标题： BEASST：基于行为熵梯度的移动机器人自适应源寻找方法 显示英文标题

标题： BEASST: Behavioral Entropic Gradient based Adaptive Source Seeking for Mobile Robots

摘要： 本文介绍了BEASST（行为熵梯度自适应源寻找用于移动机器人），这是一种在复杂未知环境中进行机器人源寻找的新框架。我们的方法通过将归一化信号强度建模为源位置的替代概率，使移动机器人能够有效地平衡探索与利用。基于行为熵（BE）和Prelec的概率加权函数，我们定义了一个目标函数，该函数根据信号可靠性及任务紧迫性，使机器人行为从风险规避转变为风险寻求。在单峰信号假设下，该框架提供了理论收敛保证，并在有界扰动下提供实际稳定性。在DARPA SubT和多房间场景中的实验验证表明，BEASST始终优于最先进方法，在智能不确定性驱动导航中动态转换激进追逐和谨慎探索，实现了路径长度减少15%和源定位速度提高20%。 显示英文摘要

摘要： 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.