HumanHalo -- 通过最小保守MPC实现安全高效的三维人群导航
HumanHalo: Safe and Efficient 3D Navigation Among Humans via Minimally Conservative MPC
AI总结：
提出HumanHalo框架，结合可达性安全保证与数据驱动人体运动预测，通过仅约束初始控制输入实现安全高效的3D MAV人群导航，在仿真和真实实验中验证了其有效性。
摘要：
在人类环境中实现安全高效的机器人导航对于将机器人融入日常生活至关重要。现有大多数方法侧重于简化的二维人群导航，未能考虑除根运动之外人体动力学的全部复杂性。我们提出HumanHalo，一个用于三维微型飞行器（MAV）在人群中导航的模型预测控制（MPC）框架，它结合了理论安全保证与用于逼真人体运动预测的数据驱动模型。我们的方法引入了基于可达性的安全公式的新变体，该公式仅约束初始控制输入以确保安全，同时在整个规划范围内对其效果进行建模，从而实现安全且高效的导航。我们在使用真实人体轨迹的模拟实验和真实世界中验证了HumanHalo，展示了其在从目标导向导航到用于人体跟踪的视觉伺服等任务中的有效性。虽然我们在本工作中将方法应用于MAV，但它是通用的，可被其他平台采用。结果表明，该方法在不过度保守的情况下确保了安全性，并在效率和可靠性上优于基线方法。
英文摘要：
Safe and efficient robotic navigation among humans is essential for integrating robots into everyday environments. Most existing approaches focus on simplified 2D crowd navigation and fail to account for the full complexity of human body dynamics beyond root motion. We present HumanHalo, an MPC framework for 3D MAV navigation among humans that combines theoretical safety guarantees with data-driven models for realistic human motion forecasting. Our approach introduces a novel reachability-based safety formulation that constrains only the initial control input for safety while modeling its effects over the entire planning horizon, enabling safe yet efficient navigation. We validate HumanHalo in both simulated experiments using real human trajectories and in the real world, demonstrating its effectiveness across tasks ranging from goal-directed navigation to visual servoing for human tracking. While we apply our method to MAV in this work, it is generic and can be adapted to other platforms. Our results show that the method preserves safety without excessive conservatism, even under imperfect human prediction and in constrained workspaces, while remaining efficient enough for real-time onboard execution.
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