用安全高效的3D导航让无人机在人群中自由穿梭
HumanHalo -- Safe and Efficient 3D Navigation Among Humans via Minimally Conservative MPC
- 基于模型预测控制，只约束初始动作确保全程安全
- 实测在模拟与真实场景中均实现高效可靠导航
- 适合需要避障且不僵化的无人机或移动机器人
安全高效的机器人在人群中的3D导航对融入日常环境至关重要。现有方法多聚焦于简化的2D人群导航，未能充分考虑人体运动的完整动态特性。本文提出HumanHalo，一种面向3D微型飞行器（MAV）在人群中的模型预测控制（MPC）框架，结合理论安全保证与数据驱动的人体运动预测模型。该方法创新性地采用基于可达性的安全形式化，仅对初始控制输入施加约束，但可推演其在整个规划时域内的影响，从而实现安全且高效的导航。我们在使用真实人类轨迹的仿真实验及真实世界测试中验证了HumanHalo的有效性，涵盖从目标导向导航到视觉伺服追踪人类等多种任务。尽管本文应用于MAV，但方法具有通用性，可适配其他平台。结果表明，该方法在不引入过度保守性的情况下，优于基线方法，在效率与可靠性上均有提升。
原文摘要 · Abstract (English)
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, a Model Predictive Control (MPC) framework for 3D Micro Air Vehicle (MAV) navigation among humans that combines theoretical safety guarantees with data-driven models for realistic human motion forecasting. Our approach introduces a novel twist to 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 MAVs in this work, it is generic and can be adapted by other platforms. Our results show that the method ensures safety without excessive conservatism and outperforms baseline approaches in both efficiency and reliability.
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