OpenSpace Lab Solution to the IROS 2026 Indoor Exploration Competition

2026-10-01

AI Summary

This study addresses the conflict between map coverage and time constraints in indoor robotic exploration, as well as redundant searching in multi-agent systems, by proposing an autonomous exploration framework for single and multiple robots. The framework integrates pretrained map completion with global path planning to prioritize unexplored regions, and designs an exploration strategy based on remaining time and return-to-base constraints. For multi-robot coordination, it introduces shared mapping and intent communication mechanisms, employing a utility function that jointly optimizes observation gain, movement cost, and budget constraints. This approach achieved first place in the single-robot public track and third place in the private track at IROS 2026, attaining map coverage rates of 61.04% and 39.5%, respectively.

Abstract

This report presents the \textbf{OpenSpace Lab}'s solution to the Competition on Intelligent Information Gathering for Single and Multi-Robot Systems Workshops, organized as part of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2026. Our team reached 1st place in the Single-Robot Public Track and 3rd place in both the Single- and Multi-Robot Private Tracks. The single-robot framework utilizes pre-trained map completion predictions for global planning to prioritize unexplored areas. To reconcile map coverage with limited operation time, we introduce a remaining-time-based exploration strategy that integrates homing constraints into the decision-making process. For multi-robot exploration, we utilize a utility-driven target selection strategy that balances observation gains, movement costs, and budget constraints, leveraging shared map and intent data to eliminate redundant search and maximize coordination efficiency. Our solution reached a 61.04\% coverage rate in the Single-Robot Public Track, while reaching 39.53\% and 39.91\% coverage in the Single- and Multi-Robot Private Tracks, respectively. An extended full-length paper based on this report is currently being prepared for submission, and the source code will be released upon acceptance of the full manuscript at https://github.com/OpenSpace-Lab/Indoor-Exploration-IROS2026.

Problem

• Indoor Exploration
• Single-Robot System
• Multi-Robot System
• Map Coverage
• Intelligent Information Gathering

Innovation

• Map Completion Prediction
• Remaining-Time-Based Exploration
• Utility-Driven Target Selection
• Multi-Robot Coordination
• Indoor Exploration