scholarly journals Improved Bat Algorithm for UAV Path Planning in Three-Dimensional Space

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 20100-20116
Author(s):  
Xianjin Zhou ◽  
Fei Gao ◽  
Xi Fang ◽  
Zehong Lan
Robotica ◽  
1990 ◽  
Vol 8 (3) ◽  
pp. 195-205 ◽  
Author(s):  
T.M. Rao ◽  
Ronald C. Arkin

SUMMARYThe problem of path planning for a mobile robot has been studied extensively in recent literature. Much of the work in this area is devoted to the study of path planning for an earth-bound robot in two dimensions. In this paper, we explore the problem for a robot that can fly in three dimensional space or crawl on 3D surfaces or use a combination of both. We assume that the obstacles can be modeled as polyhedral objects.


2011 ◽  
Vol 264-265 ◽  
pp. 6-11
Author(s):  
Xu Yue Wang ◽  
Jun Wang ◽  
L.J. Wang ◽  
W.J. Xu ◽  
D.M. Guo

A method is presented based on geometric-curvature characteristics in which a scanning path planning for laser bending of a straight tube into a curve tube in a two- and three-dimensional space. In a two-dimensional (plane) bending, the steel tube is divided into several segments according to the extreme point and inflection point of the desired shape of the tube, taking the extreme point as the initial place of the path planning, using different scanning space for every segment in order to identify the scanning paths. For a tube bending in a three-dimensional space, a projection decomposition method is used, where the three-dimensional is decomposed into two two-dimensions, and respective scanning path planning and process parameters are thus acquired. By combining the data in the two-dimensional planes, the three-dimensional scanning path plan was obtained. Finally, an experimental verification is carried out to bend straight tubes into a two-dimensional sinusoidal and a three-dimensional helical coil-shaped tube. The results show that the proposed method of scanning path planning is effective and feasible.


1997 ◽  
Vol 84 (1) ◽  
pp. 176-178
Author(s):  
Frank O'Brien

The author's population density index ( PDI) model is extended to three-dimensional distributions. A derived formula is presented that allows for the calculation of the lower and upper bounds of density in three-dimensional space for any finite lattice.


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