Jaillet, Leonard Georges
Total activity: 11
Research group
ROBiri - IRI Robotics Group
Institute
Institute of Robotics and Industrial Informatics
E-mail
ljailletiri.upc.edu
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Scientific and technological production
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1 to 11 of 11 results
  • Exploring the energy landscapes of flexible molecular loops using higher-dimensional continuation

     Porta Pleite, Josep M.; Jaillet, Leonard Georges
    Journal of computational chemistry
    Date of publication: 2013
    Journal article

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    Path planning under kinematic constraints by rapidly exploring manifolds  Open access

     Jaillet, Leonard Georges; Porta Pleite, Josep M.
    IEEE transactions on robotics
    Date of publication: 2013
    Journal article

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    The situation arising in path planning under kinematic constraints, where the valid configurations define a manifold embedded in the joint ambient space, can be seen as a limit case of the well-known narrow corridor problem. With kinematic constraints, the probability of obtaining a valid configuration by sampling in the joint ambient space is not low but null, which complicates the direct application of sampling-based path planners. This paper presents the AtlasRRT algorithm, which is a planner especially tailored for such constrained systems that builds on recently developed tools for higher-dimensional continuation. These tools provide procedures to define charts that locally parametrize a manifold and to coordinate the charts, forming an atlas that fully covers it. AtlasRRT simultaneously builds an atlas and a bidirectional rapidly exploring random tree (RRT), using the atlas to sample configurations and to grow the branches of the RRTs, and the RRTs to devise directions of expansion for the atlas. The efficiency of AtlasRRT is evaluated in several benchmarks involving high-dimensional manifolds embedded in large ambient spaces. The results show that the combined use of the atlas and the RRTs produces a more rapid exploration of the configuration space manifolds than existing approaches.

    The situation arising in path planning under kinematic constraints, where the valid configurations define a manifold embedded in the joint ambient space, can be seen as a limit case of the well-known narrow corridor problem. With kinematic constraints, the probability of obtaining a valid configuration by sampling in the joint ambient space is not low but null, which complicates the direct application of sampling-based path planners. This paper presents the AtlasRRT algorithm, which is a planner especially tailored for such constrained systems that builds on recently developed tools for higher-dimensional continuation. These tools provide procedures to define charts that locally parametrize a manifold and to coordinate the charts, forming an atlas that fully covers it. AtlasRRT simultaneously builds an atlas and a bidirectional rapidly exploring random tree (RRT), using the atlas to sample configurations and to grow the branches of the RRTs, and the RRTs to devise directions of expansion for the atlas. The efficiency of AtlasRRT is evaluated in several benchmarks involving high-dimensional manifolds embedded in large ambient spaces. The results show that the combined use of the atlas and the RRTs produces a more rapid exploration of the configuration space manifolds than existing approaches.

    Postprint (author’s final draft)

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    Efficient asymptotically-optimal path planning on manifolds  Open access  awarded activity

     Jaillet, Leonard Georges; Porta Pleite, Josep M.
    Robotics and autonomous systems
    Date of publication: 2013
    Journal article

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    This paper presents an efficient approach for asymptotically-optimal path planning on implicitly-defined configuration spaces. Recently, several asymptotically-optimal path planners have been introduced, but they typically exhibit slow convergence rates. Moreover, these planners can not operate on the configuration spaces that appear in the presence of kinematic or contact constraints, such as when manipulating an object with two arms or with a multifingered hand. In these cases, the configuration space usually becomes an implicit manifold embedded in a higher-dimensional joint ambient space. Existing sampling-based path planners on manifolds focus on finding a feasible solution, but they do not optimize the quality of the path in any sense and, thus, the returned solution is usually not adequate for direct execution. In this paper, we adapt several techniques to accelerate the convergence of the asymptotically-optimal planners and we use higher-dimensional continuation tools to deal with the case of implicitly-defined configuration spaces. The performance of the proposed approach is evaluated through various experiments.

    This paper presents an efficient approach for asymptotically-optimal path planning on implicitly-defined configuration spaces. Recently, several asymptotically-optimal path planners have been introduced, but they typically exhibit slow convergence rates. Moreover, these planners can not operate on the configuration spaces that appear in the presence of kinematic or contact constraints, such as when manipulating an object with two arms or with a multifingered hand. In these cases, the configuration space usually becomes an implicit manifold embedded in a higher-dimensional joint ambient space. Existing sampling-based path planners on manifolds focus on finding a feasible solution, but they do not optimize the quality of the path in any sense and, thus, the returned solution is usually not adequate for direct execution. In this paper, we adapt several techniques to accelerate the convergence of the asymptotically-optimal planners and we use higher-dimensional continuation tools to deal with the case of implicitly-defined configuration spaces. The performance of the proposed approach is evaluated through various experiments.

    Postprint (author’s final draft)

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    An open-source toolbox for motion analysis of closed-chain mechanisms  Open access

     Porta Pleite, Josep M.; Ros Giralt, Lluis; Bohigas Nadal, Oriol; Manubens Ferriol, Montserrat; Rosales Gallegos, Carlos; Jaillet, Leonard Georges
    Computational Kinematics
    Presentation's date: 2013
    Presentation of work at congresses

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    Many situations in Robotics require an effective analysis of the motions of a closed-chain mechanism. Despite appearing very often in practice (e.g. in parallel manipulators, reconfigurable robots, or molecular compounds), there is a lack of general tools to effectively analyze the complex configuration spaces of such systems. This paper describes the CUIK suite, an open-source toolbox for motion analysis of general closed-chain mechanisms. The package can determine the motion range of the whole mechanism or of some of its parts, detect singular configurations leading to control or dexterity issues, or find collision- and singularity-free paths between given configurations. The toolbox is the result of several years of research and development within the Kinematics and Robot Design group at IRI, Barcelona, and is available under GPLv3 license from http://www.iri.upc.edu/cuik.

