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@Article{FreitasSousMaca:2018:ReMoAu,
               author = "Freitas, Vander Luis de Souza and Sousa, Fabiano Luis de and 
                         Macau, Elbert Einstein Nehrer",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal 
                         de S{\~a}o Paulo (UNIFESP)}",
                title = "Reactive model for autonomous vehicles formation following a 
                         mobile reference",
              journal = "Applied Mathematical Modelling",
                 year = "2018",
               volume = "61",
                pages = "167--180",
             keywords = "Collective Motion, Reactive agents, Multiagent Systems, 
                         Evolutionary Optimization.",
             abstract = "The emergence of collective motion in nature is ubiquitous and can 
                         be observed from colonies of bacteria to flocks of birds. The 
                         scientific community is interested in understanding how the local 
                         interactions drive the crowd toward global behaviors. This paper 
                         presents an agent-based reactive model for groups of vehicles that 
                         aims to make the formation to follow a moving reference, 
                         represented as a virtual agent. The model is called reactive 
                         because the agents do not keep previous information but only 
                         respond to the current system state. Moreover, they only 
                         communicate with their close neighbors, limited by their sensory 
                         radius, except with the virtual agent that can be seen by everyone 
                         at the whole time. The aim of the model is to group the agents 
                         around the virtual agent while it moves to desirable directions. 
                         We solve the inverse problem of parameter estimation in order to 
                         drive the model toward specific objectives. This task is performed 
                         with the Generalized Extremal Optimization (GEO) algorithm, and 
                         the results are tested with path planning scenarios.",
                  doi = "10.1016/j.apm.2018.04.011",
                  url = "http://dx.doi.org/10.1016/j.apm.2018.04.011",
                 issn = "0307-904X",
                label = "lattes: 5339877279308939 1 FreitasSousMaca:2018:ReMoAu",
             language = "en",
           targetfile = "freitas_reactive.pdf",
                  url = "https://authors.elsevier.com/a/1W-OX,703pyWEB",
        urlaccessdate = "24 nov. 2020"
}


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