Models and Control Methods to Coordinate Fleets of Self-Driving Vehicles in Future Transportation Networks

This project aims to advance 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. We 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.

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.