Mr.MSTE: Multi-robot Multi-Source Term Estimation with Wind-Aware Coverage Control

• URL: http://arxiv.org/abs/2512.17001v1
• Date: Thu, 18 Dec 2025 19:02:07 GMT
• Title: Mr.MSTE: Multi-robot Multi-Source Term Estimation with Wind-Aware Coverage Control
• Abstract summary: The framework is built on a hybrid Bayesian inference scheme that represents the joint multi-source probability density.<n>A superposition-based measurement model is naturally accommodated, allowing sparse concentration measurements to be exploited efficiently.
• Score: 5.045646483880712
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a Multi-Robot Multi-Source Term Estimation (MRMSTE) framework that enables teams of mobile robots to collaboratively sample gas concentrations and infer the parameters of an unknown number of airborne releases. The framework is built on a hybrid Bayesian inference scheme that represents the joint multi-source probability density and incorporates physics-informed state transitions, including source birth, removal, and merging induced by atmospheric dispersion. A superposition-based measurement model is naturally accommodated, allowing sparse concentration measurements to be exploited efficiently. To guide robot deployment, we introduce a wind-aware coverage control (WCC) strategy that integrates the evolving multi-source belief with local wind information to prioritize regions of high detection likelihood. Unlike conventional coverage control or information-theoretic planners, WCC explicitly accounts for anisotropic plume transport when modelling sensor performance, leading to more effective sensor placement for multi-source estimation. Monte Carlo studies demonstrate faster convergence and improved separation of individual source beliefs compared to traditional coverage-based strategies and small-scale static sensor networks. Real-world experiments with CO2 releases using TurtleBot platforms further validate the proposed approach, demonstrating its practicality for scalable multi-robot gas-sensing applications.

Related papers

• Probabilistic Multi-Robot Gas Source Localization with Uncalibrated Sensors: A Distributed Estimation Approach [7.570246812206771]
  We propose a distributed probabilistic framework for source localization tasks.<n>Each robot independently estimates a local belief using a rank-based feature.<n>These local beliefs are then fused through a product of experts formulation to obtain a consistent global estimate.
  arXiv  Detail & Related papers  (2026-08-28T11:32:53Z)
• Sequentially-Controlled Interactive Multi-Particle Flow-Maps for Online Feedback-Driven Search [26.617381371184635]
  Sequentially-Controlled Interactive Multi-Particle Flow-Maps (IMPFM) is a framework for sample-efficient online feedback-driven search.<n>IMPFM introduces a principled and efficient posterior sample sharing mechanism across particles powered by flow maps.<n>We prove that the resulting sampling framework yields a multi-particle interaction-aware Feynman-Kac corrector.
  arXiv  Detail & Related papers  (2026-07-01T16:27:17Z)
• Active Sensing for RIS-Aided Tracking and Power Control: A Hybrid Neuroevolution and Supervised Learning Approach [31.34066005342801]
  This paper studies energy efficient tracking of power-limited mobile users with the assistance of a Reconfigurable Intelligent Surface (RIS)<n>Since localization pilot transmissions dominate the energy budget of power-constrained devices, we introduce a low-overhead feedback link from the Base Station (BS) to the user to enable dynamic uplink power control.<n>To navigate the discrete and decentralized nature of this active sensing problem, we propose a novel Dual-Agent (DA) deep learning framework.
  arXiv  Detail & Related papers  (2026-06-30T07:43:28Z)
• AI-Augmented Density-Driven Optimal Control (D2OC) for Decentralized Environmental Mapping [0.0]
  This paper presents an AI-augmented decentralized framework for multi-agent (multi-robot) environmental mapping under limited sensing and communication.<n>The proposed method introduces an adaptive and self-correcting mechanism that enables agents to iteratively refine local density estimates.
  arXiv  Detail & Related papers  (2026-01-28T23:55:02Z)
• ScaleWeaver: Weaving Efficient Controllable T2I Generation with Multi-Scale Reference Attention [86.93601565563954]
  ScaleWeaver is a framework designed to achieve high-fidelity, controllable generation upon advanced visual autoregressive( VAR) models.<n>The proposed Reference Attention module discards the unnecessary attention from image$rightarrow$condition, reducing computational cost.<n>Experiments show that ScaleWeaver delivers high-quality generation and precise control while attaining superior efficiency over diffusion-based methods.
  arXiv  Detail & Related papers  (2025-10-16T17:00:59Z)
• Discrete-Guided Diffusion for Scalable and Safe Multi-Robot Motion Planning [56.240199425429445]
  Multi-Robot Motion Planning (MPMP) involves generating trajectories for multiple robots operating in a shared continuous workspace.<n>While discrete multi-agent finding (MAPF) methods are broadly adopted due to their scalability, their coarse discretization trajectory quality.<n>This paper tackles limitations of two approaches by introducing discrete MAPF solvers with constrained generative diffusion models.
  arXiv  Detail & Related papers  (2025-08-27T17:59:36Z)
• Joint Multi-Target Detection-Tracking in Cognitive Massive MIMO Radar via POMCP [42.60472546232188]
  This correspondence presents a power-aware cognitive radar framework for joint detection and tracking of multiple targets in a massive multiple-input multiple-output (MIMO) radar environment.<n>Building on a previous single-target algorithm based on Partially Observable Monte Carlo Planning (POMCP), we extend it to the multi-target case by assigning each target an independent POMCP tree.
  arXiv  Detail & Related papers  (2025-07-23T13:43:29Z)
• Multi-source Plume Tracing via Multi-Agent Reinforcement Learning [41.03292974500013]
  Industrial catastrophes like the Bhopal disaster demonstrate the need for rapid and reliable plume tracing algorithms.<n>Traditional methods, such as gradient-based or biologically inspired approaches, often fail in realistic, turbulent conditions.<n>We present a Multi-Agent Reinforcement Learning (MARL) algorithm designed for localizing multiple airborne pollution sources.
  arXiv  Detail & Related papers  (2025-05-12T21:33:15Z)
• Resource-Efficient Beam Prediction in mmWave Communications with Multimodal Realistic Simulation Framework [57.994965436344195]
  Beamforming is a key technology in millimeter-wave (mmWave) communications that improves signal transmission by optimizing directionality and intensity.<n> multimodal sensing-aided beam prediction has gained significant attention, using various sensing data to predict user locations or network conditions.<n>Despite its promising potential, the adoption of multimodal sensing-aided beam prediction is hindered by high computational complexity, high costs, and limited datasets.
  arXiv  Detail & Related papers  (2025-04-07T15:38:25Z)
• Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning [59.660724802286865]
  Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications.<n>This work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users.
  arXiv  Detail & Related papers  (2025-02-09T09:15:47Z)
• IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint Multi-Agent Trajectory Prediction [73.25645602768158]
  IPCC-TP is a novel relevance-aware module based on Incremental Pearson Correlation Coefficient to improve multi-agent interaction modeling. Our module can be conveniently embedded into existing multi-agent prediction methods to extend original motion distribution decoders.
  arXiv  Detail & Related papers  (2023-03-01T15:16:56Z)
• F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning [110.35516334788687]
  Decentralized multi-agent reinforcement learning algorithms are sometimes unpractical in complicated applications. We propose a flexible fully decentralized actor-critic MARL framework, which can handle large-scale general cooperative multi-agent setting. Our framework can achieve scalability and stability for large-scale environment and reduce information transmission.
  arXiv  Detail & Related papers  (2020-04-17T14:56:29Z)

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.