Down Sampling using Simulated Annealing

Abstract

Robotic systems have been gathering more data as the field advances, allowing them to complete much more difficult tasks. This data has the potential to cause issues in the system once it reaches too large a size, and therefore must be preprocessed to manageable levels. This paper proposes a method to remove redundant data points from a data set based on a model formed from radial basis function (RBF) approximation. An optimization problem based off the Euclidean distance between the complete and reduced model with an additional least absolute shrinkage and selection operator (LASSO) component is formulated. A solution to the problem is then solved using simulated annealing. Finally, two simulations are set up to assess the performance of the algorithm.