This paper presents BEASST (Behavioral Entropic Gradient-based Adaptive Source Seeking for Mobile Robots), a novel framework for robotic source seeking in complex, unknown environments. Our approach enables mobile robots to efficiently balance exploration and exploitation by modeling normalized signal strength as a surrogate probability of source location. Building on Behavioral Entropy(BE) with Prelec's probability weighting function, we define an objective function that adapts robot behavior from risk-averse to risk-seeking based on signal reliability and mission urgency. The framework provides theoretical convergence guarantees under unimodal signal assumptions and practical stability under bounded disturbances. Experimental validation across DARPA SubT and multi-room scenarios demonstrates that BEASST consistently outperforms state-of-the-art methods, achieving 15% reduction in path length and 20% faster source localization through intelligent uncertainty-driven navigation that dynamically transitions between aggressive pursuit and cautious exploration.

翻译：本文提出BEASST（基于行为熵梯度的移动机器人自适应源搜索框架），这是一种用于复杂未知环境中机器人源搜索的新颖框架。我们的方法通过将归一化信号强度建模为源位置的概率替代，使移动机器人能够有效平衡探索与利用。基于行为熵（BE）与Prelec概率加权函数，我们定义了一个目标函数，该函数可根据信号可靠性与任务紧迫性，自适应地将机器人行为从风险规避调整至风险寻求。该框架在单峰信号假设下提供理论收敛保证，并在有界扰动下具备实际稳定性。在DARPA SubT及多房间场景中的实验验证表明，BEASST始终优于现有先进方法，通过智能不确定性驱动导航——在激进追踪与谨慎探索间动态切换——实现了路径长度减少15%和源定位速度提升20%。