Visibility-aware Cooperative Aerial Tracking with Decentralized LiDAR-based Swarms

2025-11-30

AI Summary

To address limited visibility and unstable target tracking of UAV swarms in complex environments, this paper proposes a decentralized LiDAR-based collaborative tracking framework. Methodologically, it introduces (1) a Spherical Signed Distance Field (SSDF) for efficient occlusion-aware environmental modeling; (2) a distributed planning strategy integrating field-of-view alignment cost with an electrostatic-potential-inspired dispersion metric to enable 3D multi-directional encirclement and dynamic obstacle avoidance; and (3) a hierarchical planning architecture that couples kinematic front-end search with spatiotemporal SE(3) back-end optimization, supporting heterogeneous LiDAR configurations and online environmental updates. Evaluated in cluttered real-world outdoor scenes, the system achieves stable multi-view tracking of high-speed UAVs and pedestrians, significantly improving visibility maintenance rate and swarm-level fault tolerance robustness.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Computer Vision: Motion & Tracking
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• Develops a decentralized LiDAR swarm framework for cooperative target tracking
• Addresses environmental occlusion with real-time 3D visibility representation
• Enhances swarm coordination for safe, occlusion-free multidirectional encirclement

Innovation

• Decentralized LiDAR swarm framework for visibility-aware tracking
• Spherical Signed Distance Field metric for real-time occlusion handling
• Electrostatic-potential-inspired distribution metric for 3-D target encirclement