NDLI: Implicit adaptive multi-robot coordination in dynamic environments

Content Provider	IEEE Xplore Digital Library
Author	Colby, M. Jen Jen Chung Tumer, K.
Copyright Year	2015
Description	Author affiliation: Autonomous Agents & Distrib. Intell. Lab., Oregon State Univ., Corvallis, OR, USA (Colby, M.; Jen Jen Chung; Tumer, K.)
Abstract	Multi-robot teams offer key advantages over single robots in exploration missions by increasing efficiency (explore larger areas), reducing risk (partial mission failure with robot failures), and enabling new data collection modes (multi-modal observations). However, coordinating multiple robots to achieve a system-level task is difficult, particularly if the task may change during the mission. In this work, we demonstrate how multiagent cooperative coevolutionary algorithms can develop successful control policies for dynamic and stochastic multi-robot exploration missions. We find that agents using difference evaluation functions (a technique that quantifies each individual agent's contribution to the team) provides superior system performance (up to 15%) compared to global evaluation functions and a hand-coded algorithm.
Starting Page	5168
Ending Page	5173
File Size	357866
Page Count	6
File Format	PDF
e-ISBN	9781479999941
DOI	10.1109/IROS.2015.7354105
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-28
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robot kinematics Approximation methods Sociology Statistics Robot sensing systems Neural networks
Content Type	Text
Resource Type	Article

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