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Deep R-Learning for Continual Area Sweeping.
Rishi Shah, Yuqian Jiang, Justin
Hart, and Peter Stone.
In Proceedings of the IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS 2020), October 2020.
1-minute
video demonstration; 13-minute Video
presentation.
[PDF]374.2kB [slides.pdf]1.1MB
Coverage path planning is a well-studied problem in robotics in which a robot must plan a path that passes through every point in a given area repeatedly, usually with a uniform frequency. To address the scenario in which some points need to be visited more frequently than others, this problem has been extended to non-uniform coverage planning. This paper considers the variant of non-uniform coverage in which the robot does not know the distribution of relevant events beforehand and must nevertheless learn to maximize the rate of detecting events of interest. This continual area sweeping problem has been previously formalized in a way that makes strong assumptions about the environment, and to date only a greedy approach has been proposed. We generalize the continual area sweeping formulation to include fewer environmental constraints, and propose a novel approach based on reinforcement learning in a Semi-Markov Decision Process. This approach is evaluated in an abstract simulation and in a high fidelity Gazebo simulation. These evaluations show significant improvement upon the existing approach in general settings, which is especially relevant in the growing area of service robotics. We also present a video demonstration on a real service robot.
@InProceedings{IROS20-shah,
author = {Rishi Shah and Yuqian Jiang and Justin Hart and Peter Stone},
title = {Deep R-Learning for Continual Area Sweeping},
booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020)},
location = {Las Vegas, NV, USA},
month = {October},
year = {2020},
abstract = {
Coverage path planning is a well-studied problem in robotics in which a robot must plan a path that passes through every point in a given area repeatedly, usually with a uniform frequency. To address the scenario in which some points need to be visited more frequently than others, this problem has been extended to non-uniform coverage planning. This paper considers the variant of non-uniform coverage in which the robot does not know the distribution of relevant events beforehand and must nevertheless learn to maximize the rate of detecting events of interest. This continual area sweeping problem has been previously formalized in a way that makes strong assumptions about the environment, and to date only a greedy approach has been proposed. We generalize the continual area sweeping formulation to include fewer environmental constraints, and propose a novel approach based on reinforcement learning in a Semi-Markov Decision Process. This approach is evaluated in an abstract simulation and in a high fidelity Gazebo simulation. These evaluations show significant improvement upon the existing approach in general settings, which is especially relevant in the growing area of service robotics. We also present a video demonstration on a real service robot.
},
wwwnote={<a href="https://youtu.be/KqEwfs1xqdw">1-minute video demonstration</a>; <a href="https://drive.google.com/file/d/1tZBNq27rWWxeWowH608meei-shsymSyw/view">13-minute Video presentation</a>.}
}
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