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How much do you know about Rapidly Exploring Random Trees (RRTs)?

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How much do you know about Rapidly Exploring Random Trees (RRTs)?

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Questions and Answers

What is the purpose of RRTs in autonomous robotic motion planning?

RRTs are used to search nonconvex, high-dimensional spaces and generate open-loop trajectories for nonlinear systems with state constraints, as well as compute approximate control policies for high-dimensional nonlinear systems.

Who developed RRTs?

RRTs were developed by Steven M. LaValle and James J. Kuffner Jr.

How do RRTs grow a tree?

RRTs grow a tree rooted at the starting configuration using random samples from the search space, attempting a connection between each sample and the nearest state in the tree.

What is the probability of expanding an existing state in RRT growth?

The probability of expanding an existing state is proportional to the size of its Voronoi region. Signup and view all the answers

Can RRT growth be biased?

Yes, RRT growth can be biased by increasing the probability of sampling states from a specific area, such as the goal. Signup and view all the answers

What is RRT*?

RRT* is a variant of RRTs developed to converge to an optimum. Signup and view all the answers

In what fields have RRTs been widely used?

RRTs have been widely used in motion planning and robotics. Signup and view all the answers

Study Notes

Rapidly exploring random trees (RRTs) are an algorithm for searching nonconvex, high-dimensional spaces.
RRTs randomly build a space-filling tree and grow towards large unsearched areas of the problem.
They were developed by Steven M. LaValle and James J. Kuffner Jr. and are used in autonomous robotic motion planning.
RRTs can generate open-loop trajectories for nonlinear systems with state constraints and compute approximate control policies for high-dimensional nonlinear systems.
RRTs grow a tree rooted at the starting configuration using random samples from the search space.
A connection is attempted between each sample and the nearest state in the tree, resulting in the addition of a new state if the connection is feasible.
The probability of expanding an existing state is proportional to the size of its Voronoi region.
RRT growth can be biased by increasing the probability of sampling states from a specific area, such as the goal.
Variants of RRTs have been developed to converge to an optimum, such as RRT*.
RRTs have been widely used in motion planning and robotics.

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Description

Test your knowledge on Rapidly Exploring Random Trees (RRTs), an algorithm used in autonomous robotic motion planning. Learn about how RRTs work, their applications, and variants developed to converge to an optimum. Keywords: RRTs, nonconvex spaces, high-dimensional spaces, motion planning, Voronoi region, RRT*.