Decentralized Multi-Robot Obstacle Detection and Tracking in a Maritime Scenario

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Autori principali:	Ahmed, Muhammad Farhan, Frémont, Vincent
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Robotics
Accesso online:	Acceder al recurso
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author	Ahmed, Muhammad Farhan Frémont, Vincent
author_facet	Ahmed, Muhammad Farhan Frémont, Vincent
contents	Autonomous aerial-surface robot teams offer a scalable solution for maritime monitoring, but deployment remains difficult due to water-induced visual artifacts and bandwidth-limited coordination. This paper presents a decentralized multi-robot framework to detect and track floating containers using multiple UAVs cooperating with an autonomous surface vessel. Each UAV runs a YOLOv8 detector augmented with stereo disparity and maintains per-target EKF tracks with uncertainty-aware data association. Robots exchange compact track summaries that are fused conservatively using Covariance Intersection, preserving estimator consistency under unknown cross-correlations. An information-driven allocator assigns targets and selects UAV hover viewpoints by trading expected uncertainty reduction in travel effort and safety separation. Implemented in ROS, the proposed system is validated in simulations and compared with representative tracking and fusion baselines, showing improved identity continuity and localization accuracy with modest communication overhead.
format	Preprint
id	arxiv_https___arxiv_org_abs_2602_12012
institution	arXiv
publishDate	2026
record_format	arxiv
spellingShingle	Decentralized Multi-Robot Obstacle Detection and Tracking in a Maritime Scenario Ahmed, Muhammad Farhan Frémont, Vincent Robotics Autonomous aerial-surface robot teams offer a scalable solution for maritime monitoring, but deployment remains difficult due to water-induced visual artifacts and bandwidth-limited coordination. This paper presents a decentralized multi-robot framework to detect and track floating containers using multiple UAVs cooperating with an autonomous surface vessel. Each UAV runs a YOLOv8 detector augmented with stereo disparity and maintains per-target EKF tracks with uncertainty-aware data association. Robots exchange compact track summaries that are fused conservatively using Covariance Intersection, preserving estimator consistency under unknown cross-correlations. An information-driven allocator assigns targets and selects UAV hover viewpoints by trading expected uncertainty reduction in travel effort and safety separation. Implemented in ROS, the proposed system is validated in simulations and compared with representative tracking and fusion baselines, showing improved identity continuity and localization accuracy with modest communication overhead.
title	Decentralized Multi-Robot Obstacle Detection and Tracking in a Maritime Scenario
topic	Robotics
url	https://arxiv.org/abs/2602.12012

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