives. In the literature, some previous work solve this problem by using weighted sum [4], [9], [19]- [21]. However, those weighted parameters appear very difficult to fine-tune as different objectives are in different scales.…”
Section: Selection Of the Final Solutionmentioning
confidence: 99%
“…However, it is not intuitive for such a comparison as each waypoint receives a vector of fitness values rather than a scalar. To deal with this difficulty, most previous work try to combine the fitness vector into a scalar with some weight parameters [4], [9], [19]- [21]. However, those weight parameters appear very difficult to fine-tune as different constraints and objectives are in different scales.…”
Section: B Reproduction and Selectionmentioning
confidence: 99%
“…These algorithms focus on path planning on a 2D plane. For path planning in a 3D space, researchers have proposed algorithms to create various optimal or sub-optimal paths [5] [6]. These algorithms provide a method of creating paths using waypoints for the 3D movement of aerial vehicles and aerial robots [7].…”
Section: Introductionmentioning
confidence: 99%
“…Famous path planning methods include Visibility Graphs [2] which plan via connecting the visible nodes of the dangerous region, Rapidly-exploring Random Tree [3] which samples the whole configuration space randomly to guarantee probabilistic completeness, A* [4] algorithm which is successful in handling path planning problems by introducing heuristic graph search algorithm, Evolutionary algorithms [5] which are inspired by biological behaviour, and mathematic model based method [6] which tries to find the best path under kinodynamic constraints with absolute smooth path etc. However, classical methods output discrete way points which are unsuitable for UAVs to follow with a certain high speed.…”
Section: Introductionmentioning
confidence: 99%