%0 Journal Article %1 noauthororeditor %A AlRaslan, Mohammad %D 2022 %E AlRaslan, Mohammad %J International Journal of Computational Science and Information Technology (IJCSITY) %K Parallel algorithm genetic %N 2 %P 01-15 %R 10.5121/ijcsity.2022.10201 %T UAVPATH PLANNING USINGGENETIC ALGORITHM WITH PARALLELIMPLEMENTATION %U https://aircconline.com/ijcsity/V10N2/10222ijcsity01.pdf %V 10 %X The objective of path planning algorithms is to find the optimal path from a source position to a target position. This paper proposes a real-time path planner for UAVs based on the genetic algorithm. The proposed approach does not identify any specific points outside or between obstacles to solve the problems of the invisible path. In addition, this approach uses no additional steps in the genetic algorithm to handle the problems resulting from generating points inside the obstacles, or the intersection between path segments with obstacles. For these reasons, this paper introduces a simple evaluation method that takes into account the intersections between the path segments and obstacles to find a collision-free and near to optimal path. This evaluation method take into account overlapped and intersected obstacles. The sequential implementation for all of the genetic algorithm steps is detailed. This paper explores the Parallel Genetic Algorithms (PGA) models and introduces the parallel implementation of the proposed path planner on multi-core processors using OpenMP. The execution time of the proposed parallel implementation is reduced compared to sequential execution.

@article{noauthororeditor, abstract = {The objective of path planning algorithms is to find the optimal path from a source position to a target position. This paper proposes a real-time path planner for UAVs based on the genetic algorithm. The proposed approach does not identify any specific points outside or between obstacles to solve the problems of the invisible path. In addition, this approach uses no additional steps in the genetic algorithm to handle the problems resulting from generating points inside the obstacles, or the intersection between path segments with obstacles. For these reasons, this paper introduces a simple evaluation method that takes into account the intersections between the path segments and obstacles to find a collision-free and near to optimal path. This evaluation method take into account overlapped and intersected obstacles. The sequential implementation for all of the genetic algorithm steps is detailed. This paper explores the Parallel Genetic Algorithms (PGA) models and introduces the parallel implementation of the proposed path planner on multi-core processors using OpenMP. The execution time of the proposed parallel implementation is reduced compared to sequential execution.}, added-at = {2022-06-07T12:13:19.000+0200}, author = {AlRaslan, Mohammad}, biburl = {https://www.bibsonomy.org/bibtex/248fea57b09303c6ed729f7ef4472e09f/claure}, doi = {10.5121/ijcsity.2022.10201}, editor = {AlRaslan, Mohammad}, interhash = {a793a896fbd72ff3edfc5f246552f866}, intrahash = {48fea57b09303c6ed729f7ef4472e09f}, issn = {ISSN : 2320-7442 (Online) ; 2320 - 8457 (Print)}, journal = {International Journal of Computational Science and Information Technology (IJCSITY)}, keywords = {Parallel algorithm genetic}, language = {English}, month = may, number = 2, pages = {01-15}, timestamp = {2022-06-07T12:13:19.000+0200}, title = {UAVPATH PLANNING USINGGENETIC ALGORITHM WITH PARALLELIMPLEMENTATION}, url = {https://aircconline.com/ijcsity/V10N2/10222ijcsity01.pdf}, volume = 10, year = 2022 }