Active sensing and inference for robotic environment monitoring

有害污染物和人为排放都与公共健康和长期气候变化密切相关。有效和及时的污染物扩散监测对于评估排放源、了解污染物扩散和预测有害空气传播物质的去向至关重要。本项目旨在开发自主移动的机器人，通过在特定地点水平上提供对目标事件/区域的污染物、有害物质或温室气体的实时、高时空感知，快速响应此类情景。机器人系统通常需要在GPS拒绝的环境中操作，而没有事先的地图或可靠的通信进行遥操作。因此，该项目的重点是开发一个主动传感和推理框架，该框架将允许机器人通过自主探索该区域并主动收集环境数据来更好地了解环境，同时保持可靠的同步定位和映射（SLAM）。整个系统将部署在真实的机器人系统上，并在现实的操作环境中进行测试。

Both hazardous contaminations and anthropogenic emissions are closely intertwined with public health and long-term climate change. Effective and prompt pollutant dispersion monitoring is of paramount importance in assessing emission sources, understanding contamination dispersions, and predicting the whereabouts of hazardous airborne materials.This project aims to develop autonomous mobile robots to rapidly respond to such scenarios by providing real-time, high spatiotemporal awareness of pollutants, hazardous materials or greenhouse gases for a targeted event/region at site-specific level. The robotic systems are often required to operate in GPS-denied environments without a prior map or reliable communication for teleoperation. Therefore, the focus of this project is to develop an active sensing and inference framework which would allow the robot to establish a better understanding of the environment by autonomously exploring the area and actively collecting environmental data, while maintaining reliable simultaneous localization and mapping (SLAM). The overall system will be deployed on real robotic systems and tested in realistic operational environments.