IEEE Robotics & Automation Magazine - September 2015 - 96
Benchmarking Motion
Planning Algorithms
An Extensible Infrastructure
for Analysis and Visualization
M
otion planning is a key problem in robotics that is
concerned with finding a path that satisfies a goal
specification subject to constraints. In its simplest
form, the solution to this problem consists of finding a
path connecting two states, and the only constraint is
to avoid collisions. Even for this version of the motion planning problem, there is no efficient solution for the general case [1]. The addition of differential constraints on robot motion or more general goal
specifications makes motion planning even harder. Given its complexity, most planning algorithms forego completeness and optimality for slightly weaker notions such as resolution
completeness, probabilistic completeness [2], and
asymptotic optimality.
By Mark Moll, loan A. Sucan,
˛
and Lydia E. Kavraki
maze-©istockphoto.com/iserg,
man with arrows-image licensed by graphic stock
Digital Object Identifier 10.1109/MRA.2015.2448276
Date of publication: 18 August 2015
96
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IEEE ROBOTICS & AUTOMATION MAGAZINE
*
September 2015
Comparing Planning Algorithms
Sampling-based planning algorithms
are the most common probabilistically complete algorithms and are
widely used on robot platforms
with many degrees of freedom.
Within this class of algorithms, many variants have
been proposed over the last
20 years, yet there is still no
characterization of which algorithms are well-suited for
which classes of problems.
We present a benchmarking
infrastructure for motion planning algorithms that can be a
useful component for such a characterization. The infrastructure is
aimed both at end users who want to select a motion planning algorithm that performs best on problems of interest, as well as
motion planning researchers who want to compare
the performance of a new algorithm relative to other
state-of-the-art algorithms.
The benchmarking infrastructure consists of three main components (see Figure 1). First, we have created an extensive benchmarking software framework that is included in the Open Motion
1070-9932/15©2015IEEE
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