发表机构
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（中国矿业大学（北京）; 澳门科技大学; 中国科学院自动化研究所; 穆罕默德·本·扎耶德人工智能大学; 深圳大学; 慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对IROS 2026室内探索竞赛，提出基于预训练地图补全与剩余时间策略的单机器人探索，及效用驱动的多机器人协调方法，获公开赛道第一、私有赛道第三。
AI 中文摘要
本报告介绍了 OpenSpace Lab 对 IEEE/RSJ 国际智能机器人与系统会议（IROS）2026 期间组织的单机器人与多机器人系统智能信息采集研讨会竞赛的解决方案。我们的团队在单机器人公开赛道中获得了第一名，并在单机器人和多机器人私有赛道中均获得了第三名。单机器人框架利用预训练的地图补全预测进行全局规划，以优先探索未探测区域。为了协调地图覆盖与有限的操作时间，我们引入了一种基于剩余时间的探索策略，该策略将返航约束整合到决策过程中。对于多机器人探索，我们采用了一种效用驱动的目标选择策略，该策略平衡了观测收益、移动成本和预算约束，并利用共享的地图和意图数据来消除冗余搜索并最大化协调效率。我们的解决方案在单机器人公开赛道中达到了 61.04% 的覆盖率，在单机器人和多机器人私有赛道中分别达到了 39.53% 和 39.91% 的覆盖率。基于本报告的扩展全文论文目前正在准备投稿，源代码将在全文被接受后于此 https URL 发布。
英文摘要
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.
CommentsIROS2026" target="_blank" rel="noopener">https://github.com/OpenSpace-Lab/Indoor-Exploration-IROS2026
