集中式多无人机探索与三维重建：基于单无人机规划器
Centralized Multi-UAV Exploration and 3D Reconstruction Using Single-UAV Planners
查看机构详情
- Institute for Systems and Robotics（系统与机器人研究所）
- Instituto Superior Técnico（高等技术学院）
机构由 AI 辅助整理，请以论文原文为准。
浏览论文内容
中文总结 AI 辅助
提出集中式多无人机探索框架，复用单无人机采样规划器，通过共享TSDF地图和集中规划实现协同探索，仿真表明分离启动策略显著提升探索速度和覆盖率。
中文摘要 AI 辅助
将单无人机（UAV）探索方法扩展到多无人机团队可以提高覆盖速度和鲁棒性，但也带来了诸如一致建图、安全导航和部署策略等挑战。在这项工作中，我们提出了一个集中式多无人机探索框架，使得现有的基于采样的单无人机规划器能够在多无人机环境中使用。所提出的架构允许多架无人机利用共享的全局截断符号距离场（TSDF）地图和集中式规划协同探索未知环境。基于voxblox库，我们调整了其建图流程，以支持将来自多架无人机的深度测量实时融合到共同的TSDF表示中。此外，系统集成了无人机间碰撞避免和机器人自过滤机制，以确保安全导航并防止将其他无人机重建为静态障碍物。该框架在仿真中使用四种基于采样的探索规划器——RH-NBVP、KRH-NBVP、AEP和KAEP——进行了评估，这些规划器的核心采样逻辑得以保留，仅针对多无人机操作进行了系统级调整。实验在多种环境中进行，并采用两种部署配置：联合启动（JS），即无人机在近距离初始化；分离启动（SS），即无人机在不同位置初始化。结果表明，SS部署在所有规划器中始终实现更快的探索和更高的覆盖率，凸显了部署策略在多无人机探索性能中的重要性。
英文摘要
Extending single Unmanned Aerial Vehicles (UAVs) exploration methods to multi-UAV teams can improve coverage speed and robustness, but introduces challenges such as consistent mapping, safe navigation, and deployment strategy. In this work, we present a centralized multi-UAV exploration framework that enables the use of existing single-UAV sampling-based planners in a multi-UAV setting. The proposed architecture allows multiple UAVs to collaboratively explore unknown environments using a shared global Truncated Signed Distance Field (TSDF) map and centralized planning. Building on the voxblox library, we adapt its mapping pipeline to support real-time fusion of depth measurements from multiple UAVs into a common TSDF representation. In addition, inter-UAV collision avoidance and robot self-filtering mechanisms are integrated into the system to ensure safe navigation and prevent reconstruction of other UAVs as static obstacles. The framework is evaluated in simulation using four sampling-based exploration planners - RH-NBVP, KRH-NBVP, AEP, and KAEP - whose core sampling logic is preserved, with only system-level adaptations for multi-UAV operation. Experiments are conducted across multiple environments and under two deployment configurations: Joint Start (JS), where UAVs are initialized in close proximity, and Separated Start (SS), where UAVs are initialized in distinct locations. Results show that SS deployments consistently achieve faster exploration and improved coverage across all planners, highlighting the importance of the deployment strategy in multi-UAV exploration performance.
