具有视觉意识的协同空中跟踪与基于去中心化激光雷达的群体
Visibility-Aware Cooperative Tracking with Decentralized LiDAR-Based Aerial Swarms
AI总结：
本文提出了一种基于去中心化激光雷达的群体协同跟踪框架，通过SSDF度量和电势启发分布度量，实现复杂环境中的可视化目标跟踪与动态群体重构。
摘要：
自主无人机空中跟踪为监视、 cinematography 和工业检查应用提供了巨大潜力。尽管单无人机跟踪系统已广泛研究，但基于群体的目标跟踪仍处于探索阶段，尽管其具有分布式感知、容错冗余和多方向目标覆盖的独特优势。为了填补这一差距，我们提出了一种新颖的去中心化基于激光雷达的群体跟踪框架，该框架能够在复杂环境中实现具有视觉意识的协同目标跟踪，同时充分利用群体系统的独特能力。为了解决可视性问题，我们引入了一种新的球面符号距离场（SSDF）度量方法用于三维环境遮挡表示，并结合一种高效的算法，使SSDF更新能够在实时 onboard 进行。提出了一种通用的视野（FOV）对齐成本，以支持异构激光雷达配置的一致目标观察。通过协作成本增强群体协调，这些成本强制机器人之间安全的间隔，防止相互遮挡，并显著促进通过新颖的静电势启发分布度量实现的三维多方向目标包围。这些创新被整合到一个分层规划器中，结合了运动动力学前端搜索器和时空SE(3)后端优化器，以生成无碰撞、可视性优化的轨迹。在异构激光雷达群体上部署，我们的完全去中心化实现具有协作感知、分布式规划和动态群体重构能力。通过严格的现实世界实验验证，在杂乱的户外环境中，所提出的系统展示了对敏捷目标（无人机、人类）的鲁棒协同跟踪，同时实现了优越的可视性维护。
英文摘要：
Autonomous aerial tracking with drones offers vast potential for surveillance, cinematography, and industrial inspection applications. While single-drone tracking systems have been extensively studied, swarm-based target tracking remains underexplored, despite its unique advantages of distributed perception, fault-tolerant redundancy, and multidirectional target coverage. To bridge this gap, we propose a novel decentralized LiDAR-based swarm tracking framework that enables visibility-aware, cooperative target tracking in complex environments, while fully harnessing the unique capabilities of swarm systems. To address visibility, we introduce a novel Spherical Signed Distance Field (SSDF)-based metric for 3-D environmental occlusion representation, coupled with an efficient algorithm that enables real-time onboard SSDF updating. A general Field-of-View (FOV) alignment cost supporting heterogeneous LiDAR configurations is proposed for consistent target observation. Swarm coordination is enhanced through cooperative costs that enforce inter-robot safe clearance, prevent mutual occlusions, and notably facilitate 3-D multidirectional target encirclement via a novel electrostatic-potential-inspired distribution metric. These innovations are integrated into a hierarchical planner, combining a kinodynamic front-end searcher with a spatiotemporal $SE(3)$ back-end optimizer to generate collision-free, visibility-optimized trajectories.Deployed on heterogeneous LiDAR swarms, our fully decentralized implementation features collaborative perception, distributed planning, and dynamic swarm reconfigurability. Validated through rigorous real-world experiments in cluttered outdoor environments, the proposed system demonstrates robust cooperative tracking of agile targets (drones, humans) while achieving superior visibility maintenance.
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