Multi-robot Multi-source Localization in Complex Flows with Physics-Preserving Environment Models

• URL: http://arxiv.org/abs/2509.14228v1
• Date: Wed, 17 Sep 2025 17:58:25 GMT
• Title: Multi-robot Multi-source Localization in Complex Flows with Physics-Preserving Environment Models
• Authors: Benjamin Shaffer, Victoria Edwards, Brooks Kinch, Nathaniel Trask, M. Ani Hsieh,
• Abstract summary: Source localization in a complex flow poses a significant challenge for multi-robot teams tasked with localizing the source of chemical leaks or tracking the dispersion of an oil spill.<n>We present a distributed mobile sensing framework for source localization in which each robot carries a machine-learned, finite element model of its environment.<n>Our approach achieves faster error reduction compared to baseline sensing strategies and results in more accurate source localization compared to baseline machine learning approaches.
• Score: 7.60216127875876
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Source localization in a complex flow poses a significant challenge for multi-robot teams tasked with localizing the source of chemical leaks or tracking the dispersion of an oil spill. The flow dynamics can be time-varying and chaotic, resulting in sporadic and intermittent sensor readings, and complex environmental geometries further complicate a team's ability to model and predict the dispersion. To accurately account for the physical processes that drive the dispersion dynamics, robots must have access to computationally intensive numerical models, which can be difficult when onboard computation is limited. We present a distributed mobile sensing framework for source localization in which each robot carries a machine-learned, finite element model of its environment to guide information-based sampling. The models are used to evaluate an approximate mutual information criterion to drive an infotaxis control strategy, which selects sensing regions that are expected to maximize informativeness for the source localization objective. Our approach achieves faster error reduction compared to baseline sensing strategies and results in more accurate source localization compared to baseline machine learning approaches.

Related papers

• Wireless Traffic Prediction with Large Language Model [54.07581399989292]
  TIDES is a novel framework that captures spatial-temporal correlations for wireless traffic prediction.<n> TIDES achieves efficient adaptation to domain-specific patterns without incurring excessive training overhead.<n>Our results indicate that integrating spatial awareness into LLM-based predictors is the key to unlocking scalable and intelligent network management in future 6G systems.
  arXiv  Detail & Related papers  (2025-12-19T04:47:40Z)
• Efficiency-Aware Computational Intelligence for Resource-Constrained Manufacturing Toward Edge-Ready Deployment [8.383160350994816]
  Industrial cyber physical systems operate under heterogeneous sensing, dynamics, and shifting process conditions.<n>High-fidelity datasets remain costly, confidential, and slow to obtain, while edge devices face strict limits on latency, bandwidth, and energy.<n>Motivated by these challenges, this dissertation develops an efficiency grounded computational framework that enables data lean, physics-aware, and deployment ready intelligence.
  arXiv  Detail & Related papers  (2025-12-10T05:08:55Z)
• Learning Causal Structure Distributions for Robust Planning [53.753366558072806]
  We find that learning the functional relationships while accounting for the uncertainty about the structural information leads to more robust dynamics models.<n>This in contrast with common model-learning methods that ignore the causal structure and fail to leverage the sparsity of interactions in robotic systems.<n>We show that our model can be used to learn the dynamics of a robot, which together with a sampling-based planner can be used to perform new tasks in novel environments.
  arXiv  Detail & Related papers  (2025-08-08T22:43:17Z)
• A Foundation Model for Massive MIMO Precoding with an Adaptive per-User Rate-Power Tradeoff [4.8310710966636545]
  We propose a transformer-based foundation model for mMIMO precoding that seeks to minimize the energy consumption of the transmitter while dynamically adapting to per-user rate requirements.<n>At equal energy consumption, zero-shot deployment of the proposed foundation model significantly outperforms zero forcing, and approaches weighted minimum mean squared error performance with 8x less complexity.<n>Our work enables the implementation of DL-based solutions in practice by addressing challenges of data availability and training complexity.
  arXiv  Detail & Related papers  (2025-07-24T17:10:06Z)
• Kriformer: A Novel Spatiotemporal Kriging Approach Based on Graph Transformers [5.4381914710364665]
  This study addresses posed by sparse sensor deployment and unreliable data by framing the problem as an environmental challenge. A graphkriformer model, Kriformer, estimates data at locations without sensors by mining spatial and temporal correlations, even with limited resources.
  arXiv  Detail & Related papers  (2024-09-23T11:01:18Z)
• Navigating the Human Maze: Real-Time Robot Pathfinding with Generative Imitation Learning [0.0]
  We introduce goal-conditioned autoregressive models to generate crowd behaviors, capturing intricate interactions among individuals. The model processes potential robot trajectory samples and predicts the reactions of surrounding individuals, enabling proactive robotic navigation in complex scenarios.
  arXiv  Detail & Related papers  (2024-08-07T14:32:41Z)
• Federated Hybrid Model Pruning through Loss Landscape Exploration [17.589308358508863]
  Federated learning (FL) stands at the heart of this transformation.<n>We present AutoFLIP, a novel framework that optimize FL through an adaptive hybrid pruning approach.<n>We show that AutoFLIP delivers quantifiable benefits: a 48.8% reduction in computational overhead, a 35.5% decrease in communication costs, and a notable improvement in global accuracy.
  arXiv  Detail & Related papers  (2024-05-16T17:27:41Z)
• Learning-based adaption of robotic friction models [50.72489248401199]
  We introduce a novel approach to adapt an existing friction model to new dynamics using as little data as possible.<n>Our method does not rely on data with external load during training, eliminating the need for external torque sensors.
  arXiv  Detail & Related papers  (2023-10-25T14:50:15Z)
• Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse Data using a Learning-based Unscented Kalman Filter [65.93205328894608]
  We learn the residual errors between a dynamic and/or simulator model and the real robot. We show that with the learned residual errors, we can further close the reality gap between dynamic models, simulations, and actual hardware.
  arXiv  Detail & Related papers  (2022-09-07T15:15:12Z)
• Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management [64.17887333976593]
  Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. We use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization.
  arXiv  Detail & Related papers  (2022-06-21T20:38:13Z)
• Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks [50.68446003616802]
  Federated learning (FedL) has emerged as a popular technique for distributing model training over a set of wireless devices. We develop parallel successive learning (PSL), which expands the FedL architecture along three dimensions. Our analysis sheds light on the notion of cold vs. warmed up models, and model inertia in distributed machine learning.
  arXiv  Detail & Related papers  (2022-02-07T05:11:01Z)
• Sensitivity Prewarping for Local Surrogate Modeling [0.3222802562733786]
  We propose a framework for incorporating information from a global sensitivity analysis into the surrogate model. We perform an input warping such that the "warped simulator" is equally sensitive to all input directions, freeing local models to focus on local dynamics.
  arXiv  Detail & Related papers  (2021-01-15T20:42:32Z)

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.