Vertex-based Networks to Accelerate Path Planning Algorithms

TL;DR

This paper introduces vertex-based neural networks to improve RRT* path planning efficiency by focusing on critical vertices, employing focal loss, and optimizing masking strategies, resulting in over 400% speed improvements in experiments.

Contribution

It presents a novel vertex-based network approach for RRT* that enhances sampling efficiency and incorporates focal loss to handle data imbalance, with practical masking configurations.

Findings

Achieved over 400% speed improvement over baseline

Focused on critical vertices for efficient path abstraction

Utilized focal loss to address data imbalance

Abstract

Path planning plays a crucial role in various autonomy applications, and RRT* is one of the leading solutions in this field. In this paper, we propose the utilization of vertex-based networks to enhance the sampling process of RRT*, leading to more efficient path planning. Our approach focuses on critical vertices along the optimal paths, which provide essential yet sparser abstractions of the paths. We employ focal loss to address the associated data imbalance issue, and explore different masking configurations to determine practical tradeoffs in system performance. Through experiments conducted on randomly generated floor maps, our solutions demonstrate significant speed improvements, achieving over a 400% enhancement compared to the baseline model.