Real-time robot path planning from simple to complex obstacle patterns via transfer learning of options
We consider the problem of path planning in an initially unknown environment where a robot does not have an a priori map of its environment but has access to prior information accumulated by itself from navigation in similar but not identical environments. To address the navigation problem, we propo...
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Published in | Autonomous robots Vol. 43; no. 8; pp. 2071 - 2093 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
New York
Springer US
01.12.2019
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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Summary: | We consider the problem of path planning in an initially unknown environment where a robot does not have an
a priori
map of its environment but has access to prior information accumulated by itself from navigation in similar but not identical environments. To address the navigation problem, we propose a novel, machine learning-based algorithm called
Semi-Markov Decision Process with Unawareness and Transfer (SMDPU-T)
where a robot records a sequence of its actions around obstacles as action sequences called
options
which are then reused by it within a framework called
Markov Decision Process with unawareness (MDPU)
to learn suitable, collision-free maneuvers around more complex obstacles in future. We have analytically derived the cost bounds of the selected option by SMDPU-T and the worst case time complexity of our algorithm. Our experimental results on simulated robots within Webots simulator illustrate that SMDPU-T takes
24
%
planning time and
39
%
total time to solve same navigation tasks while, our hardware results on a Turtlebot robot indicate that SMDPU-T on average takes
53
%
planning time and
60
%
total time as compared to a recent, sampling-based path planner. |
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ISSN: | 0929-5593 1573-7527 |
DOI: | 10.1007/s10514-019-09852-5 |