A Training-Testing-Benchmarking Environment for Learning-Enabled Control Frameworks and Algorithms
Abstract
In this proposal, we request funding to develop a training-testing-benchmarking environment to validate learning-enabled control (LEC) frameworks and algorithms on physical systems. This environment will consist of i) a server that hosts advanced graphical processing units (GPUs) to provide sufficient computational power for training machine learning models; ii) a scaled-down urban testing environment, where autonomous cars and drones running the developed LEC algorithms are deployed for testing in a test site with scaled-down buildings, roads, and traffic lights and signs that support various types of urban appearances; and iii) a linear double inverted pendulum as a standard testbed for benchmarking the studied LEC frameworks and algorithms. The ultimate goal of the proposed equipment is to promote breakthroughs in LEC frameworks and algorithms that can safely and reliably run on physical systems. The results can be applied to the domains with a high level of autonomy yet with dynamically varying environments, e.g., autonomous driving and urban air mobility. This testbed is fundamental for performing experiments that will verify the safety and reliability of already existing LEC frameworks and algorithms as well as those that are still in development. When fully operational, the proposed environment will allow fast iterations between training, testing, and bench- marking, with the sufficient computational powers enabled by the GPU server. More importantly, the extensive training and testing within the proposed environment will refine the workflow, generate software tools, and enhance the quality and performance of LEC frameworks and algorithms when deployed to physical systems. Last but not least, the availability of such an environment will significantly benefit research efforts in (cooperative) trajectory planning, decentralized collaboration and coordination of heterogeneous multiagent systems, L1 adaptive augmentation for autonomous aerial-ground vehicles, and other relevant state-of-the-art research projects at UIUC.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 29, 2024
- Source ID
- FA95502310129
Entities
People
- Naira Hovakimyan
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of Illinois Urbana–Champaign