@article{1437818,
      recid = {1437818},
      author = {Carabaza, Sara Pérez.},
      title = {Multi-UAS minimum time search in dynamic and uncertain  environments /},
      publisher = {Springer,},
      address = {Cham, Switzerland :},
      pages = {1 online resource},
      year = {2021},
      note = {"Doctoral thesis accepted by Universidad Complutense de  Madrid, Spain."},
      abstract = {This book proposes some novel approaches for finding  unmanned aerial vehicle trajectories to reach targets with  unknown location in minimum time. At first, it reviews  probabilistic search algorithms that have been used for  dealing with the minimum time search (MTS) problem, and  discusses how metaheuristics, and in particular the ant  colony optimization algorithm (ACO), can help to find  high-quality solutions with low computational time. Then,  it describes two ACO-based approaches to solve the discrete  MTS problem and the continuous MTS problem, respectively.  In turn, it reports on the evaluation of the ACO-based  discrete and continuous approaches to the MTS problem in  different simulated scenarios, showing that the methods  outperform in most all the cases over other  state-of-the-art approaches. In the last part of the  thesis, the work of integration of the proposed techniques  in the ground control station developed by Airbus to  control ATLANTE UAV is reported in detail, providing  practical insights into the implementation of these methods  for real UAVs.},
      url = {http://library.usi.edu/record/1437818},
      doi = {https://doi.org/10.1007/978-3-030-76559-0},
}