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 new sample-based strategy for narrow passage detection
Zahra Sadeghi,Hadi Moradi +1 more
TL;DR: Three different milestone generation methods are proposed to better overcome the narrow passage problem in Probabilistic Roadmaps and show better performance in terms of runtime, uniform coverage of the configuration space, and success rate in narrow passage detection and final path generation.
A Simple Path Non-Existence Algorithm for low DOF robots
TL;DR: This work presents a simple algorithm to check for path non-existence for a robot among static obstacles based on adaptive cell decomposition of C-space, and describes simple and efficient algorithms to perform C-free and C-obstacle queries using separation and generalized penetration distance computations.
Proceedings ArticleDOI
Multi-robot caravanning
Jory Denny,Andrew Giese,Aditya Mahadevan,Arnaud Marfaing,Rachel Glockenmeier,Colton Revia,Samuel Rodriguez,Nancy M. Amato +7 more
TL;DR: The use of leader election is used to efficiently exploit the unique environmental knowledge available to each robot in order to plan paths for the group, which makes it general enough to work with robots that have heterogeneous representations of the environment.
Book ChapterDOI
Small tree probabilistic roadmap planner for hyper-redundant manipulators
Eric Lanteigne,Amor Jnifene +1 more
TL;DR: A single-query probabilistic roadmap planner for hyper-redundant manipulators, called Small Tree, that incrementally builds two solution paths from small alternating roadmaps rooted at the two input configurations, until a connection linking both paths is found.
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.