We are developing new adaptive planning techniques for pervasive battle-space situational awareness with ground and aerial sensors. This project is funded by AFOSR's DDDAS program.
What if hundreds of robots and drones worked together to optimize your farm?
What if excavators and backhoes learned from the best operators and taught you new tricks?
What if thousands of drones and robots collaborated to reveal denied and concealed information?
These and other visions for a better tomorrow drive our research at the Distributed Autonomous Systems (DAS) Laboratory. We pursue research at the intersection of decision and control theory, machine learning, and robotics. Our goal is to enable the next generation of autonomous systems that operate reliably in stochastic time-varying and distributed settings. We are a highly motivated and dedicated group of researchers focused on challenging the state of the art.
You may visit us in AESB 128 on the University of Illinois Urbana-Champaign campus.
Director: A. Prof. Girish Chowdhary
Algorithms for Spatiotemporal Agricultural Data Modeling
Algorithms and software for Bayesian Nonparametric Learning
Familiarity Based Navigation Algorithms
Planning and Reinforcement Learning Algorithms in Dynamic time-varying Environments
Learning from Demonstration and Input Segmentation Algorithms
Muggin: 10 ft wingspan fixed-wing UAS
Penguin-B: 11 ft wingspan fixed-wing UAS
Skyhunter: 6 ft wingspan fixed-wing UAS
OptiTrac Motion Capture System
UAFS Unmanned Aircraft Flight Sation
Hardware-in-the-Loop Flight Simulation
Cloud based Multi Agent Game Emulator (MAGE)
Caterpillar 1-14 Scale Excavator Model
OSU Autonomous Golf Cart Project
Stabilis Onboard Autonomy Module (Autopilot)