Award Abstract #1454737
CAREER: Driving the Future: Models and Control Methods to Coordinate Fleets of Self-Driving Vehicles in Future Transportation Networks

NSF Org:	CMMI Div Of Civil, Mechanical, & Manufact Inn
Initial Amendment Date:	January 8, 2015
Latest Amendment Date:	January 8, 2015
Award Number:	1454737
Award Instrument:	Standard Grant
Program Manager:	Massimo Ruzzene CMMI Div Of Civil, Mechanical, & Manufact Inn ENG Directorate For Engineering
Start Date:	February 1, 2015
End Date:	January 31, 2020 (Estimated)
Awarded Amount to Date:	$500,000.00
Investigator(s):	Marco Pavone pavone@stanford.edu (Principal Investigator)
Sponsor:	Stanford University 3160 Porter Drive Palo Alto, CA 94304-1212 (650)723-2300
NSF Program(s):	Dynamics, Control and System D
Program Reference Code(s):	034E, 035E, 1045, 8024
Program Element Code(s):	7569

ABSTRACT

This Faculty Early Career Development (CAREER) Program project advances scientific knowledge on the modeling, analysis, and control of robotic networks consisting of unmanned vehicles autonomously operating in a coordinated fashion to fulfill service requests such as the transportation of people or goods. To work efficiently, such systems must overcome allocation and scheduling challenges that, in practice, can create backups, unacceptable wait times, and detrimental cascade effects. This project will cast the problem within the framework of spatial queuing theory, and investigate theoretical models and real-time control methods to optimally allocate vehicles to service requests. Theory and control algorithms will be applied for the design, system-wide control, and economic assessment of autonomous mobility-on-demand systems. Such systems represent a transformative, rapidly developing mode of transportation where electric, self-driving shuttles transport urban passengers and provide a mobility option to people unable or unwilling to drive. The results of this project will benefit the U.S. economy by fostering clean and efficient future transportation systems and addressing 21st century mobility needs. More broadly, this research is applicable to a large class of robotic coordination problems and will positively impact several critical sectors including automated supply chains and logistics and national security. Experiments on full-scale autonomous shuttles will help broaden the participation of underrepresented groups in research and catalyze engineering education on cyber-physical systems.

Current methods for controlling robotic networks are limited, particularly with respect to predictive accuracy and control synthesis with formal performance guarantees. Spatial queuing theory considers dynamic systems consisting of (i) spatially-localized queues that collect service requests generated by an exogenous dynamical process, and (ii) robotic service vehicles traveling among queues in a given network topology. As such, spatial queuing theory models a large variety of robotic coordination problems, with autonomous mobility-on-demand systems as a relevant example. The project will advance knowledge in the field by leveraging recent algorithmic techniques from stochastic network optimization to generate provably-correct tools for the modeling, analysis, and control of spatial queuing systems of increasing complexity and realism. Specifically, this award supports fundamental research to 1) advance the theory of spatial queuing systems, by devising methods for tractable analyses in complex setups, 2) generate control methods with performance guarantees for the optimal assignment of robotic vehicles to service requests, and 3) apply theory and control methods to the control of autonomous mobility-on-demand systems, through case studies and the deployment of algorithms on full scale test beds.

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