4CNet: A Diffusion Approach to Map Prediction for Decentralized Multi-Robot Exploration

Understand

Mobile robots in unknown cluttered environments with irregularly shaped obstacles often face energy and communication challenges which directly affect their ability to explore these environments.

• In this paper, we introduce a novel deep learning architecture, Confidence-Aware Contrastive Conditional Consistency Model (4CNet), for robot map prediction during decentralized, resource-limited multi-robot exploration.
• 4CNet uniquely incorporates: 1) a conditional consistency model for map prediction in unstructured unknown regions, 2) a contrastive map-trajectory pretraining framework for a trajectory encoder that extracts spatial information from the trajectories of nearby robots during map prediction, and 3) a confidence network to measure the uncertainty of map prediction for effective exploration under resource constraints.
• We incorporate 4CNet within our proposed robot exploration with map prediction architecture, 4CNet-E.