Selection of Input Primitives for the Generalized Label Correcting Method
This work addresses a domain-specific challenge in trajectory optimization for robotics or control systems, focusing on input space geometry like the n-dimensional sphere, and is incremental as it builds on an existing method.
The paper tackles the problem of selecting control primitives for the generalized label correcting method in trajectory optimization, achieving a factor of two improvement in running time compared to random sampling in a numerical experiment.
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.