We study multi-robot caravanning, which is loosely defined as the problem of a heterogeneous team of robots visiting specific areas of an environment (waypoints) as a group. After formally defining this problem, we propose a novel solution that requires minimal communication and scales with the number of waypoints and robots. Our approach restricts explicit communication and coordination to occur only when robots reach waypoints, and relies on implicit coordination when moving between a given pair of waypoints. At the heart of our algorithm is the use of leader election to efficiently exploit the unique environmental knowledge available to each robot in order to plan paths for the group, which makes it general enough to work with robots that have heterogeneous representations of the environment. We implement our approach both in simulation and on a physical platform, and characterize the performance of the approach under various scenarios. We demonstrate that our approach can successfully be used to combine the planning capabilities of different agents. © 2013 IEEE.
|Original language||English (US)|
|Title of host publication||2013 IEEE/RSJ International Conference on Intelligent Robots and Systems|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||8|
|State||Published - Nov 2013|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: This research supported in part by NSF awards CNS-0551685, CCF-0833199, CCF-0830753, IIS-0917266, IIS-0916053, EFRI-1240483, RI-1217991, by NIH NCI R25 CA090301-11, by THECB NHARP award000512-0097-2009, by Chevron, IBM, Intel, Oracle/Sun and by AwardKUS-C1-016-04, made by King Abdullah University of Science and Technology(KAUST).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.