让机器人在人群中安全互动导航，用双层优化统一预测与规划。
Deploying SICNav in the Field: Safe and Interactive Crowd Navigation using MPC and Bilevel Optimization
- 用双层模型预测控制融合预测与规划，显式建模人机交互。
- 实测在室内外环境自主导航近7公里，持续两小时无卡顿。
- 适合服务机器人、轮椅辅助等需与人实时互动的场景。
在人群密集环境中实现安全高效的导航，仍是提供送餐、自动驾驶轮椅等服务任务的机器人所面临的关键挑战。传统方法将人类运动预测与机器人路径规划分离，忽略了人与机器人之间的闭环互动。这种对人类对机器人行为反应（如避让）缺乏建模的情况，可能导致机器人陷入停滞。本文提出的安全交互式人群导航（SICNav）方法是一种结合预测与规划的双层模型预测控制（MPC）框架，显式建模多智能体间的交互关系。本文介绍了用于在先前未见的室内外环境中部署SICNav的导航平台系统概览，并提供了初步分析：系统在室内外环境中累计实现近7公里、持续约两小时的自主导航。
原文摘要 · Abstract (English)
Safe and efficient navigation in crowded environments remains a critical challenge for robots that provide a variety of service tasks such as food delivery or autonomous wheelchair mobility. Classical robot crowd navigation methods decouple human motion prediction from robot motion planning, which neglects the closed-loop interactions between humans and robots. This lack of a model for human reactions to the robot plan (e.g. moving out of the way) can cause the robot to get stuck. Our proposed Safe and Interactive Crowd Navigation (SICNav) method is a bilevel Model Predictive Control (MPC) framework that combines prediction and planning into one optimization problem, explicitly modeling interactions among agents. In this paper, we present a systems overview of the crowd navigation platform we use to deploy SICNav in previously unseen indoor and outdoor environments. We provide a preliminary analysis of the system's operation over the course of nearly 7 km of autonomous navigation over two hours in both indoor and outdoor environments.
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