CPS: Medium: Correct-by-Construction Controller Synthesis using Gaussian Process Transfer Learning
This project proposes a novel and rigorous methodology for the design of embedded control software for safety-critical cyber-physical systems (CPS) with complex and possibly unknown dynamics by embracing ideas from control theory, formal verification in computer science, and Gaussian processes (GPs) from machine learning.
ALPACA: Autonomous Learning with Probability & Abstraction for Competency Awareness
Multi-vehicle Collaboration with Minimal Communications and Minimal Energy
Targeted Observation by Radars and UAS in Supercells (TORUS)
The TORUS project (Targeted Observation by Radars and UAS of Supercells) will be conducted by more than 50 scientists and students deploying a broad suite of cutting-edge instrumentation into the US Great Plains during the 2019 and 2022 storm seasons. TORUS aims to use the data collected to improve the conceptual model of supercell thunderstorms (the parent storms of the most destructive tornadoes) by exposing how small-scale structures within these storms might lead to tornado formation.
MARBLE: Multi-agent Autonomy with RADAR-based Localization for Exploration
The DARPA Subterranean, or "SubT", Challenge seeks novel approaches to rapidly map, navigate, and search underground environments during time-sensitive combat operations or disaster response scenarios. As a response to the challenge, MARBLE is a small team of autonomous robotic drones (ground, legged, and aerial) equipped to deploy a temporary, highly adaptable wireless mesh network and use it to simultaneously map, navigate and search the underground environment’s tunnels, identify artifacts and communicate information to an above-ground relay station.
Targeted observation and seeding using autonomous unmanned aircraft systems
The main goal of this research is to develop and assess an autonomous UAS that utilizes in-situ real time data to sense, target and implement seeding. This project addresses an innovative approach towards autonomous enhancement of precipitation: to develop new sensing technologies, to create new data assimilation tools, to design novel targeted sampling and delivery strategies, and to integrate them into an unmanned aircraft system (UAS) for real-time, autonomous guidance of rainfall enhancement operations.
I/UCRC Phase 2: Center for Unmanned Aircraft Systems: I/UCRC for Unmanned Aircraft Systems
The Center for Unmanned Aircraft Systems (C-UAS) was established in 2012 and is an Industry/University Cooperative Research Center involving Brigham Young University, the University of Colorado at Boulder, Virginia Tech, the University of Michigan, and Texas A&M University. The center is the only National Science Foundation-funded unmanned aircraft research center.
Coordinated Persistent Airborne Information Gathering: Cloud Robotics in the Clouds
Old Projects
Severe-storm Targeted Observation and Robotic Monitoring (STORM)
This project addresses the development of autonomous self-deploying aerial robotic systems (SDARS) that will enable new in-situ atmospheric science applications through targeted observation.
User-Adaptive Assurances for Enhancing Trust
This project will investigate the closed loop interactions between assured autonomy and user trust. This project investigates a model-based approach to understanding how user trust evolves in systems consisting of a supervising user and an autonomous agent.
Scalable Cooperative Tracking of Moving RF Ground Targets
This work will develop a new approach to decentralized sensor fusion and trajectory optimization to enable multiple networked UAS assets to cooperatively localize moving RF signal sources on the ground in the presence of uncertainties in ownship states and sensing models. Our approach ties together model predictive planning with the recently developed idea of factorized distributed data fusion, which allows each tracker vehicle to ignore state uncertainties for other vehicles and absorb new target state and local model information without sacrificing overall estimation performance.
Project Storm
Project Storm leverages the CU-Boulder’s proven expertise and extensive capability in nomadic, integrated collection of real-time supercell data using unmanned aircraft systems (UAS) to better predict tornadogenesis and dramatically increase Tornado Warning lead-time. CU-Boulder collaborates with an established network of government, academic, and industry partners to improve measurement and collection of supercell data. The CU-Boulder Earth Lab works closely with IRISS to process and distribute datasets for improved tornado forecasting techniques.
Project Forest
Project Forest will employ CU-Boulder’s highly sophisticated unmanned aircraft infrastructure for remote sensing, data collection, and monitoring of ecosystem health - such as vegetation growth and tree distribution. Earth Lab will then process and analyze collected data in collaboration with the Cooperative Institute for Research in Environmental Sciences (CIRES) and the Institute of Arctic and Alpine Research (INSTAAR), also located in Boulder, to inform future ecological and conservationist endeavors.
Persistent Information-Gathering with Airborne Surveillance and Communication Networks in Fading Environments
The purpose of this collaborative effort is to develop and evaluate a hierarchical framework for real-time, path planning for persistent information-gathering tasks in fading communication environments. Novel features of the proposed work are learning the radio environment and background winds; consideration of the energy that can be gained from wind field patterns (energetics); and a persistent, communication-aware information-based formulation of sensing tasks (informatics) that can integrate multiple sensing modalities.
Collaborative Research: IUCRC: FRP: Network Enabled Airborne Autonomy
This effort will investigate the value of utilizing cloud robotics techniques for network-enabled airborne autonomy. Algorithms will be designed and assessed that allow cooperating unmanned aircraft to access dispersed cloud computing resources. This infrastructure will be used to perform complex computation in support of autonomous perception, planning, and learning tasks.
Collaborative Research: RAPID: Integration of Unmanned Aircraft System (UAS) into the Program for Research on Elevated Convection with Intense Precipitation
Funded by the National Science Foundation (NSF), the goal of this project is to further the knowledge of the nature of elevated convection with heavy rain. The University of Colorado is teaming with the University of Missouri and the University of Nebraska to integrate unmanned aircraft into the PRECIP field campaign.
Efficient Reconfigurable Cockpit Design and Fleet Operations using Software Intensive, Networked and Wireless Enabled Architecture (ECON)
The purpose of this project is to investigate the utility of software-intensive, networked, wireless, reconfigurable cockpit design for safe and efficient fleet operations. A large collaborative team is being led by the NASA Ames Research Center. We will make specific contributions in the area of cybersecurity for networked operations.