Background
The overall guiding motivation is to provide useful tools to enable rapid path planning for autonomous vehicle operators in real-time and to train operators in large surface area food systems. This work develops a digital twin and machine-learning optimization framework for model problems combining:
- Fluid-dynamics of released objects ranging from powders to encapsulated packets and sensors
- Ground-based vehicle or aircraft (unmanned or manned) dynamics
- Energy-efficient path planning of multiple ground or air vehicles
- Lidar-based and hyperspectral data extraction for agricultural mapping for planning and assessment.
Goals
Specifically, we will develop clean and easy-to-use digital tools that are repeatedly in demand by food system researchers such as:
- Data collection
- Compression and assimilation from the environment
- Rapid ODE and PDE solvers
- Neural nets and genetic algorithms with embedded detailed models of fundamental physics (fluid flow, heat transfer, stress analysis, electromagnetism, optics, robotics, etc.)
Impact
The framework is designed to enable digital twin type technologies to run at much faster rates, in order to enable machine-learning to optimize the planning.
Project Team

Ana C. Arias
Professor of Electrical Engineering and Computer Sciences, UC Berkeley

Mason Earles
Assistant Professor of Viticulture & Enology and Biological & Agricultural Engineering, UC Davis

Isaya Kisekka
Associate Professor of Land, Air, and Water Resources, UC Davis

Ethan Ligon
Associate Professor of Agricultural and Resource Economics, UC Berkeley

Xin Liu
Professor of Computer Science, UC Davis

Simo Mäkiharju
Assistant Professor of Mechanical Engineering, UC Berkeley

Khalid Mosalam
Professor of Civil Engineering, UC Berkeley

Mark Mueller
Assistant Professor of Mechanical Engineering, UC Berkeley

Ilias Tagkopoulos
Professor of Computer Science and the Genome Center, UC Davis

Tarek Zohdi
Professor of Mechanical Engineering, UC Berkeley