用高斯过程与霍夫变换实现机器人安全避障的场域映射
A Hough transform approach to safety-aware scalar field mapping using Gaussian Processes
- 基于高斯过程建模场域，实时估计高危区域
- 结合霍夫变换动态识别危险区，保障测量安全
- 适合需避障的自主机器人环境感知任务
本文提出一种框架，用于传感器搭载的自主机器人在不安全环境中映射未知标量场。不安全区域定义为场值超过预设安全阈值的高密度区域。为实现安全高效的场域映射，机器人需在测量过程中避开高值区域。本文将标量场建模为高斯过程（GP）样本，支持贝叶斯推断，并提供预测均值与不确定性的闭式表达。同时，利用霍夫变换（HT）实时估计高值区域的空间结构，基于演化的GP后验分布。采用安全采样策略引导机器人至安全测量点，基于概率性安全保证。估计出的高值区域还用于设计安全运动规划。通过两次数值仿真和一次室内实验验证了方法有效性，实验中使用轮式移动机器人对光照强度场进行映射。
原文摘要 · Abstract (English)
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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