@Article{VérasMedeGuim:2019:RaExRa,
               author = "V{\'e}ras, Luiz Gustavo Diniz de Oliveira and Medeiros, Felipe L. 
                         L. and Guimar{\~a}es, Lamartine N. F.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         de Estudos Avan{\c{c}}ados (IEAv)} and {Instituto de Estudos 
                         Avan{\c{c}}ados (IEAv)}",
                title = "Rapidly exploring Random Tree* with a sampling method based on 
                         Sukharev grids and convex vertices of safety hulls of obstacles",
              journal = "International Journal of Advanced Robotic Systems",
                 year = "2019",
               volume = "16",
               number = "1",
                month = "Jan.",
             keywords = "Path planning, RRT*, sampling, convex vertices, Sukharev grids.",
             abstract = "The path planning for an Unmanned Aerial Vehicles ensures that a 
                         dynamically feasible and collision-free path is planned between a 
                         start and an end point within a navigation environment. One of the 
                         most used algorithms for path planning is the Rapidly exploring 
                         Random Tree, where each one of its nodes is randomly collected 
                         from the navigation environment until the start and end navigation 
                         points are connected through them. The Rapidly exploring Random 
                         Tree algorithm is probabilistically complete, which ensures that a 
                         path, if one exists, will be found if the quantity of sampled 
                         nodes increases infinitely. However, there is no guarantee that 
                         the shortest path to a navigation environment will be planned by 
                         Rapidly exploring Random Tree algorithm. The Rapidly exploring 
                         Random Tree* algorithm is a path planning method that guarantees 
                         the shorter path length to the UAV but at a high computational 
                         cost. Some authors state that by informing sample collection to 
                         specific positions on the navigation environment, it would be 
                         possible to improve the planning time of this algorithm, as 
                         example of the Rapidly exploring Random Tree*-Smart algorithm, 
                         that utilize intelligent sampling and path optimization procedures 
                         to this purpose. This article introduces a novel Rapidly exploring 
                         Random Tree*-based algorithm, where a new sampling process based 
                         on Sukharev grids and convex vertices of the security hulls of 
                         obstacles is proposed. Computational tests are performed to verify 
                         that the new sampling strategy improves the planning time of 
                         Rapidly exploring Random Tree*, which can be applied to real-time 
                         navigation of Unmanned Aerial Vehicles. The results presented 
                         indicate that the use of convex vertices and grid of Sukharev 
                         accelerate the planning time of the Rapidly exploring Random Tree* 
                         and show better performance than the Rapidly exploring Random 
                         Tree*-Smart algorithm in several navigation environments with 
                         different quantities and spatial distributions of polygonal 
                         obstacles.",
                  doi = "10.1177/1729881419825941",
                  url = "http://dx.doi.org/10.1177/1729881419825941",
                 issn = "1729-8806",
             language = "en",
           targetfile = "veras_rapidly.pdf",
        urlaccessdate = "27 abr. 2024"
}