用熵梯度自适应调整机器人的搜源策略，更智能地平衡探索与追踪。
BEASST: Behavioral Entropic Gradient based Adaptive Source Seeking for Mobile Robots
- 基于信号强度构建概率模型，动态调节行为风险偏好。
- 路径长度减少15%，定位速度提升20%，在复杂环境中表现优异。
- 适合需要自主寻源的移动机器人任务，如搜救或探测。
本文提出BEASST（基于行为熵梯度的自适应源寻找框架），用于复杂未知环境中的机器人寻源。该方法将归一化信号强度建模为源位置的概率代理，结合行为熵（BE）与Prelec概率加权函数，定义了随信号可靠性与任务紧迫性自适应变化的目标函数，使机器人行为在规避风险与主动追击间动态切换。在单峰信号假设下提供理论收敛性保证，在有界扰动下具备实际稳定性。在DARPA SubT和多房间场景的实验验证表明，相较于现有最优方法，BEASST平均路径缩短15%，源定位速度加快20%，通过智能的不确定性驱动导航实现高效探索与精准追踪。
原文摘要 · Abstract (English)
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.
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