Flying Together: Human-Guided Immersive Shared Control for Aerial Robot Teams in Unknown Environments
Flying Together: Human-Guided Immersive Shared Control for Aerial Robot Teams in Unknown Environments
AI总结：
本文提出了一种基于虚拟现实的共享控制框架，用于在约束和未知环境中操作无人机团队，通过实时用户引导探索，提升在无结构环境中的自主导航能力。核心方法是一种基于用户引导的运动原语规划器，结合阻抗控制器，使操作员能够灵活影响团队行为并引导无人机前往自主规划器可能忽略的感兴趣区域。
摘要：
尽管自主多机器人能够实现安全协调的导航，但它们往往难以适应突发状况并捕捉操作员驱动的目标。本文提出了一种基于虚拟现实（VR）的共享控制框架，用于在约束和未知环境中操作无人机团队，实现实时用户引导探索。我们的方法核心是一种新颖的基于用户引导的运动原语规划器，能够计算连续的碰撞免费轨迹，同时持续整合操作员输入。该规划器与阻抗控制器相结合，使操作员能够灵活影响团队行为并引导无人机前往感兴趣区域。系统支持混合现实操作，包括物理和模拟无人机，并实现双方面VR接口，使操作员通过迁移点引导机器人团队，同时接收即时的团队状态视觉反馈。实验结果表明，共享控制提高了障碍物避障能力，保持了机器人间的间距，并减少了操作员的负担，展示了沉浸式、人机协作多机器人导航的可行性和优势。
英文摘要：
While autonomous multi-robots can achieve safe and coordinated navigation, they often struggle to adapt to unforeseen conditions and to capture operator-driven objectives in unstructured environments. We present a Virtual Reality (VR)-based shared control framework for teams of drones operating in constrained and unknown environments, enabling real-time, user-guided exploration. At the core of our approach is a novel, user-guided motion-primitive-based planner that computes continuous, collision-free trajectories while continuously integrating operator input. This planner is coupled with an admittance controller, allowing the operator to flexibly influence team behavior and guide drones toward regions of interest that autonomous planners may overlook. The system supports mixed-reality operations with both physical and simulated drones, and implements a bilateral VR-based interface, allowing the operator to guide the robot team via migration points while receiving immediate visual feedback of the team state. Experimental results show that shared control improves obstacle avoidance, maintains inter-agent spacing, and reduces operator effort, demonstrating the feasibility and advantages of immersive, human-in-the-loop multi-robot navigation.
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。
