Possible datasets for testing path finding algorithms

I'm doing some work on pathfinding.

So far I have test开发者_如何学Goed my code on scenes composed of 2D cells. I've also created a simple 3d scene to test my work on as well.

I'd like to test my work on some 3d scenes .. but it is time consuming to create them.

Does anyone know of any scene datasets that I could use to test my pathfinding algorithms on?

To get a better answer, you really need to specify the dimensionality of the configuration spaces that you want to consider. You aren't going to be tackling protein folding and docking problems (200+ degrees of freedom) with discrete graph searches. Even a relatively small planning problems (in terms of academic problems), of about 6 degrees of freedom can quickly become intractable.

Most of the best examples for planning tend to be published in research papers first, and then make their way into more general use. Some of the best work tends to be published in IEEE journals, or at the Intelligent Robots and Systems (IROS) and International Conference on Robotics and Automation (ICRA) conferences. It may also be worth using the bibliography of a well known reference in the field, such as "Motion Planning" by LaValle as a starting point for further research (available in bibtex here)

Mark Overmars work in the computational geometry and planning communities have made some of the problems considered in his publications very recognizable. It is worth checking if any his current grad students and collaborators have any data sets available at the moment.

If you're happy to still be doing some work in 2d, and to manually convert an image to geometric data, Kris Beevers website has a number of worked examples for a range of planners in 2d work spaces.

The Motion Strategy Library contains a number of classical motion planning problems for use in 2d and 3d workspaces, with varying dimensionality of configuration space depending on the problem. It includes:

• L sections into a birdcage
• trailers
• multiple trailers
• mazes
• kinematic chains
• non-holonomic cars

A more recent implementation of an academic motion planning library is The Open Motion Planning Library developed by the Kavraki lab. Because of licensing, I haven't checked personally, but I assume that they ship some examples and tests with their project.

A number of significantly more complex kinodynamic motion planning examples are now publicly available as part of the OpenRAVE project. Their gallery is eye opening.

when I need big 3D datasets, I usually use attractors or other dynamical series. You simply have to iterate as many time as you want and it will generate a nice set of 3D data.

Try this 'Peter de Jong Attractor':

Xn+1 = sin(a Yn) - cos(b Xn)    
Yn+1 = sin(c Xn) - cos(d Yn)

Where (for example): a = 1.4, b = -2.3, c = 2.4, d = -2.1