Employing Multiple Unmanned Aerial Vehicles for Co-Operative Path Planning
Abstract In this paper, we work to develop a path planning solution for a group of Unmanned Aerial Vehicles (UAVs) using a Mixed Integer Linear Programming (MILP) approach. Co-operation among team members not only helps reduce mission time, it makes the execution more robust in dynamic environments....
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Online Access: | https://doi.org/10.5772/56286 |
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doaj-7142e1e1fcb74bdcaf5a968ceb35142c2020-11-25T02:55:15ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142013-05-011010.5772/5628610.5772_56286Employing Multiple Unmanned Aerial Vehicles for Co-Operative Path PlanningDurdana Habib0Habibullah Jamal1Shoab A. Khan2 Dept. of Electrical Engineering, National University of Computer & Emerging Sciences, Islamabad, Pakistan Dept. of Electrical Engineering, University of Engineering & Technology, Taxila, Pakistan Dept. of Computer Systems Engineering, National University of Sciences & Technology, Rawalpindi, PakistanAbstract In this paper, we work to develop a path planning solution for a group of Unmanned Aerial Vehicles (UAVs) using a Mixed Integer Linear Programming (MILP) approach. Co-operation among team members not only helps reduce mission time, it makes the execution more robust in dynamic environments. However, the problem becomes more challenging as it requires optimal resource allocation and is NP-hard. Since UAVs may be lost or may suffer significant damage during the course of the mission, plans may need to be modified in real-time as the mission proceeds. Therefore, multiple UAVs have a better chance of completing a mission in the face of failures. Such military operations can be treated as a variant of the Multiple Depot Vehicle Routing Problem (MDVRP). The proposed solution must be such that m UAVs start from multiple source locations to visit n targets and return to a set of destination locations such that (1) each target is visited exactly by one of the chosen UAVs (2) the total distance travelled by the group is minimized and (3) the number of targets that each UAV visits may not be less than K or greater than L.https://doi.org/10.5772/56286 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Durdana Habib Habibullah Jamal Shoab A. Khan |
spellingShingle |
Durdana Habib Habibullah Jamal Shoab A. Khan Employing Multiple Unmanned Aerial Vehicles for Co-Operative Path Planning International Journal of Advanced Robotic Systems |
author_facet |
Durdana Habib Habibullah Jamal Shoab A. Khan |
author_sort |
Durdana Habib |
title |
Employing Multiple Unmanned Aerial Vehicles for Co-Operative Path Planning |
title_short |
Employing Multiple Unmanned Aerial Vehicles for Co-Operative Path Planning |
title_full |
Employing Multiple Unmanned Aerial Vehicles for Co-Operative Path Planning |
title_fullStr |
Employing Multiple Unmanned Aerial Vehicles for Co-Operative Path Planning |
title_full_unstemmed |
Employing Multiple Unmanned Aerial Vehicles for Co-Operative Path Planning |
title_sort |
employing multiple unmanned aerial vehicles for co-operative path planning |
publisher |
SAGE Publishing |
series |
International Journal of Advanced Robotic Systems |
issn |
1729-8814 |
publishDate |
2013-05-01 |
description |
Abstract In this paper, we work to develop a path planning solution for a group of Unmanned Aerial Vehicles (UAVs) using a Mixed Integer Linear Programming (MILP) approach. Co-operation among team members not only helps reduce mission time, it makes the execution more robust in dynamic environments. However, the problem becomes more challenging as it requires optimal resource allocation and is NP-hard. Since UAVs may be lost or may suffer significant damage during the course of the mission, plans may need to be modified in real-time as the mission proceeds. Therefore, multiple UAVs have a better chance of completing a mission in the face of failures. Such military operations can be treated as a variant of the Multiple Depot Vehicle Routing Problem (MDVRP). The proposed solution must be such that m UAVs start from multiple source locations to visit n targets and return to a set of destination locations such that (1) each target is visited exactly by one of the chosen UAVs (2) the total distance travelled by the group is minimized and (3) the number of targets that each UAV visits may not be less than K or greater than L. |
url |
https://doi.org/10.5772/56286 |
work_keys_str_mv |
AT durdanahabib employingmultipleunmannedaerialvehiclesforcooperativepathplanning AT habibullahjamal employingmultipleunmannedaerialvehiclesforcooperativepathplanning AT shoabakhan employingmultipleunmannedaerialvehiclesforcooperativepathplanning |
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