论文标题
赛车手：与分散的多UAV系统的快速协作探索
RACER: Rapid Collaborative Exploration with a Decentralized Multi-UAV System
论文作者
论文摘要
尽管使用多个无人机（UAV）具有快速自动探索的巨大潜力，但它的关注程度很少。在本文中，我们提出了赛车手，这是一种使用分散无人机的舰队快速的合作探索方法。为了有效派遣无人机，使用了基于在线Hgrid空间分解的成对交互。它仅使用异步和有限的沟通来确保所有无人机同时探索不同的区域。此外，我们优化了未知空间的覆盖路径，并通过电容的车辆路由问题（CVRP）配方平衡分配给每个无人机的工作负载。鉴于任务分配，每个无人机都会不断更新覆盖路径，并逐步提取至关重要的信息以支持探索计划。分层规划师可以找到探索路径，完善本地观点并生成序列的最小时间轨迹，以敏捷，安全地探索未知的空间。对所提出的方法进行了广泛的评估，显示出较高的勘探效率，可扩展性和对有限交流的鲁棒性。此外，我们第一次与现实世界中多个无人机进行了完全分散的协作探索。我们将作为开源软件包发布实施。
Although the use of multiple Unmanned Aerial Vehicles (UAVs) has great potential for fast autonomous exploration, it has received far too little attention. In this paper, we present RACER, a RApid Collaborative ExploRation approach using a fleet of decentralized UAVs. To effectively dispatch the UAVs, a pairwise interaction based on an online hgrid space decomposition is used. It ensures that all UAVs simultaneously explore distinct regions, using only asynchronous and limited communication. Further, we optimize the coverage paths of unknown space and balance the workloads partitioned to each UAV with a Capacitated Vehicle Routing Problem(CVRP) formulation. Given the task allocation, each UAV constantly updates the coverage path and incrementally extracts crucial information to support the exploration planning. A hierarchical planner finds exploration paths, refines local viewpoints and generates minimum-time trajectories in sequence to explore the unknown space agilely and safely. The proposed approach is evaluated extensively, showing high exploration efficiency, scalability and robustness to limited communication. Furthermore, for the first time, we achieve fully decentralized collaborative exploration with multiple UAVs in real world. We will release our implementation as an open-source package.
