提出可扩展的不确定性感知主动探索框架，实现定位与建图协同决策。

Active robot exploration requires decision-making processes that integrate localization and mapping under tightly coupled uncertainty. However, managing these interdependent uncertainties over long-term operations in large-scale environments rapidly becomes computationally intractable. To address this challenge, we propose B-ActiveSEAL, a scalable information-theoretic active exploration framework that explicitly accounts for coupled uncertainties-from perception through mapping-into the decision-making process. Our framework (i) adaptively balances map uncertainty (exploration) and localization uncertainty (exploitation), (ii) accommodates a broad class of generalized entropy measures, enabling flexible and uncertainty-aware active exploration, and (iii) establishes Behavioral entropy (BE) as an effective information measure for active exploration by enabling intuitive and adaptive decision-making under coupled uncertainties. We establish a theoretical foundation for propagating coupled uncertainties and integrating them into general entropy formulations, enabling uncertainty-aware active exploration under tightly coupled localization-mapping. The effectiveness of the proposed approach is validated through rigorous theoretical analysis and extensive experiments on open-source maps and ROS-Unity simulations across diverse and complex environments. The results demonstrate that B-ActiveSEAL achieves a well-balanced exploration-exploitation trade-off and produces diverse, adaptive exploration behaviors across environments, highlighting clear advantages over representative baselines.