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Open AccessProceedings Article

OBPRM: an obstacle-based PRM for 3D workspaces

TLDR
This paper presents a new class of randomized path planning methods, known as Probabilistic Roadmap Methods (prms), which use randomization to construct a graph of representative paths in C-space whose vertices correspond to collision-free con gurations of the robot.
Abstract
Recently, a new class of randomized path planning methods, known as Probabilistic Roadmap Methods (prms) have shown great potential for solving complicated high-dimensional problems. prms use randomization (usually during preprocessing) to construct a graph of representative paths in C-space (a roadmap) whose vertices correspond to collision-free con gurations of the robot and in which two vertices are connected by an edge if a path between the two corresponding con gurations can be found by a local planning method.

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Citations
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Proceedings ArticleDOI

Motion planning for Human-Robot Interaction based on stereo vision and SIFT

TL;DR: This paper presents a motion planning method, based on visual feedback, for safe Human-Robot Interaction (HRI) in dynamic environments, and shows that position of people can be detected in real-time in environments with several people walking inside, and the accuracy can reach 96%.
Dissertation

Motion Planning and Control of Robot Manipulators

TL;DR: The design and implementation of an algorithm capable to face all challenges of motion planning in dynamic environments are presented and the result is an extensible and future-oriented planning system that can plan and execute movement between a starting and target position while preserving task constraints and reacting to environment changes in real time.
Dissertation

Planification de saisie pour la manipulation d'objets par un robot autonome

TL;DR: In this article, a planificateur base on inertielles de l'objet et a decomposition en elements quasi-convex tout en prenant en compte les contraintes imposees imposedes par le systeme mobile complet dans un environnement donne.
Dissertation

From high-level tasks to low-level motions: motion planning for high-dimensional nonlinear hybrid robotic systems

TL;DR: Synergic Combination of Layers of Planning (SyCLoP) synergically combines high-level discrete planning and low-level motion planning to solve motion-planning problems with respect to rich models of the robot and the physical world.
Dissertation

Planification de mouvement interactive: coopération humain-machine pour la recherche de trajectoire et l'animation

TL;DR: In this article, a methode de planification interactive is introduced, which fait cooperer un operateur and un algorithme of planification de mouvement dans a boucle d'interaction.
References
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Book

Robot Motion Planning

TL;DR: This chapter discusses the configuration space of a Rigid Object, the challenges of dealing with uncertainty, and potential field methods for solving these problems.
Journal ArticleDOI

Probabilistic roadmaps for path planning in high-dimensional configuration spaces

TL;DR: Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (/spl ap/150 MIPS), after learning for relatively short periods of time (a few dozen seconds).
Journal ArticleDOI

Robot motion planning: a distributed representation approach

TL;DR: A new approach to robot path planning that consists of building and searching a graph connecting the local minima of a potential function defined over the robot's configuration space is proposed and a planner based on this approach has been implemented.
Journal ArticleDOI

Gross motion planning—a survey

TL;DR: This paper surveys the work on gross-motion planning, including motion planners for point robots, rigid robots, and manipulators in stationary, time-varying, constrained, and movable-object environments.
Proceedings Article

Complexity of the Mover's Problem and Generalizations Extended Abstract

John H. Reif
TL;DR: This paper concerns the problem of moving a polyhedron through Euclidean space while avoiding polyhedral obstacles.