基于高斯过程的安全意识标量场映射方法
A Hough transform approach to safety-aware scalar field mapping using Gaussian Processes
AI总结：
本文提出了一种利用高斯过程和霍夫变换的安全标量场映射框架，通过实时估计高强度区域并指导机器人安全采样，验证了方法的有效性。
摘要：
本文提出了一种框架，用于使用配备传感器的自主机器人在不安全环境中映射未知标量场。不安全区域定义为高强度区域，其中场值超过预定义的安全阈值。为了安全高效地映射标量场，配备传感器的机器人必须在测量过程中避免高强度区域。本文将标量场建模为高斯过程（GP）的样本，这使得贝叶斯推断成为可能，并提供了预测均值和不确定性的闭式表达式。同时，利用演进的GP后验，实时估计高强度区域的空间结构。然后采用安全采样策略，利用演进GP后验的概率安全保证，引导机器人前往安全的测量位置。估计的高强度区域也促进了机器人的安全运动计划设计。通过两个数值模拟研究和一个室内实验验证了该方法的有效性，用于使用轮式移动机器人映射光强度场。
英文摘要：
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.
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