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

TL;DR

This paper introduces a decentralized multi-robot system for maritime obstacle detection and tracking, combining UAVs and surface vessels with advanced perception, data fusion, and task allocation to improve accuracy and efficiency in water environments.

Contribution

It presents a novel decentralized framework integrating YOLOv8 detection, stereo disparity, EKF tracking, and covariance intersection fusion for maritime obstacle monitoring.

Findings

Enhanced identity continuity and localization accuracy compared to baselines.

Effective fusion of robot data with modest communication overhead.

Validated system performance in simulation environments.

Abstract

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.