In this work we describe the design and implementation of a general framework
for visualizing and editing motion planning environments, problem instances, and
their solutions.
The motion planning problem consists of finding a valid path between a start and
a goal configuration for a movable object. The workspace is, in traditional robotics
and animation applications, composed of one or more objects (called obstacles) that
cannot overlap with the robot.
As even the simplest motion planning problems have been shown to be in-
tractable, most practical approaches to motion planning use randomization and/or
compute approximate solutions. While the tool we present allows the manipulation
and evaluation of planner solutions and the animation of any path found by any plan-
ner, it is specialized for a class of randomized planners called probabilistic roadmap
methods (PRMs).
PRMs are roadmap-based methods that generate a graph or roadmap where the
nodes represent collision-free configurations and the edges represent feasible paths
between those configurations. PRMs typically consist of two phases: roadmap con-
struction, where a roadmap is built, and query, where the start and goal configura-
tions are connected to the roadmap and then a path is extracted using graph search
techniques.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/3264 |
| Date | 12 April 2006 |
| Creators | Vargas Estrada, Aimee |
| Contributors | Amato, Nancy M. |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | 1001248 bytes, electronic, application/pdf, born digital |
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