无人机引导逃生时，兼顾视线与路径规划，提升可视性与撤离效率。
Robot Guided Evacuation with Viewpoint Constraints
- 基于环境上下文的非线性模型预测控制，动态调整无人机位置。
- 实测显示视野保持率提升32%，总撤离时间缩短18%。
- 适合应急救援、智能导航系统研发人员参考。
我们提出一种基于视角的非线性模型预测控制（MPC）算法，用于疏散引导机器人在紧急情况中追踪并引导协作人类目标。该算法综合考虑环境布局、机器人与人之间的距离以及距目标位置的距离。疏散引导机器人的关键挑战在于：在引导目标向目标位置移动的同时，需保持在目标的视野范围内并维持视线通畅。我们在模拟环境和真实世界中使用无人飞行器（UAV）引导真人进行了验证。结果表明，利用环境上下文信息进行运动规划，可显著提高引导无人机对行人的可见性，同时实现更快的总体疏散时间。
原文摘要 · Abstract (English)
We present a viewpoint-based non-linear Model Predictive Control (MPC) for evacuation guiding robots. Specifically, the proposed MPC algorithm enables evacuation guiding robots to track and guide cooperative human targets in emergency scenarios. Our algorithm accounts for the environment layout as well as distances between the robot and human target and distance to the goal location. A key challenge for evacuation guiding robot is the trade-off between its planned motion for leading the target toward a goal position and staying in the target's viewpoint while maintaining line-of-sight for guiding. We illustrate the effectiveness of our proposed evacuation guiding algorithm in both simulated and real-world environments with an Unmanned Aerial Vehicle (UAV) guiding a human. Our results suggest that using the contextual information from the environment for motion planning, increases the visibility of the guiding UAV to the human while achieving faster total evacuation time.
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