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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Book ChapterDOI
Generating Motion of Cooperating Robots—The Dual Arm Case
TL;DR: In this paper, an intelligent search algorithm for the motion planning of 13 DOF dual arm industrial robots is presented, which is based on the gradually approach of all the alternatives configurations and evaluates them.
Dissertation
A Reactive Planning Framework for Dexterous Robotic Manipulation
Ballester Huesca,José Andrés +1 more
TL;DR: This thesis investigates a reactive motion planning and controller framework that enables robots to manipulate objects dexterously and develops a robotic platform that can quickly and reliably replan actions based on sensed information.
Interactive Motion Planning for Assembly Tasks
TL;DR: This paper proposes a modification of a classic motion planning method, the Rapidly-exploring Random Tree to build an Interactive-RRT, based on exchanging forces between the algorithm and the user, and on data gathering from the virtual scene.
Rigidity Analysis for Modeling Protein Motion
TL;DR: This research aims to model molecular movement using a motion framework originally developed for robotic applications called the Probabilistic Roadmap Method, and presents a methodology for incrementally constructing roadmaps until they satisfy a set of evaluation criteria.
Improved Connected-Component Expansion Strategies for Sampling-Based Motion Planning
TL;DR: A set of connected component (CC) expansion algorithms, each with different biases and expansion node selection policies that create a linked-chain of configurations designed to enable efficient connection of CCs, are presented.
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.