1 research outputs found
Clustering in Discrete Path Planning for Approximating Minimum Length Paths
In this paper we consider discrete robot path planning problems on metric
graphs. We propose a clustering method, Gamma-Clustering for the planning graph
that significantly reduces the number of feasible solutions, yet retains a
solution within a constant factor of the optimal. By increasing the input
parameter Gamma, the constant factor can be decreased, but with less reduction
in the search space. We provide a simple polynomial- time algorithm for finding
optimal Gamma-Clusters, and show that for a given Gamma, this optimal is
unique. We demonstrate the effectiveness of the clustering method on traveling
salesman instances, showing that for many instances we obtain significant
reductions in computation time with little to no reduction in solution quality.Comment: 11 pages, 6 figures, 1 table, ACC 201