在真实场景中部署 SICNav：基于 MPC 和双层优化的安全交互式人群导航
Deploying SICNav in the Field: Safe and Interactive Crowd Navigation using MPC and Bilevel Optimization
AI总结：
本文介绍 SICNav 的真实部署系统，其通过双层 MPC 将人群运动预测与机器人规划联合优化，以建模人机交互，并在未见室内外环境中完成近 7 公里自主导航验证。
摘要：
在拥挤环境中安全高效地导航，对于执行送餐或自主轮椅移动等多种服务任务的机器人而言仍是一项关键挑战。经典的机器人人群导航方法将人体运动预测与机器人运动规划解耦，忽视了人与机器人之间的闭环交互。这种缺乏人类对机器人方案反应（例如让路）模型的情况，可能导致机器人卡住。我们提出的安全交互式人群导航（SICNav）方法是一种双层模型预测控制（MPC）框架，将预测和规划结合到一个优化问题中，显式建模智能体之间的交互。在本文中，我们概述了用于在先前未见过的室内和室外环境中部署 SICNav 的人群导航平台系统。我们对该系统在室内和室外环境中、两小时内近 7 公里自主导航过程中的运行情况进行了初步分析。
英文摘要：
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.
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。
