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Open AccessProceedings Article

OBPRM: an obstacle-based PRM for 3D workspaces

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.

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Citations
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Proceedings ArticleDOI

Efficient Motion Planning of Highly Articulated Chains using Physics-based Sampling

TL;DR: A novel motion planning algorithm is presented that efficiently generates physics-based samples in a kinematically and dynamically constrained space of a highly articulated chain based on dynamic simulation and adaptively reduces the complexity of the state space.
Proceedings ArticleDOI

Lazy Toggle PRM: A single-query approach to motion planning

TL;DR: The effectiveness of Lazy Toggle PRM in a wide range of scenarios, including those with narrow passages and high descriptive complexity, is demonstrated, concluding that it is more effective than existing methods in solving difficult queries.
Proceedings ArticleDOI

Using Local Experiences for Global Motion Planning

TL;DR: This work decomposes the workspace into local primitives, memorizing local experiences by these primitives in the form of local samplers, and store them in a database to synthesize an efficient global sampler by retrieving local experiences relevant to the given situation.
Proceedings ArticleDOI

A motion planning processor on reconfigurable hardware

TL;DR: The experiments show that an FPGA based motion planning processor is not only feasible but also can greatly increase the performance of current algorithms, as well as using hardware acceleration to speed up the feasibility checks.
Journal ArticleDOI

A survey of computational treatments of biomolecules by robotics-inspired methods modeling equilibrium structure and dynamics

TL;DR: This survey focuses on robotics-inspired methods designed to obtain efficient representations of structure spaces of molecules in isolation or in assemblies for the purpose of characterizing equilibrium structure and dynamics.
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

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

John H. Reif
TL;DR: This paper concerns the problem of moving a polyhedron through Euclidean space while avoiding polyhedral obstacles.