B-ActiveSEAL: Scalable Uncertainty-Aware Active Exploration with Tightly Coupled Localization-Mapping

2025-12-13

AI Summary

In large-scale environments, long-term autonomous exploration suffers from scalability limitations due to coupled localization and mapping uncertainties that hinder robust decision-making. To address this, we propose a unified information-theoretic modeling framework. We introduce Behavioral Entropy (BE) — the first adaptive information measure explicitly designed for coupled uncertainty — and establish its theoretical foundation in generalized entropy and uncertainty propagation. Furthermore, we design a tunable exploration–exploitation trade-off mechanism enabling environment-adaptive, diverse exploration strategies. Our approach integrates information-theoretic active exploration, Bayesian filtering, and uncertainty propagation modeling. Evaluated on a ROS-Unity co-simulation platform and multiple complex open-source maps, it significantly outperforms baseline methods in exploration efficiency and mapping accuracy. The framework achieves more balanced, robust, and scalable long-term autonomous exploration performance.

Technology Category

• Reasoning under Uncertainty: Sequential Decision Making
• Machine Learning: Calibration & Uncertainty Quantification
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Addresses scalable active exploration under coupled localization-mapping uncertainty
• Balances map and localization uncertainty for exploration-exploitation trade-off
• Proposes Behavioral entropy for adaptive decision-making in complex environments

Innovation

• Scalable framework balances map and localization uncertainty
• Uses Behavioral entropy for adaptive decision-making under uncertainty
• Propagates coupled uncertainties into general entropy formulations