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Piyush Khandelwal, Samuel Barrett, and Peter Stone. Leading the Way: An Efficient Multi-robot Guidance System. In International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2015.
Recent advances in service robotics have made it possible to deploy a large number of mobile robots in indoor environments to perform tasks such as delivery, maintenance and eldercare. If a centrally connected multi-robot system is available, can it be effectively used to aid humans in other on-demand tasks? In this paper, we demonstrate how individual service robots in a multi-robot system can be temporarily reassigned from their original task to help guide a human from one location to another in the environment. We formulate this multi-robot treatment of the human guidance problem as a Markov Decision Process (MDP). Solving the MDP produces a policy to efficiently guide the human, but the state space size makes it infeasible to optimally solve it. Instead, we use the Upper Confidence bound for Trees (UCT) planner to obtain an approximate solution. We show that this solution outperforms an approach that uses a single robot to guide the human from start to finish.
@InProceedings{AAMAS15-khandelwal,
author = {Piyush Khandelwal and Samuel Barrett and Peter Stone},
title = {Leading the Way: An Efficient Multi-robot Guidance System},
booktitle = {International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
location = {Istanbul, Turkey},
month = {May},
year = {2015},
abstract = {
Recent advances in service robotics have made it possible to deploy a large
number of mobile robots in indoor environments to perform tasks such as
delivery, maintenance and eldercare. If a centrally connected multi-robot
system is available, can it be effectively used to aid humans in other
on-demand tasks? In this paper, we demonstrate how individual service
robots in a multi-robot system can be temporarily reassigned from their
original task to help guide a human from one location to another in the
environment. We formulate this multi-robot treatment of the human guidance
problem as a Markov Decision Process (MDP). Solving the MDP produces a
policy to efficiently guide the human, but the state space size makes it
infeasible to optimally solve it. Instead, we use the Upper Confidence
bound for Trees (UCT) planner to obtain an approximate solution. We show
that this solution outperforms an approach that uses a single robot to
guide the human from start to finish.
},
}
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