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
On the probabilistic completeness of the sampling-based feedback motion planners in belief space
TL;DR: It is shown that under mild conditions the sampling-based methods constructed based on the abstract framework of FIRM (Feedback-based Information Roadmap Method) are probabilistically complete under uncertainty.
Book ChapterDOI
A Region-Based Strategy for Collaborative Roadmap Construction
TL;DR: It is demonstrated that Region Steering provides roadmap customizability, reduced mapping time, and smaller roadmap sizes compared with fully automated PRMs, e.g., Gaussian PRM.
Journal ArticleDOI
Legged Motion Planning in Complex Three-Dimensional Environments
TL;DR: In this article, contact dynamic roadmaps (CDRM) are extended with contact information to support full-body motion planning in complex 3-dimensional environments, and the concept behind this is to perform the expensive foothold candidate generation and collision checking phases offline and store the data for use in the online planner.
Efficient Configuration Space Construction ant Dptimization for Motion Planning
TL;DR: This paper designs efficient GPU-based parallel k -nearest neighbor and parallel collision detection algorithms and uses these algorithms to accelerate motion planning and present new configuration space construction algorithms based on machine learning and geometric approximation techniques.
Proceedings ArticleDOI
Blind RRT: A probabilistically complete distributed RRT
TL;DR: A new algorithm is presented, Blind RRT, which ignores obstacles during initial growth to efficiently explore the entire space of parallel RRTs and overcomes the motion planning limitations that Radial RRT has in a series of difficult motion planning tasks.
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.