Approximate dynamic programming

Project Summary

Intelligent mobile sensor agent can adapt to heterogeneous environmental conditions, to achieve the optimal performance, such as demining, maneuvering target tracking. 

Themes and Categories
Wenjie Lu

The mobile sensor agent is a robot with onboard sensors, and it is deployed to navigate obstacle-populated workspaces subject to sensing objectives. The expected performance of available future measurements is estimated using information theoretic metrics, and is optimized while minimizing the cost of operating the sensors, including distance. Approximate dynamic programming and non-parametric Bayesian models are studied in the heterogeneous system.

Related People

Related Projects

Caroline Tang (Math/Stats) joined CS majors Frankie Willard and Alex Kumar in a ten-week exploration of AI methods to improve the mapping of energy infrastructure within satellite imagery. The team used cutting-edge methods to create synthetic imagery that, when blended with real imagery, improved the performance of deep learning methods on the energy infrastructure detection task.


View the team's project poster here

Watch the team's final presentation on Zoom here:


Project Lead: Kyle Bradbury

Project Manager: Wei Hu

Tejvasi Patil (MEM), Sophia Stameson (CS), and Larry Zheng (Bio) spent ten weeks working with drone footage from different rainforest sources. The team designed a pipeline that performed image classification on the drone footage, and curated a training dataset using SQL.


View the team's project poster here

Watch the team's final presentation on Zoom:


Project Lead: Martin Brooke

Project Manager: Ryan Huang