A Comparison of Roadmap and Cell Decomposition Methods in Robot Motion Planning

Peer-reviewed article not indexed in WoS or Scopus

en

The task of planning trajectories plays an important role in transportation, robotics, information systems, etc. In robot motion planning, the robot should pass around obstacles from a given starting position to a given target position, touching none of them, i.e. the goal is to find a collision-free path from the starting to the target position. Research on path planning has yielded many fundamentally different approaches to the solution of this problem that can be classified as roadmap methods (visibility graph method, Voronoi diagram) and methods based on cell decomposition. Assuming movements only in a restricted number of directions (eight directional or horizontal/vertical) the task, with respect to its combinatorial nature, can be solved by decomposition methods using heuristic techniques. The cell decomposition methods may include a case-based reasoning procedure. Robot motion planning algorithms can be modified by considering a robot as a point and enlarging the obstacles in the workspace accordingly. In order to avoid the explicit construction of the obstacle region, sampling-based motion planning algorithms have been proposed such as probabilistic roadmap methods or rapidly exploring random trees. We present drawbacks of these approaches Then, using the Voronoi diagrams, we need only polynomial time for finding a solution and, choosing a Euclidean or rectilinear metric, it can be adapted to tasks with general or directional-constrained movements

motion planning, cell decomposition, sampling methods, roadmap method, Voronoi diagram

2007-02-15

1991-8763

2

2

101–108

8