@inProceedings{
 title = {MOGAMR: A Multi-Objective Genetic Algorithm for real-time Mission Replanning},
 type = {inProceedings},
 year = {2017},
 identifiers = {[object Object]},
 id = {c17622b4-49e3-3c4d-bb5a-d0d6b33362ce},
 created = {2019-03-18T15:00:29.341Z},
 file_attached = {false},
 profile_id = {77ead516-4fb7-3189-a920-09efa515b9dd},
 last_modified = {2019-03-18T15:00:29.341Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {© 2016 IEEE. From the last few years the interest and repercussion on Unmanned Aerial Vehicle (UAV) technologies have been extended from pure military applications to industrial and societal applications. One of the basic tasks to any UAV problems is related to the Mission Planning. This problem is particularly complex when a set of UAVs is considered. In the field of Multi-UAV Mission Planning, some approaches have been carried out in the last years. However, there are few works related to real-time Mission Replanning, which is the focus of this work. In Mission Replanning, some changes in the mission, such as the arrival of new tasks, require to update the preplanned solution as fast as possible. In this paper a Multi-Objective Genetic Algorithm for Mission Replanning (MOGAMR) is proposed to handle this problem. This approach uses a set of previous plans (or solutions), generated using an oa liffline planning process, in order to initialize the population of the algorithm, then acts as a complete regeneration method. In order to simulate a real-time system we have fixed a time limit of 2 minutes. This has been considered as an appropriate time for a human operator to take a decision. Using this time restriction, a set of experiments adding from 1 to 5 new tasks in the Replanning Problems has been carried out. The experiments show that the algorithm works well with this few number of new tasks during the replanning process generating a set of feasible solutions under the time restriction considered.},
 bibtype = {inProceedings},
 author = {Ramirez-Atencia, C. and Bello-Orgaz, G. and R-Moreno, M.D. and Camacho, D.},
 booktitle = {2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016}
}

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One of the basic tasks to any UAV problems is related to the Mission Planning. This problem is particularly complex when a set of UAVs is considered. In the field of Multi-UAV Mission Planning, some approaches have been carried out in the last years. However, there are few works related to real-time Mission Replanning, which is the focus of this work. In Mission Replanning, some changes in the mission, such as the arrival of new tasks, require to update the preplanned solution as fast as possible. In this paper a Multi-Objective Genetic Algorithm for Mission Replanning (MOGAMR) is proposed to handle this problem. This approach uses a set of previous plans (or solutions), generated using an oa liffline planning process, in order to initialize the population of the algorithm, then acts as a complete regeneration method. In order to simulate a real-time system we have fixed a time limit of 2 minutes. This has been considered as an appropriate time for a human operator to take a decision. Using this time restriction, a set of experiments adding from 1 to 5 new tasks in the Replanning Problems has been carried out. The experiments show that the algorithm works well with this few number of new tasks during the replanning process generating a set of feasible solutions under the time restriction considered.","bibtype":"inProceedings","author":"Ramirez-Atencia, C. and Bello-Orgaz, G. and R-Moreno, M.D. and Camacho, D.","booktitle":"2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016","bibtex":"@inProceedings{\n title = {MOGAMR: A Multi-Objective Genetic Algorithm for real-time Mission Replanning},\n type = {inProceedings},\n year = {2017},\n identifiers = {[object Object]},\n id = {c17622b4-49e3-3c4d-bb5a-d0d6b33362ce},\n created = {2019-03-18T15:00:29.341Z},\n file_attached = {false},\n profile_id = {77ead516-4fb7-3189-a920-09efa515b9dd},\n last_modified = {2019-03-18T15:00:29.341Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {© 2016 IEEE. 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