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This paper introduces a decentralized multi-robot system for detecting and tracking floating containers in maritime environments, using a team of UAVs and an autonomous surface vessel. The system employs YOLOv8 and stereo disparity for visual detection on each UAV, followed by per-object Extended Kalman Filters (EKFs) for tracking with uncertainty-aware data association. Track summaries are exchanged and fused using covariance intersection to maintain consistency, and an information-driven assignment module optimizes target allocation and UAV viewpoints.
Achieve robust maritime monitoring with a decentralized multi-robot system that balances coverage, accuracy, and communication efficiency.
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.