PeRL STUDIES AUTONOMOUS NAVIGATION & MAPPING FOR MOBILE ROBOTS IN A PRIORI UNKNOWN ENVIRONMENTS.

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Theses

MPDM: Multi-policy Decision-Making from Autonomous Driving to Social Robot Navigation

Summary


. Alex G. Cunningham, Enric Galceran, Dhanvin Mehta, Gonzalo Ferrer, Ryan M. Eustice and Edwin Olson, in MPDM: Multi-policy Decision-Making from Autonomous Driving to Social Robot Navigation. Springer International Publishing, 2019. (In Press).

Abstract

This chapter presents multi-policy decision-making (MPDM): a novel approach to navigating in dynamic multi-agent environments. Rather than planning the trajectory of the robot explicitly, the planning process selects one of a set of closed-loop behaviors whose utility can be predicted through forward simulation that captures the complex interactions between the actions of these agents. These polices capture different high-level behavior and intentions, such as driving along a lane, turning at an intersection, or following pedestrians. We present two different scenarios where MPDM has been applied successfully: an autonomous driving environment models vehicle behavior for both our vehicle and nearby vehicles and a social environment, where multiple agents or pedestrians configure a dynamic environment for autonomous robot navigation. We present extensive validation for MPDM on both scenarios, using simulated and real-world experiments.

Bibtex entry

@INBOOK { acunningham-2019a,
    AUTHOR = { Alex G. Cunningham and Enric Galceran and Dhanvin Mehta and Gonzalo Ferrer and Ryan M. Eustice and Edwin Olson },
    EDITOR = { Harald Waschl and Ilya Kolmanovsky and Frank Willems },
    TITLE = { {MPDM}: Multi-policy Decision-Making from Autonomous Driving to Social Robot Navigation },
    BOOKTITLE = { Control Strategies for Advanced Driver Assistance Systems and Autonomous Driving Functions : Development, Testing and Verification },
    PUBLISHER = { Springer International Publishing },
    YEAR = { 2019 },
    PAGES = { 201--223 },
    NOTE = { In Press },
}