Казанский (Приволжский) федеральный университет, КФУ
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COMBINING VORONOI GRAPH AND SPLINE-BASED APPROACHES FOR A MOBILE ROBOT PATH PLANNING
Форма представленияСтатьи в зарубежных журналах и сборниках
Год публикации2017
Языканглийский
  • Лавренов Роман Олегович, автор
  • Магид Евгений Аркадьевич, автор
  • Хасьянов Айрат Фаридович, автор
  • Свинин Михаил , автор
  • Библиографическое описание на языке оригинала Magid E. Combining Voronoi graph and spline-based approaches for a mobile robot path planning / Lavrenov R., Svinin M., Khasianov A. // Lecture Notes in Electrical Engineering. - 2017. - № 495. - p. 475-496.
    Аннотация Potential function based methods play significant role in both global and local path planning. While these methods are characterized with good reactive behaviour and implementation simplicity, they suffer from a well-known problem of getting stuck in local minima of a navigation function. In this paper we propose a modification of our original spline-based path planning algorithm for a mobile robot navigation, which succeeds to solve local minima problem and considers additional criteria of start and target points visibility to help optimizing the path selection. We apply a Voronoi graph based path as an input for iterative multi criteria optimization algorithm and present a path finding strategy within different homotopies that uses the new method. The algorithm was implemented in Matlab environment and demonstrated significantly better results than the original approach. The comparison was based on success rate, number of iterations and running time of the algorithms. In total, several thousands tests were performed in 18 different simulated environments.
    Ключевые слова MATLAB, Mobile robot, Path planning algorithm, Potential field, Robotics, Simulated experiments, Voronoi diagram
    Название журнала Lecture Notes in Electrical Engineering
    URL https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065493962&doi=10.1007%2f978-3-030-11292-9_24&partnerID=40&md5=346ee5837b437fdb3f42d7de62610ebd
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