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Shih-Yun Lo, Shiqi Zhang, and Peter
Stone. PETLON: Planning Efficiently for Task-Level Optimal Navigation. In Proceedings of the 17th International
Conference on Autonomous Agents and Multiagent Systems (AAMAS), July 2018.
Winner of the Best Robotics Paper
Award at AAMAS 2018
Intelligent mobile robots have recently become able to operate autonomously in large-scale indoor environments for extended periods of time. Task planning in such environments involves sequencing the robot's high-level goals and subgoals, and typically requires reasoning about the locations of people, rooms, and objects in the environment, and their interactions to achieve a goal. One of the prerequisites for optimal task planning that is often overlooked is having an accurate estimate of the actual distance (or time) a robot needs to navigate from one location to another. State-of-the-art motion planners, though often computationally complex, are designed exactly for this purpose of finding routes through constrained spaces. In this work, we focus on integrating task and motion planning (TMP) to achieve task-level optimal planning for robot navigation while maintaining manageable computational efficiency. To this end, we introduce TMP algorithm PETLON (Planning Efficiently for Task-Level-Optimal Navigation) for everyday service tasks using a mobile robot. PETLON is more efficient than planning approaches that pre-compute motion costs of all possible navigation actions, while still producing plans that are optimal at the task level.
@InProceedings{AAMAS18-yunl,
title={{PETLON}: Planning Efficiently for Task-Level Optimal Navigation},
author={Shih-Yun Lo and Shiqi Zhang and Peter Stone},
booktitle = {Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
location = {Stockholm, Sweden},
month = {July},
year = {2018},
abstract = {
Intelligent mobile robots have recently become able to operate
autonomously in large-scale indoor environments for extended periods of
time. Task planning in such
environments involves sequencing the robot's high-level goals and subgoals,
and typically requires reasoning about the locations of people, rooms, and
objects in the environment, and their interactions to achieve a goal.
One of the prerequisites for optimal task planning that is often
overlooked is having an accurate estimate of the
actual distance (or time) a robot needs to navigate from one location to another.
State-of-the-art motion planners, though often
computationally complex, are designed exactly for this
purpose of finding routes through constrained spaces.
In this work, we focus on integrating
task and motion planning (TMP) to achieve task-level optimal planning for robot
navigation while maintaining manageable computational efficiency.
To this end, we introduce TMP algorithm PETLON (Planning Efficiently for Task-Level-Optimal Navigation) for everyday service tasks
using a mobile robot. PETLON is more efficient than planning approaches
that pre-compute motion costs of all possible navigation actions, while still
producing plans that are optimal at the task level.
},
wwwnote={Winner of the <b>Best Robotics Paper Award</b> at <a href="http://celweb.vuse.vanderbilt.edu/aamas18/">AAMAS 2018</a>},
}
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