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

2026-02-12

AI Summary

This work proposes a decentralized cooperative system of multiple unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) for robust and scalable detection and tracking of floating obstacles—such as shipping containers—in communication-constrained maritime environments. The system integrates YOLOv8 with stereo disparity for visual detection and employs a per-target extended Kalman filter (EKF) for tracking. To ensure estimation consistency under unknown cross-platform correlation, it introduces a novel covariance intersection–based conservative trajectory fusion mechanism coupled with an uncertainty-aware data association method. Furthermore, an information-driven viewpoint selection and task allocation strategy is designed to significantly enhance target coverage, localization accuracy, and tracking consistency while maintaining low communication overhead.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Motion & Tracking
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Autonomous aerial-surface robot teams are promising for maritime monitoring. Robust deployment requires reliable perception over reflective water and scalable coordination under limited communication. We present a decentralized multi-robot framework for detecting and tracking floating containers using multiple UAVs cooperating with an autonomous surface vessel. Each UAV performs YOLOv8 and stereo-disparity-based visual detection, then tracks targets with per-object EKFs using uncertainty-aware data association. Compact track summaries are exchanged and fused conservatively via covariance intersection, ensuring consistency under unknown correlations. An information-driven assignment module allocates targets and selects UAV hover viewpoints by trading expected uncertainty reduction against travel effort and safety separation. Simulation results in a maritime scenario demonstrate improved coverage, localization accuracy, and tracking consistency while maintaining modest communication requirements.

Problem

• decentralized multi-robot
• obstacle detection
• object tracking
• maritime scenario
• limited communication

Innovation

• decentralized multi-robot
• covariance intersection
• uncertainty-aware tracking
• information-driven assignment
• maritime perception