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The Rapidly-exploring Random Tree (RRT) algorithm, based on incremental sampling, efficiently computes motion plans. Although the RRT algorithm quickly produces candidate feasible solutions, it tends to converge to a solution that is far from optimal. Practical applications favor “anytime” algorithms that quickly identify an initial feasible(More)
The RRT* algorithm has recently been proposed as an optimal extension to the standard RRT algorithm [1]. However, like RRT, RRT* is difficult to apply in problems with complicated or underactuated dynamics because it requires the design of a two domain-specific extension heuristics: a distance metric and node extension method. We propose automatically(More)
This article reviews the present status of the spin-foam approach to the quantization of gravity. Special attention is payed to the pedagogical presentation of the recently-introduced new models for four-dimensional quantum gravity. The models are motivated by a suitable implementation of the path integral quantization of the Plebanski formulation of(More)
In this article we review the present status of the spin foam formulation of non-perturbative (background independent) quantum gravity. The article is divided in two parts. In the first part we present a general introduction to the main ideas emphasizing their motivation from various perspectives. Riemannian 3-dimensional gravity is used as a simple example(More)
A desirable property of path planning for robotic manipulation is the ability to identify solutions in a sufficiently short amount of time to be usable. This is particularly challenging for the manipulation problem due to the need to plan over high-dimensional configuration spaces and to perform computationally expensive collision checking procedures.(More)
We propose a new method for applying RRT* to kinodynamic motion planning problems by using finite-horizon linear quadratic regulation (LQR) to measure cost and to extend the tree. First, we introduce the method in the context of arbitrary affine dynamical systems with quadratic costs. For these systems, the algorithm is shown to converge to optimal(More)
Citation Karaman, Sertac et al. "Anytime Motion Planning using the RRT*. The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Abstract— The Rapidly-exploring Random Tree (RRT) algorithm , based on incremental sampling, efficiently computes motion plans. Although the RRT algorithm quickly(More)
In this paper, we describe how security and privacy can be increased in user-centric Identity Management (IdM) by the introduction of a so-called IdM card. This IdM card securely stores and processes identity data of the card owner, an end user. The card represents a trusted device that supports the user in managing its digital identities and also in(More)