Physics-Based Learning for Robotic Environmental Sensing
This addresses the challenge of efficient environmental sensing for robotics, offering a hybrid method that reduces measurement needs compared to data-driven approaches, though it is incremental in combining existing techniques.
The authors tackled the problem of learning environmental fields like turbulent flows using a mobile robot by proposing a physics-based Bayesian framework that selects likely models from precomputed numerical solutions, and they demonstrated that this approach better approximates real flows compared to prior solutions and purely data-driven methods.
We propose a physics-based method to learn environmental fields (EFs) using a mobile robot. Common purely data-driven methods require prohibitively many measurements to accurately learn such complex EFs. Alternatively, physics-based models provide global knowledge of EFs but require experimental validation, depend on uncertain parameters, and are intractable for mobile robots. To address these challenges, we propose a Bayesian framework to select the most likely physics-based models of EFs in real-time, from a pool of numerical solutions generated offline as a function of the uncertain parameters. Specifically, we focus on turbulent flow fields and utilize Gaussian processes (GPs) to construct statistical models for them, using the pool of numerical solutions to inform their prior mean. To incorporate flow measurements into these GPs, we control a custom-built mobile robot through a sequence of waypoints that maximize the information content of the measurements. We experimentally demonstrate that our proposed framework constructs a posterior distribution of the flow field that better approximates the real flow compared to the prior numerical solutions and purely data-driven methods.