分布式多机器人在海洋场景中的障碍物检测与跟踪
Decentralized Multi-Robot Obstacle Detection and Tracking in a Maritime Scenario
AI总结：
本文提出了一种分布式多机器人框架，利用无人机与自主水面舰艇协作，通过YOLOv8检测器和扩展卡尔曼滤波实现漂浮集装箱的障碍物检测与跟踪，提升了身份连续性和定位精度。
摘要：
自主的空-表层机器人团队为海洋监测提供了可扩展的解决方案，但部署仍然困难，因为水诱导的视觉伪影和带宽受限的协调限制了性能。本文提出了一种分布式多机器人框架，利用多个无人机与自主水面舰艇协作，以检测和跟踪漂浮集装箱。每个无人机运行一个YOLOv8检测器，并结合立体视差，同时为每个目标维护带有不确定性感知的数据关联的扩展卡尔曼滤波跟踪。机器人交换紧凑的跟踪摘要，并通过协方差交叠保守融合，以在未知交叉相关性下保持估计器一致性。一个信息驱动的分配器通过在旅行努力和安全分离之间进行权衡，分配目标并选择无人机悬停视角。该系统在ROS中实现，并在模拟中验证，与代表性的跟踪和融合基线相比，显示出改进的身份连续性和定位精度，同时通信开销较低。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
