This paper presents a framework for mapping unknown scalar fields using a sensor-equipped autonomous robot operating in unsafe environments. The unsafe regions are defined as regions of high-intensity, where the field value exceeds a predefined safety threshold. For safe and efficient mapping of the scalar field, the sensor-equipped robot must avoid high-intensity regions during the measurement process. In this paper, the scalar field is modeled as a sample from a Gaussian process (GP), which enables Bayesian inference and provides closed-form expressions for both the predictive mean and the uncertainty. Concurrently, the spatial structure of the high-intensity regions is estimated in real-time using the Hough transform (HT), leveraging the evolving GP posterior. A safe sampling strategy is then employed to guide the robot towards safe measurement locations, using probabilistic safety guarantees on the evolving GP posterior. The estimated high-intensity regions also facilitate the design of safe motion plans for the robot. The effectiveness of the approach is verified through two numerical simulation studies and an indoor experiment for mapping a light-intensity field using a wheeled mobile robot.

翻译：本文提出了一种框架，利用配备传感器的自主机器人在非安全环境中对未知标量场进行映射。非安全区域定义为高强度区域，即场值超过预设安全阈值的区域。为安全高效地完成标量场映射，传感器机器人必须在测量过程中避开高强度区域。本文将标量场建模为高斯过程（GP）的样本，该模型支持贝叶斯推断，并提供预测均值与不确定性的闭式表达式。同时，基于实时更新的高斯过程后验，利用霍夫变换（HT）估计高强度区域的空间结构。随后采用安全采样策略，基于不断演化的高斯过程后验的概率性安全保证，引导机器人前往安全测量位置。所估计的高强度区域还有助于设计机器人的安全运动规划。通过两项数值仿真研究及一项利用轮式移动机器人映射光强度场的室内实验，验证了该方法的有效性。