We are entering an age of unprecedented access to information, where transformational methodologies are demonstrating a clear vision of an autonomy-driven future. Self-driving cars, precision agriculture, robotic monitoring, and infrastructure inspection are but a few areas experiencing an autonomy revolution. To continue in this promising direction, it is critical that we facilitate the safe and reliable coordination of diverse cyber-physical systems (CPS). Unfortunately, at present there is a wide gap in our understanding that limits this goal: a stark divide exists between algorithms for decision-making, sensing, and motion, and underlying computational resources. This project therefore seeks to define computation-aware autonomy by answering the following questions: (1) How does an environment impact computation? (2) How should autonomy adapt to improve computational awareness? (3) How are computational resources optimized at run-time in support of autonomy? and (4) How is autonomy software rendered resilient to errors? This project aims to answer these questions through optimization, computational resource management, and software resilience, with evaluation in an outdoor robotic testbed. Finally, the broader impacts of this work include: (1) K-12 academic experiences for underrepresented students in collaboration with Virginia Tech's Center for Enhancement of Engineering Diversity; (2) autonomy curriculum and design projects; and (3) participation in a series of symposiums through the Ridge and Valley chapter of the Association for Unmanned Vehicle Systems International.
This project focuses on the investigation of: (1) a unified theory and scalable algorithms for multi-agent task allocation and motion planning with constraints on run-time resource optimization and software reliability; (2) efficient analysis and optimization techniques for run-time resource optimization in time-critical CPS; (3) lightweight and flexible methodologies for achieving soft error resilience in computational kernels for autonomy; and (4) a heterogeneous multi-agent testbed for target tracking and infrastructure mapping missions. This project will advance knowledge in the largely unexplored areas of computation and reliability-aware task allocation and motion planning, run-time resource optimization, and flexible software reliability. The new unified approach closes the loop for robust task allocation and motion planning as it acts as a fundamental tool to advance scalability, adaptability, resiliency, safety, security, and usability of CPS with provable behaviors in increasingly complex multi-agent missions across dynamic environments.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
