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

Alex G. Cunningham, Enric Galceran, Dhanvin Mehta, Gonzalo Ferrer, Ryan M. Eustice, Edwin Olson

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

8 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationLecture Notes in Control and Information Sciences
PublisherSpringer Verlag
Pages201-223
Number of pages23
DOIs
Publication statusPublished - 2019
Externally publishedYes

Publication series

NameLecture Notes in Control and Information Sciences
Volume476
ISSN (Print)0170-8643

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