Adaptive Sensor Steering Strategy Using Deep Reinforcement Learning for Dynamic Data Acquisition in Digital Twins

• URL: http://arxiv.org/abs/2504.10248v1
• Date: Mon, 14 Apr 2025 14:11:00 GMT
• Title: Adaptive Sensor Steering Strategy Using Deep Reinforcement Learning for Dynamic Data Acquisition in Digital Twins
• Authors: Collins O. Ogbodo, Timothy J. Rogers, Mattia Dal Borgo, David J. Wagg,
• Abstract summary: This paper introduces a sensor steering methodology based on deep reinforcement learning to enhance the predictive accuracy and decision support capabilities of digital twins.<n>The proposed approach addresses this limitation by offering an adaptive framework for sensor placement within the digital twin paradigm.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces a sensor steering methodology based on deep reinforcement learning to enhance the predictive accuracy and decision support capabilities of digital twins by optimising the data acquisition process. Traditional sensor placement techniques are often constrained by one-off optimisation strategies, which limit their applicability for online applications requiring continuous informative data assimilation. The proposed approach addresses this limitation by offering an adaptive framework for sensor placement within the digital twin paradigm. The sensor placement problem is formulated as a Markov decision process, enabling the training and deployment of an agent capable of dynamically repositioning sensors in response to the evolving conditions of the physical structure as represented by the digital twin. This ensures that the digital twin maintains a highly representative and reliable connection to its physical counterpart. The proposed framework is validated through a series of comprehensive case studies involving a cantilever plate structure subjected to diverse conditions, including healthy and damaged conditions. The results demonstrate the capability of the deep reinforcement learning agent to adaptively reposition sensors improving the quality of data acquisition and hence enhancing the overall accuracy of digital twins.

Related papers

• Data Sensor Fusion In Digital Twin Technology For Enhanced Capabilities In A Home Environment [0.0]
  This paper investigates the integration of data sensor fusion in digital twin technology to bolster home environment capabilities.<n>The research integrates Cyber-physical systems, IoT, AI, and robotics to fortify digital twin capabilities.
  arXiv  Detail & Related papers  (2025-02-13T01:14:30Z)
• Predictive Digital Twin for Condition Monitoring Using Thermal Imaging [0.0]
  This paper explores the development and practical application of a predictive digital twin specifically designed for condition monitoring. We employ advanced mathematical models and thermal imaging techniques to establish a robust digital twin framework. We introduce the use of a human-machine interface that includes virtual reality, enhancing user interaction and system understanding.
  arXiv  Detail & Related papers  (2024-11-08T11:23:57Z)
• Virtual Sensing-Enabled Digital Twin Framework for Real-Time Monitoring of Nuclear Systems Leveraging Deep Neural Operators [0.36651088217486427]
  This paper introduces the use of Deep Operator Networks (DeepONet) as a core component of a digital twin framework.<n>DeepONet serves as a dynamic and scalable virtual sensor by accurately mapping the interplay between operational input parameters and spatially distributed system behaviors.<n>Our results show that DeepONet achieves accurate predictions with low mean squared error and relative L2 error and can make predictions on unknown data 1400 times faster than traditional CFD simulations.
  arXiv  Detail & Related papers  (2024-10-17T16:56:04Z)
• Digital Twin Framework for Optimal and Autonomous Decision-Making in Cyber-Physical Systems: Enhancing Reliability and Adaptability in the Oil and Gas Industry [0.0]
  This work proposes a digital twin framework for optimal and autonomous decision-making applied to a gas-lift process in the oil and gas industry. The framework combines Bayesian inference, Monte Carlo simulations, transfer learning, online learning, and novel strategies to confer cognition to the DT.
  arXiv  Detail & Related papers  (2023-11-21T18:02:52Z)
• A Real-time Human Pose Estimation Approach for Optimal Sensor Placement in Sensor-based Human Activity Recognition [63.26015736148707]
  This paper introduces a novel methodology to resolve the issue of optimal sensor placement for Human Activity Recognition. The derived skeleton data provides a unique strategy for identifying the optimal sensor location. Our findings indicate that the vision-based method for sensor placement offers comparable results to the conventional deep learning approach.
  arXiv  Detail & Related papers  (2023-07-06T10:38:14Z)
• Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User Experiences [118.91584633024907]
  A novel joint sensing, communication, and artificial intelligence (AI) framework is proposed so as to optimize extended reality (XR) experiences over terahertz (THz) wireless systems.
  arXiv  Detail & Related papers  (2023-04-29T00:39:50Z)
• Optimizing a Digital Twin for Fault Diagnosis in Grid Connected Inverters -- A Bayesian Approach [5.335631208278852]
  We channelize our efforts towards an online optimization of the digital twins, which allows a flexible implementation with limited data. For classification performance assessment, we consider different fault cases in virtual synchronous generator (VSG) controlled grid-forming converters. Our research outcomes reveal the increased accuracy and fidelity levels achieved by our digital twin design.
  arXiv  Detail & Related papers  (2022-12-07T10:44:19Z)
• Visual-tactile sensing for Real-time liquid Volume Estimation in Grasping [58.50342759993186]
  We propose a visuo-tactile model for realtime estimation of the liquid inside a deformable container. We fuse two sensory modalities, i.e., the raw visual inputs from the RGB camera and the tactile cues from our specific tactile sensor. The robotic system is well controlled and adjusted based on the estimation model in real time.
  arXiv  Detail & Related papers  (2022-02-23T13:38:31Z)
• An automatic differentiation system for the age of differential privacy [65.35244647521989]
  Tritium is an automatic differentiation-based sensitivity analysis framework for differentially private (DP) machine learning (ML) We introduce Tritium, an automatic differentiation-based sensitivity analysis framework for differentially private (DP) machine learning (ML)
  arXiv  Detail & Related papers  (2021-09-22T08:07:42Z)
• Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor Setups [68.8204255655161]
  We present a method to calibrate the parameters of any pair of sensors involving LiDARs, monocular or stereo cameras. The proposed approach can handle devices with very different resolutions and poses, as usually found in vehicle setups.
  arXiv  Detail & Related papers  (2021-01-12T12:02:26Z)
• Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action Recognition [131.6328804788164]
  We propose a framework, named Semantics-aware Adaptive Knowledge Distillation Networks (SAKDN), to enhance action recognition in vision-sensor modality (videos) The SAKDN uses multiple wearable-sensors as teacher modalities and uses RGB videos as student modality.
  arXiv  Detail & Related papers  (2020-09-01T03:38:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.