NSF Award
2209795
This project augments the Chrono computer simulation platform in transformative ways. Chrono's purpose is to predict through simulation the interplay between mechatronic systems, the environment they operate in, and humans with whom they might interact. The open-source simulation platform is slated to become a community-shared virtual investigation tool used to probe competing engineering designs and test hypotheses that would be too dangerous, difficult, or costly to verify through physical experiments. Chrono has been and will continue to be used in multiple fields and disciplines, e.g., terramechanics, astrophysics; soft matter physics; biomechanics; mechanical engineering; civil engineering; industrial engineering; and computer science. Specifically, it is used to engineer the 2023 VIPER lunar rover; relied upon by US Army experts in evaluating its wheeled and tracked vehicle designs; used in the US and Germany in the wind turbine industry; and involved in designing wave energy conversion solutions in Europe. Upon project completion, Chrono will become a simulation engine in Gazebo, which is widely used in robotics research; operate on the largest driving simulator in the US; empower research in the bio-robotics and field-robotics communities; and assist efforts in the broad area of automotive research carried out by a consortium of universities and companies under the umbrella of the Automotive Research Center. The educational impact of this project is threefold: training undergraduate, graduate, and post-doctoral students in a multi-disciplinary fashion that emphasizes advanced computing skills development; anchoring two new courses in autonomous vehicle control and simulation in robotics; and broadening participation in computing through a residential program on the campus of the University of Wisconsin-Madison that engages teachers and students from rural high-schools. <br/><br/>Innovation and discovery are fueled by quality data. At its core, this project seeks to increase the share of this data that has simulation as its provenance. In this context, a multi-disciplinary team of 40 researchers augments and validates a physics-based simulation framework that empowers research in autonomous agents (AAs). The AAs operate in complex and unstructured dynamic environments and might engage in two-way interaction with humans or other AAs. This project enables Chrono to generate machine learning training data quickly and inexpensively; facilitates comparison of competing designs for assessing trade-offs; and gauges candidate design robustness via testing in simulation of corner-case scenarios. These tasks are accomplished by upgrading and extending Chrono to leverage recent computational dynamics innovations, e.g., a faster index 3 differential algebraic equations solver; a new approach to solving frictional contact problems; a real-time solver for handling flexible-body dynamics in soft robotics via nonlinear finite element analysis; a best-in-class simulator for terradynamics applications; reliance on just-in-time compiling for producing executables that are both problem- and hardware-optimized; a novel way for using mixed data representations for parsimonious storing of state information; and a scalable multi-agent framework that enables geographically-distributed, over the Internet, real-time simulation of human-AA interaction.<br/><br/>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.
