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
Sparse Roadmap Spanners
TL;DR: This work proposes an approach, called spars, that provides the following asymptotic properties: completeness, near- optimality and the probability of adding nodes to the spanner converges to zero as iterations increase, which suggests that finite-size data structures may exist that have near-optimality properties.
Proceedings ArticleDOI
Roadmap composition for multi-arm systems path planning
TL;DR: Results presented for a three-arm system and a model of the complex DLR's Justin robot show a significant performance gain of such a two-stage roadmap construction method with respect to single-stage methods applied to the whole system.
Proceedings ArticleDOI
Probabilistic motion planning for parallel mechanisms
Juan Cortés,Thierry Siméon +1 more
TL;DR: This paper proposes a general approach based on probabilistic motion planning techniques that combines random sampling techniques with simple but general geometric algorithms that guide the sampling toward feasible configurations satisfying the closure constraints of the parallel mechanism.
Proceedings ArticleDOI
Ligand binding with OBPRM and user input
TL;DR: It is found that user input helps the planner, and haptic device helps the user to understand the protein structure by enabling them to feel the difficult-to-visualize forces.
Book ChapterDOI
Simulating protein motions with rigidity analysis
TL;DR: A novel method based on rigidity theory to sample conformation space more effectively is proposed and extensions of the framework to automate the process and to map transitions between specified conformations are described.
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.