This National Robotics Initiative project will promote the progress of science, advance the national prosperity and welfare, and secure the national defense by advancing multi-robot team abilities to assist in disaster evacuation and crowd control to protect humans, minimize casualty and economic damage. To achieve this goal, the team will develop hardware and software for distributed estimation, control, and machine learning for networked multi-robot shepherding. The developed algorithms will have broad applicability in a variety of other applications, including cloud robotics, machine learning, and automated fulfillment. This project will produce one hundred low-cost wheeled mobile robots to experimentally test the algorithms on a large-scale flock of robots. The ?shepherd? and ?sheep? robots will be capable of being teleoperated, allowing humans to participate in heterogeneous teleoperated-autonomous teams. The algorithms developed in this project will be tested in collaboration with the Army Research Lab (ARL), increasing the likelihood of transition of the theory to near-term application. Undergraduate and graduate students will be supported with this grant, who will also have opportunity to demonstrate their work to thousands of school children each year at the Chicago Museum of Science and Industry.
To achieve effective shepherding, this project will develop novel algorithms for distributed estimation of the shape of the herded flock, distributed optimal control of the shape of the flock, and machine learning of the dynamics of the flock. These algorithms will be supplemented by a rigorous analysis of the stability of the interacting estimation, learning, and control laws. The ambitious objectives of this work require fundamental advances in several related areas, including distributed optimization and estimation; distributed machine learning; stability of coupled learning and control systems; network controllability and observability. To achieve these breakthroughs, the research will build on recent results by the team of PIs on passivity theory for coupled distributed estimators and controllers, flexible first order distributed optimization algorithms. The experimental validation and human-interaction studies require a new generation of teleoperable swarm robots. Taking advantage of the latest generation of low-cost high-performance hardware, this project will create a flock of one hundred small but highly capable wheeled robots, at a price point an order of magnitude less expensive than any comparable commercially available swarm robot.
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.
