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Open AccessJournal ArticleDOI

Probabilistic roadmaps for path planning in high-dimensional configuration spaces

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TLDR
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).
Abstract
A new motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collision-free configurations and whose edges correspond to feasible paths between these configurations. These paths are computed using a simple and fast local planner. In the query phase, any given start and goal configurations of the robot are connected to two nodes of the roadmap; the roadmap is then searched for a path joining these two nodes. The method is general and easy to implement. It can be applied to virtually any type of holonomic robot. It requires selecting certain parameters (e.g., the duration of the learning phase) whose values depend on the scene, that is the robot and its workspace. But these values turn out to be relatively easy to choose, Increased efficiency can also be achieved by tailoring some components of the method (e.g., the local planner) to the considered robots. In this paper the method is applied to planar articulated robots with many degrees of freedom. 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).

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Citations
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Book ChapterDOI

An Effective Framework for Path Planning Amidst Movable Obstacles

TL;DR: A probabilistically complete framework for solving path planning problems among movable obstacles and heuristics are presented to provide efficient solutions for problems in environments encountered in practical situations.
Proceedings ArticleDOI

Manipulation planning with goal sets using constrained trajectory optimization

TL;DR: CHOMP is extended, a recent trajectory optimizer that has proven effective on high-dimensional problems, to handle trajectory-wide constraints, and the solution is related to the intuition of taking unconstrained steps and subsequently projecting them onto the constraints.
Proceedings ArticleDOI

Rapidly-exploring random tree based memory efficient motion planning

TL;DR: A modified version of the RRT* motion planning algorithm, which limits the memory required for storing the tree, which outperforms RRT and comes close to R RT* with respect to the optimality of returned path, while needing much less number of nodes stored in the tree.
Proceedings ArticleDOI

Workspace importance sampling for probabilistic roadmap planning

TL;DR: Workstation importance sampling (WIS) as discussed by the authors uses geometric information from a robot's workspace as "importance" values to guide sampling in the corresponding configuration space, which increases the sampling density in narrow passages and decreases the sample density in wide-open regions.
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

An algorithm for planning collision-free paths among polyhedral obstacles

TL;DR: A collision avoidance algorithm for planning a safe path for a polyhedral object moving among known polyhedral objects that transforms the obstacles so that they represent the locus of forbidden positions for an arbitrary reference point on the moving object.
Journal ArticleDOI

Spatial Planning: A Configuration Space Approach

TL;DR: In this article, the authors propose an approach based on characterizing the position and orientation of an object as a single point in a configuration space, in which each coordinate represents a degree of freedom in the position or orientation of the object.
Journal ArticleDOI

Exact robot navigation using artificial potential functions

TL;DR: A methodology for exact robot motion planning and control that unifies the purely kinematic path planning problem with the lower level feedback controller design is presented.
Book

Spatial planning: a configuration space approach

TL;DR: Algorithms for computing constraints on the position of an object due to the presence of ther objects, which arises in applications that require choosing how to arrange or how to move objects without collisions are presented.