IEEE Conference on Decision and Control and European Control Conference 2011
DOI: 10.1109/cdc.2011.6160385
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3D path planning in a threat environment

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Cited by 36 publications
(26 citation statements)
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“…The radius of DD is set to be an optimal value 10 Á le [15,16]. In the 2D environment, we set the UAV motion parameters as: v min ¼ 0:5 units/s, v max ¼ 1:5 units/s, a v ¼ 0:02 units/s 2 , r min ¼ sl min ¼ 5 units; the DT velocity v D = 1 unit/s; TH= 15 seconds; UAV starts at [10,90], ends at [95,5]; DT starts at [10,10]; the maximum threat radius of radar R max ¼ 38 units; the maximum static threat radius of DT RD max ¼ 10 units; the radius of SH is 33 units; the v p in the non-membership function is 1 unit/s. In the 3D environment, the motion parameters of UAV and DT are set to be five times of those in the two dimensional scenario; the radar characteristic coefficient K R ¼ 0:003; TH=25 seconds; UAV starts at [950, 80 ,80], ends at [50, 950, 100]; DT starts at [950,950,0]; R max = 380 units; RD max ¼ 50 units; v p = 5 units/s; the radius of SH is 150 units.…”
Section: Simulation Environments Settingmentioning
confidence: 99%
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“…The radius of DD is set to be an optimal value 10 Á le [15,16]. In the 2D environment, we set the UAV motion parameters as: v min ¼ 0:5 units/s, v max ¼ 1:5 units/s, a v ¼ 0:02 units/s 2 , r min ¼ sl min ¼ 5 units; the DT velocity v D = 1 unit/s; TH= 15 seconds; UAV starts at [10,90], ends at [95,5]; DT starts at [10,10]; the maximum threat radius of radar R max ¼ 38 units; the maximum static threat radius of DT RD max ¼ 10 units; the radius of SH is 33 units; the v p in the non-membership function is 1 unit/s. In the 3D environment, the motion parameters of UAV and DT are set to be five times of those in the two dimensional scenario; the radar characteristic coefficient K R ¼ 0:003; TH=25 seconds; UAV starts at [950, 80 ,80], ends at [50, 950, 100]; DT starts at [950,950,0]; R max = 380 units; RD max ¼ 50 units; v p = 5 units/s; the radius of SH is 150 units.…”
Section: Simulation Environments Settingmentioning
confidence: 99%
“…The other method utilizes a probabilistic model. Albeit it is practicable, it is coarse because the complicated model parameters cannot be considered thoroughly and accurately [5]. Besides, the probabilistic model has no special mechanism for expressing the uncertainty of a threat.…”
mentioning
confidence: 99%
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“…Sampling based algorithms [1], graph based search algorithms [2,3], mathematic model based algorithms [4,5], and bioinspired algorithms [6][7][8] are the fundamental families of path planning algorithms [9]. Most of the path planning solutions were originally developed for 2D problems and then extended to the third dimension, thus increasing the complexity of the approach and its computational cost.…”
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%