Robotic load balancing for mobility-on-demand systems
Name
Frazzoli_Robotic load balancing.pdf
Size
1.04 MB
Format
Adobe PDF
Checksum (MD5)
554162a7fe7c3090ced42a700098ca9e
Author(s)
Frazzoli, Emilio
Rus, Daniela L.
Pavone, Marco
Smith, Stephen L.
Date Issued
May 2012
Journal
The International Journal of Robotics Research
Publisher
Sage Publications
Citation
Pavone, M., S. L. Smith, E. Frazzoli, and D. Rus. “Robotic load balancing for mobility-on-demand systems.” The International Journal of Robotics Research 31, no. 7 (May 30, 2012): 839-854.
Version
Author's final manuscript
Abstract
In this paper we develop methods for maximizing the throughput of a mobility-on-demand urban transportation system. We consider a finite group of shared vehicles, located at a set of stations. Users arrive at the stations, pickup vehicles, and drive (or are driven) to their destination station where they drop-off the vehicle. When some origins and destinations are more popular than others, the system will inevitably become out of balance: vehicles will build up at some stations, and become depleted at others. We propose a robotic solution to this rebalancing problem that involves empty robotic vehicles autonomously driving between stations. Specifically, we utilize a fluid model for the customers and vehicles in the system. Then, we develop a rebalancing policy that lets every station reach an equilibrium in which there are excess vehicles and no waiting customers and that minimizes the number of robotic vehicles performing rebalancing trips. We show that the optimal rebalancing policy can be found as the solution to a linear program. We use this solution to develop a real-time rebalancing policy which can operate in highly variable environments. Finally, we verify policy performance in a simulated mobility-on-demand environment and in hardware experiments.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1177/0278364912444766
