Open AccessProceedings Article
OBPRM: an obstacle-based PRM for 3D workspaces
Nancy M. Amato,O. Burchan Bayazit,Lucia K. Dale,Christopher Jones,Daniel Vallejo +4 more
- pp 155-168
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.read more
Citations
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Proceedings ArticleDOI
A Fast and Approximate Medial Axis Sampling Technique
TL;DR: In this article, a sampling-based approach is proposed to reduce the computational burden of planning on or near the medial axis by reasoning about the clearance of nearby configurations, which significantly reduces the necessary collision detection invocations.
Dissertation
Sistema de guiado asistido para la ejecución de tareas virtuales con dispositivos hapticos
Chacón Encalada,Luis Alejandro +1 more
TL;DR: In this paper, an arquitectura for the desarrollo de aplicaciones de guiado haptico for teleoperación or ejecucion de tareas virtuales, basada en una sola libreria (H3D), is presented.
Dissertation
Incremental high quality probabilistic roadmap construction for robot path planning
TL;DR: The text is divided into four sections: Declaration,Acknowledgment, Table of contents, List of figures, and List of Notations.
Proceedings ArticleDOI
Estimating the minimum number of robots to finish given multi-objects task
TL;DR: A method is proposed that could solve the problem of estimating the minimum number of robots to finish the given multi-objects task and builds the roadmap to compute the cost of motion between objects and then builds the network with the motion and work cost.
Posted Content
On Probabilistic Completeness of Probabilistic Cell Decomposition
TL;DR: This work presents a detailed proof of probabilistic completeness here for the first time, and shows that PCD is probabilistically complete.
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
Yong K. Hwang,Narendra Ahuja +1 more
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
TL;DR: This paper concerns the problem of moving a polyhedron through Euclidean space while avoiding polyhedral obstacles.