Vertex-based Networks to Accelerate Path Planning Algorithms
This work addresses path planning efficiency for autonomy applications, representing an incremental improvement through a novel method for a known bottleneck.
The paper tackles the problem of inefficient sampling in RRT* path planning algorithms by using vertex-based networks to focus on critical vertices along optimal paths, achieving over 400% speed improvement compared to baseline models in experiments on randomly generated floor maps.
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.