Learned Critical Probabilistic Roadmaps for Robotic Motion Planning

TL;DR

This paper introduces Critical Probabilistic Roadmaps, a method that identifies and emphasizes critical states in motion planning, significantly improving efficiency while maintaining probabilistic completeness.

Contribution

It proposes a novel approach to learn and leverage critical states in probabilistic roadmaps using graph-theoretic measures and local environment features.

Findings

Achieves up to 1000x faster planning than uniform sampling.

Maintains probabilistic guarantees of traditional sampling-based methods.

Demonstrates effectiveness across various complex environments.

Abstract

Sampling-based motion planning techniques have emerged as an efficient algorithmic paradigm for solving complex motion planning problems. These approaches use a set of probing samples to construct an implicit graph representation of the robot's state space, allowing arbitrarily accurate representations as the number of samples increases to infinity. In practice, however, solution trajectories only rely on a few critical states, often defined by structure in the state space (e.g., doorways). In this work we propose a general method to identify these critical states via graph-theoretic techniques (betweenness centrality) and learn to predict criticality from only local environment features. These states are then leveraged more heavily via global connections within a hierarchical graph, termed Critical Probabilistic Roadmaps. Critical PRMs are demonstrated to achieve up to three orders of magnitude improvement over uniform sampling, while preserving the guarantees and complexity of sampling-based motion planning. A video is available at https://youtu.be/AYoD-pGd9ms.