Improved Optimization of Motion Primitives for Motion Planning in State Lattices

TL;DR

This paper introduces an automated framework for generating and optimizing motion primitives in lattice-based motion planning, reducing manual effort and enabling quick re-optimization for systems with changing parameters.

Contribution

The proposed method automatically optimizes motion primitives and boundary conditions for system families, improving efficiency and adaptability in motion planning.

Findings

Reduces manual effort in motion primitive generation.

Enables fast re-optimization with changing system parameters.

Improves overall motion planning performance.

Abstract

In this paper, we propose a framework for generating motion primitives for lattice-based motion planners automatically. Given a family of systems, the user only needs to specify which principle types of motions, which are here denoted maneuvers, that are relevant for the considered system family. Based on the selected maneuver types and a selected system instance, the algorithm not only automatically optimizes the motions connecting pre-defined boundary conditions, but also simultaneously optimizes the end-point boundary conditions as well. This significantly reduces the time consuming part of manually specifying all boundary value problems that should be solved, and no exhaustive search to generate feasible motions is required. In addition to handling static a priori known system parameters, the framework also allows for fast automatic re-optimization of motion primitives if the system parameters change while the system is in use, e.g, if the load significantly changes or a trailer with a new geometry is picked up by an autonomous truck. We also show in several numerical examples that the framework can enhance the performance of the motion planner in terms of total cost for the produced solution.