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Extensions of sampling-based approaches to path planning in complex cost spaces: applications to robotics and structural biology

Abstract : Planning a path for a robot in a complex environment is a crucial issue in robotics. So-called probabilistic algorithms for path planning are very successful at solving dicult problems and are applied in various domains, such as aerospace, computer animation, and structural biology. However, these methods have traditionally focused on nding paths avoiding collisions, without considering the quality of these paths. In recent years, new approaches have been developed to generate high-quality paths: in robotics, this can mean nding paths maximizing safety or control; in biology, this means nding motions minimizing the energy variation of a molecule. In this thesis, we propose several extensions of these methods to improve their performance and allow them to solve ever more dicult problems. The applications we present stem from robotics (industrial inspection and aerial manipulation) and structural biology (simulation of molecular motions and exploration of energy landscapes).
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https://tel.archives-ouvertes.fr/tel-01079963
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Submitted on : Tuesday, November 4, 2014 - 10:40:53 AM
Last modification on : Thursday, June 10, 2021 - 3:05:13 AM
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  • HAL Id : tel-01079963, version 1

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Didier Devaurs. Extensions of sampling-based approaches to path planning in complex cost spaces: applications to robotics and structural biology. Robotics [cs.RO]. INP DE TOULOUSE, 2014. English. ⟨tel-01079963⟩

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