CS 440/ECE 448
Margaret Fleck

## Searching configuration space

How do we compute a path in a configuration space?

For simple 2D examples, e.g. MP 2, we can digitize the configuration space positions and use a digitized maze search algorithm. However, this approach scales very badly.

• Digitized size increases exponentially in the number of dimensions
• Free space tends to be mostly large open areas, where many paths are possible
• Precision needs are variable: low in free space, high near obstacles

Normally we convert configuration space into a graph representation containing

• waypoints
• edges joining them

These graph representations are compact and can be searched efficiently.

## Visibility graphs

One graph-based approach uses vertices of objects as waypoints. Edges are straight lines connecting vertices. This "visibility graph" can be constructed incrementally, directly from a polygonal representation of the obstacles:

(from Howie Choset's book)

This type of representation was popular in early configuration space algorithms. It produces paths of minimal length. However, these paths aren't very safe because they skim the edges of obstacles. It's risky to get too close to obstacles. We could easily hit the obstacle if there are any errors in our model of our shape, our position, and/or the obstacle position. For most practical applications, it's better to have a somewhat longer path that is reasonably short and doesn't come too close to obstacles.

## Skeletonization

Another class of methods converts freespace into a skeleton that goes through the middle of regions, called a "roadmap". Paths via the skeleton stay far from obstacles, so they tend to be safe but perhaps overly long.

The "Generalized Voronoi Diagram" places the skeleton along curves that are equidistant from two or more obstacles. The waypoints are the places where these curves intersect.

(from Howie Choset)

The cell decomposition below divides free space somewhat more arbitrarily into trapezoids aligned with the coordinate axes. Waypoints are then located in the middle of each trapezoid and the middles of its edges.

(from Philippe Cheng, MIT, 2001)

## Details of path construction

A roadmap for a configuration space is usually precomputed and then used to plan many paths, e.g. as the robot moves around its environment. The start and goal of the path will typically not be on the roadmap. So the path is constructed in three parts:

• from start onto nearest point on skeleton
• along skeleton
• to goal from nearest point on skeleton

Paths assembled from roadmap edges may be longer than needed, and the edges aren't joined together smoothly. Sharp changes in orientation may be impossible for the robot to execute and/or cause excessive wear on the joints. So it is standard practice to optimize them (e.g. by smoothing the curve) in a post-processing step.

## Probabilistic Roadmap (PRM) Planners

Recent algorithms create roadmaps more efficiently by sampling the configuration space without every computing the details of all the obstacles. Specifically we

• Generate a large number of sample points in configuration space.
• Keep (as our waypoints) only the points that are in free space.
• Try to connect each waypoint to nearby waypoints.

There are fast algorithms for deciding whether a specific point is inside any obstacle. If waypoints are close together, there are also simple methods for connecting them, e.g. check whether the straight line between them is in free space.

Here is a probabilistic roadmap for a simple planning problem (left) and a plan constructed using it (right).

( (from Howie Choset book)