This work presents and evaluates a novel strategy for robotic exploration that leverages human models of uncertainty perception. To do this, we introduce a measure of uncertainty that we term "Behavioral entropy", which builds on Prelec's probability weighting from Behavioral Economics. We show that the new operator is an admissible generalized entropy, analyze its theoretical properties and compare it with other common formulations such as Shannon's and Renyi's. In particular, we discuss how the new formulation is more expressive in the sense of measures of sensitivity and perceptiveness to uncertainty introduced here. Then we use Behavioral entropy to define a new type of utility function that can guide a frontier-based environment exploration process. The approach's benefits are illustrated and compared in a Proof-of-Concept and ROS-Unity simulation environment with a Clearpath Warthog robot. We show that the robot equipped with Behavioral entropy explores faster than Shannon and Renyi entropies.

翻译：本研究提出并评估了一种新颖的机器人探索策略，该策略利用了人类对不确定性感知的认知模型。为此，我们引入了一种称为"行为熵"的不确定性度量方法，该方法建立在行为经济学中普雷莱克概率加权理论的基础上。我们证明了该新算子是一种可接受的广义熵，分析了其理论特性，并将其与香农熵、瑞利熵等其他常见形式进行了比较。特别地，我们讨论了新公式在本文引入的敏感性与不确定性感知度方面具有更强的表达能力。随后，我们利用行为熵定义了一种新型效用函数，可用于指导基于边界的环境探索过程。该方法在概念验证与ROS-Unity仿真环境中通过Clearpath Warthog机器人进行了效果展示与对比分析。实验表明，搭载行为熵的机器人探索速度优于使用香农熵和瑞利熵的配置。