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Path planning on manifolds using randomized higher-dimensional continuation
Porta Pleite, Josep Maria; Jaillet, Leonard Georges
Universitat Politècnica de Catalunya. Institut de Robòtica i Informàtica Industrial
Despite the significant advances in path planning methods, problems involving highly constrained spaces are still challenging. In particular, in many situations the configuration space is a non-parametrizable variety implicitly defined by constraints, which complicates the successful generalization of sampling-based path planners. In this paper, we present a new path planning algorithm specially tailored for highly constrained systems. It builds on recently developed tools for Higher-dimensional Continuation, which provide numerical procedures to describe an implicitly defined variety using a set of local charts. We propose to extend these methods to obtain an efficient path planner on varieties, handling highly constrained problems. The advantage of this planner comes from that it directly operates into the configuration space and not into the higher-dimensional ambient space, as most of the existing methods do.
Planning / Artificial intelligence
robots PARAULES AUTOR: path planning
higher-dimensional continuation
Planificació / Intel·ligència artificial
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial artificial
Classificació INSPEC::Cybernetics::Artificial intelligence::Planning (artificial intelligence)
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/submittedVersion
info:eu-repo/semantics/conferenceObject
Springer
         

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