Urban water bodies are complex environmental systems, which are further complicated by the dynamics of urban run-offs and closely woven human activities. Maintaining clean, healthy and aesthetically pleasing water bodies for recreational activities and potable use requires careful and effective monitoring to facilitate effective management. While modern robotics has started to improve the traditionally labour-intensive measurement process, measuring varying spatiotemporal field remains a challenge today.

The project aims to improve the sampling efficiency by developing informative path planning algorithms to replace naïve path planning in existing robots. Building on top of real-time data collected by a team of connected robots, such as NUSwan, the algorithms learn time-efficient models of the field and use them to plan paths that minimise the overall error of the estimated field. Additionally, we also extend the path-planning algorithm to sample water and estimate the field simultaneously to ensure a valid sample of interest are collected.

The project also integrates cost-effective smart sensors and combines their operations with industrial standard sensors to form a heterogenous monitoring system for big-data collection in the reservoirs. The system consists of NUSwan, drifters and static nodes to collectively improve the monitoring efficiency and accuracy.

This project involves collaboration with industry and research partners across different aspects. These include collaboration with LightHaus Photonics Pte Ltd to develop a novel, cost-effective crystal-based spectrometer for Chlorophyll-a and potentially turbidity sensing,  Subnero Pte Ltd to develop an efficient software agent framework to provide a modern and scalable integrated decision support system for the operations of the heterogeneous system, and the Department of Biological Sciences to use robots with adaptive path planning to efficiently collect targeted water samples to study the relationship between microorganism communities and water parameters. The project constantly looks for new collaborations to expand the use of the smart system in environmental sensing.