多无人机协同追踪海上集装箱，降低通信开销并提升定位精度。
Decentralized Multi-Robot Obstacle Detection and Tracking in a Maritime Scenario
- 各无人机用YOLOv8+立体视觉检测，用扩展卡尔曼滤波跟踪目标。
- 通过协方差交集融合轨迹，保持估计一致性，通信量小。
- 信息驱动分配策略优化航点选择，兼顾精度与安全距离。
自主空-海机器人团队为海上监测提供了可扩展的解决方案，但受海水引起的视觉失真和带宽受限的协调问题影响，部署仍具挑战。本文提出一种去中心化的多机器人框架，利用多架无人机与一艘自主水面船协作，检测并跟踪漂浮集装箱。每架无人机运行增强立体视差的YOLOv8检测器，并对每个目标维护带有不确定性感知的数据关联的EKF轨迹。机器人之间交换紧凑的轨迹摘要，采用协方差交集进行保守融合，确保在未知交叉相关性下的估计一致性。信息驱动分配器通过权衡预期不确定性降低、飞行代价与安全间隔，分配目标并选择无人机悬停视角。系统在ROS中实现，经仿真验证并与代表性跟踪与融合基线对比，结果显示身份连续性和定位精度显著提升，且通信开销适中。
原文摘要 · Abstract (English)
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.
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