    Many situations in Robotics require an effective analysis of the motions of a closed-chain mechanism. Despite appearing very often in practice (e.g. in parallel manipulators, reconfigurable robots, or molecular compounds), there is a lack of general tools to effectively analyze the complex configuration spaces of such systems. This paper describes the CUIK suite, an open-source toolbox for motion analysis of general closed-chain mechanisms. The package can determine the motion range of the whole mechanism or of some of its parts, detect singular configurations leading to control or dexterity issues, or find collision- and singularity-free paths between given configurations. The toolbox is the result of several years of research and development within the Kinematics and Robot Design group at IRI, Barcelona, and is available under GPLv3 license from http://www.iri.upc.edu/cuik.

    Postprint (author’s final draft)

  • Randomized path planning on manifolds based on higher-dimensional continuation

     Porta Pleite, Josep M.; Jaillet, Leonard Georges; Bohigas Nadal, Oriol
    International journal of robotics research
    Date of publication: 2012
    Journal article

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  • Asymptotically-optimal path planning on manifolds

     Jaillet, Leonard Georges; Porta Pleite, Josep M.
    Robotics: Science and Systems
    Presentation's date: 2012
    Presentation of work at congresses

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    Randomized tree construction algorithm to explore energy landscapes  Open access

     Jaillet, Leonard Georges; Corcho Sanchez, Francisco Jose; Perez Gonzalez, Juan Jesus; Cortés, Juan
    Journal of computational chemistry
    Date of publication: 2011-12
    Journal article

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    We report in the present work a new method for exploring conformational energy landscapes. The method, called T-RRT, combines ideas from statistical physics and robot path planning algorithms. A search tree is constructed on the conformational space starting from a given state. The tree expansion is driven by a double strategy: on the one hand, it is naturally biased towards yet unexplored regions of the space; on the other, a Monte Carlo-like transition test guides the expansion toward energetically favorable regions. The balance between these two strategies is automatically achieved thanks to a self-tuning mechanism. The method is able to efficiently find both, energy minima and transition paths between them. As a proof of concept, the method is applied to two academic benchmarks and to the alanine dipeptide.

    Postprint (author’s final draft)

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    Path planning with loop closure constraints using an atlas-based RRT  Open access

     Jaillet, Leonard Georges; Porta Pleite, Josep M.
    International Symposium on Robotics Research
    Presentation's date: 2011
    Presentation of work at congresses

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    In many relevant path planning problems, loop closure constraints reduce the configuration space to a manifold embedded in the higher-dimensional joint ambient space. Whereas many progresses have been done to solve path planning problems in the presence of obstacles, only few work consider loop closure constraints. In this paper we present the AtlasRRT algorithm, a planner specially tailored for such constrained systems that builds on recently developed tools for higher-dimensional continuation. These tools provide procedures to define charts that locally parametrize manifolds and to coordinate them forming an atlas. AtlasRRT simultaneously builds an atlas and a Rapidly-Exploring Random Tree (RRT), using the atlas to sample relevant configurations for the RRT, and the RRT to devise directions of expansion for the atlas. The new planner is advantageous since samples obtained from the atlas allow a more efficient extension of the RRT than state of the art approaches, where samples are generated in the joint ambient space.

    In many relevant path planning problems, loop closure constraints reduce the configuration space to a manifold embedded in the higher-dimensional joint ambient space. Whereas many progresses have been done to solve path planning problems in the presence of obstacles, only few work consider loop closure constraints. In this paper we present the AtlasRRT algorithm, a planner specially tailored for such constrained systems that builds on recently developed tools for higher-dimensional continuation. These tools provide procedures to define charts that locally parametrize manifolds and to coordinate them forming an atlas. AtlasRRT simultaneously builds an atlas and a Rapidly-Exploring Random Tree (RRT), using the atlas to sample relevant configurations for the RRT, and the RRT to devise directions of expansion for the atlas. The new planner is advantageous since samples obtained from the atlas allow a more efficient extension of the RRT than state of the art approaches, where samples are generated in the joint ambient space.

    Postprint (author’s final draft)

  • EG-RRT: Environment-guided random trees for kinodynamic motion planning with uncertainty and obstacles

     Jaillet, Leonard Georges; Hoffman, Judy; van den Berg, Jur; Abbeel, Pieter; Porta Pleite, Josep M.; Goldberg, Ken
    IEEE/RSJ International Conference on Intelligent Robots and Systems
    Presentation's date: 2011
    Presentation of work at congresses

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    Sampling-based path planning on configuration-space costmaps  Open access

     Jaillet, Leonard Georges; Cortés, Juan; Simeon, Thierry
    IEEE transactions on robotics
    Date of publication: 2010
    Journal article

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    This paper addresses path planning to consider a cost function defined over the configuration space. The proposed planner computes low-cost paths that follow valleys and saddle points of the configuration-space costmap. It combines the exploratory strength of the Rapidly exploring Random Tree (RRT) algorithm with transition tests used in stochastic optimization methods to accept or to reject new potential states. The planner is analyzed and shown to compute low-cost solutions with respect to a path-quality criterion based on the notion of mechanical work. A large set of experimental results is provided to demonstrate the effectiveness of the method. Current limitations and possible extensions are also discussed.

  • Path planning on manifolds using randomized higher-dimensional continuation

     Porta Pleite, Josep M.; Jaillet, Leonard Georges
    International Workshop on the Algorithmic Foundations of Robotics
    Presentation's date: 2010
    Presentation of work at congresses

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