Learning local behavioral sequences to better infer non-local properties in real multi-robot systems

Taeyeong Choi, Sehyeok Kang, Theodore P. Pavlic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

When members of a multi-robot team follow regular motion rules sensitive to robots and other environmental factors within sensing range, the team itself may become an informational fabric for gaining situational awareness without explicit signalling among robots. In our previous work [1], we used machine learning to develop a scalable module, trained only on data from 3-robot teams, that could predict the positions of all robots in larger multi-robot teams based only on observations of the movement of a robot's nearest neighbor. Not only was this approach scalable from 3-to-many robots, but it did not require knowledge of the control laws of the robots under observation, as would a traditional observer-based approach. However, performance was only tested in simulation and could only be a substitute for explicit communication for short periods of time or in cases of very low sensing noise. In this work, we apply more sophisticated machine learning methods to data from a physically realized robotic team to develop Remote Teammate Localization (ReTLo) modules that can be used in realistic environments. To be specific, we adopt Long-Short-Term-Memory (LSTM) [2] to learn the evolution of behaviors in a modular team, which has the effect of greatly reducing errors from regression outcomes. In contrast with our previous work in simulation, all of the experiments conducted in this work were conducted on the Thymio physical, two-wheeled robotic platform.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Robotics and Automation, ICRA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2138-2144
Number of pages7
ISBN (Electronic)9781728173955
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Robotics and Automation, ICRA 2020 - Paris, France
Duration: May 31 2020Aug 31 2020

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2020 IEEE International Conference on Robotics and Automation, ICRA 2020
Country/TerritoryFrance
CityParis
Period5/31/208/31/20

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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