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Multirobot Symbolic Planning under Temporal Uncertainty.
Shiqi Zhang,
Yuqian Jiang, Guni Sharon, and Peter
Stone.
In Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Sytems (AAMAS),
May 2017.
Accompanying videos at https://youtu.be/ADbH3sppLHQ
Multirobot symbolic planning (MSP) aims at computing plans, each in the form of a sequence of actions, for a team of robots to achieve their individual goals while minimizing overall cost. Solving MSP problems requires modeling limited domain resources (e.g., corridors that allow at most one robot at a time) and the possibility of action synergy (e.g., multiple robots going through a door after a single door-opening action). However, the temporal uncertainty that propagates over actions, such as delays caused by obstacles in navigation actions, makes it challenging to plan for resource sharing and realizing synergy in a team of robots. This paper, for the first time, introduces the problem of MSP under temporal uncertainty (MSPTU).We present a novel, iterative inter-dependent planning (IIDP) algorithm, including two configurations (simple and enhanced), for solving general MSPTU problems. We then focus on multirobot navigation tasks, presenting a full instantiation of IIDP that includes a new algorithm for computing conditional plan cost under temporal uncertainty and a novel shifted-Poisson distribution for accumulating temporal uncertainty over actions. The algorithms have been implemented both in simulation and on real robots. We observed a significant reduction in overall cost compared to baselines in which robots do not communicate or model temporal uncertainty.
@InProceedings{AAMAS17-Zhang,
author = {Shiqi Zhang and Yuqian Jiang and Guni Sharon and Peter Stone},
title = {Multirobot Symbolic Planning under Temporal Uncertainty},
booktitle = {Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Sytems (AAMAS)},
location = {Sao Paulo, Brazil},
month = {May},
year = {2017},
abstract = {
Multirobot symbolic planning (MSP) aims at computing plans, each in the
form of a sequence of actions, for a team of robots to achieve their
individual goals while minimizing overall cost. Solving MSP problems
requires modeling limited domain resources (e.g., corridors that allow at
most one robot at a time) and the possibility of action synergy
(e.g., multiple robots going through a door after a single door-opening
action). However, the temporal uncertainty that propagates over actions,
such as delays caused by obstacles in navigation actions, makes it
challenging to plan for resource sharing and realizing synergy in a team
of robots. This paper, for the first time, introduces the problem of MSP
under temporal uncertainty (MSPTU).We present a novel, iterative
inter-dependent planning (IIDP) algorithm, including two configurations
(simple and enhanced), for solving general MSPTU problems. We then focus
on multirobot navigation tasks, presenting a full instantiation of IIDP
that includes a new algorithm for computing conditional plan cost under
temporal uncertainty and a novel shifted-Poisson distribution for
accumulating temporal uncertainty over actions. The algorithms have been
implemented both in simulation and on real robots. We observed a
significant reduction in overall cost compared to baselines in which
robots do not communicate or model temporal uncertainty.
},
wwwnote={Accompanying videos at <a href="https://youtu.be/ADbH3sppLHQ">https://youtu.be/ADbH3sppLHQ</a>},
}
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