Selection of Input Primitives for the Generalized Label Correcting Method

TL;DR

This paper improves the generalized label correcting method for trajectory optimization by systematically selecting control primitives that minimize a generalized energy function, leading to faster computation especially in complex input spaces.

Contribution

It introduces a principled approach for selecting control primitives based on energy minimization, enhancing the efficiency of the label correcting method in challenging geometries.

Findings

Twofold reduction in running time with optimized primitives

Effective control primitive selection improves trajectory search efficiency

Demonstrated on n-dimensional sphere input space

Abstract

The generalized label correcting method is an efficient search-based approach to trajectory optimization. It relies on a finite set of control primitives that are concatenated into candidate control signals. This paper investigates the principled selection of this set of control primitives. Emphasis is placed on a particularly challenging input space geometry, the $n$-dimensional sphere. We propose using controls which minimize a generalized energy function and discuss the optimization technique used to obtain these control primitives. A numerical experiment is presented showing a factor of two improvement in running time when using the optimized control primitives over a random sampling strategy.