Organizations: China University of Mining and Technology-Beijing · Macau University of Science and Technology · Institute of Automation, Chinese Academy of Sciences · Mohamed bin Zayed University of Artificial Intelligence · Shenzhen University · Technical University of Munich
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.
Figures & tables
Figure 1 : Framework of Single-Robot Exploration.
Figure 2 : Qualitative visualization of single-robot exploration performance in the first map among the seven public maps.
Figure 3 : Qualitative visualization of three-robot cooperative exploration in the second map among the seven public maps.
Table 1 : Results of the single-agent and multi-agent private tracks.
Rank
Team
Score
1
OpenSpaceLab
61.04%
2
HeRoLab
60.12%
3
TAMU-UADY-Robotics
55.89%
4
DZT328
32.45%
5
HORIZON
25.69%
Table 2 : Results of the single-agent public track.
Aerospace Controls Lab, Massachusetts Institute of Technology, Cambridge, MA, USA · Draper Scholar with the Charles Stark Draper Laboratory, Cambridge, MA · Charles Stark Draper Laboratory, Cambridge, MA, USA
MARMoT Lab, Department of Mechanical Engineering, National University of Singapore · NEAR Lab, AI.Robotics Strategic Technology Centre, Singapore Technologies Engineering