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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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A Two Level Fuzzy PRM

TL;DR: This paper presents an al-orithm thatends the piabahilastic roadnimp (PRM) framework to manipulataon plnnnin, by using a two level approrich, a PRM of PRMs, made possible by the introduction of a new kind of roadmap, called the fuzzy roadmap.
Proceedings Article

Solving motion planning problems

TL;DR: Rolling Stone solver presented the best results for Sokoban, which uses multiple domain-independent and -dependent enhancements, and solves six instances from the standard set of 90 instances, limited to 20 million expanded nodes.
Proceedings ArticleDOI

Adaptive and relaxed visibility-based PRM

TL;DR: A new sampler for robotic motion planning using the probabilistic roadmap approach that uses a relaxed criterion that can significantly reduce the computational cost in the roadmap construction phase while increasing the size of the roadmap only mildly.
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.