A Hough transform approach to safety-aware scalar field mapping using Gaussian Processes
2026-04-22 • Robotics
Robotics
AI summaryⓘ
The authors present a method for a robot to safely explore and map areas where some measurements could be dangerous (called high-intensity regions). They model the unknown environment using a mathematical tool called a Gaussian process that helps predict both what the robot expects to find and how uncertain those predictions are. To detect risky areas, the authors use the Hough transform to identify shapes of high-intensity zones in real time. Their strategy ensures the robot only takes measurements in safe spots while planning paths that avoid danger. They tested this approach in simulations and a real robot experiment measuring light intensity indoors.
Gaussian processscalar field mappingHough transformsafe explorationautonomous robotBayesian inferenceuncertainty quantificationmotion planninghigh-intensity regionssensor-equipped robotics
Authors
Muzaffar Qureshi, Trivikram Satharasi, Tochukwu E. Ogri, Kyle Volle, Rushikesh Kamalapurkar
Abstract
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.