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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2015 Vol. 5(2): 86-90 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.488

Cleaning Task Planning for an Autonomous Robot in Indoor Places with Multiples Rooms

Paulo Pinheiro, Eleri Cardozo, Jacques Wainer, and Eric Rohmer
Abstract—This paper presents a mobile robot planning approach for solving the problem of indoor cleaning tasks. In general, the robot must find its pose first and only then move to the final destination to cleaning out. Our model works with a multi-level planning approach where the mission is treated online only. The user sets up the cleaning or the robot uses an agenda with predefined set of missions. POMDP plans are created for the localization, using the map and the robot specifications. The plans are created offline only once and used indefinitely regardless of missions. We will show the multi-level planning process where the robot finds its pose in the high level of representative rooms and then moves to the lower level to finding its precise pose. We demonstrated the approach with experiments on both simulator and real robot. The multi-level planning allowed the robot to find its pose and fulfill the tasks of the agenda faster while keeping the precision.

Index Terms—Cleaning robot tasking, localization, robotic planning, POMDP.

P. Pinheiro, E. Cardozo, and E. Rohmer are with the School of Electrical and Computer Engineering, University of Campinas, SP 6101 Brazil (e-mail: pinheiro@ic.unicamp.br, eleri@dca.fee.unicamp.br, erohmer@dca.fee.unicamp.br).
J. Wainer is with the Institute of Computing, University of Campinas, SP 13083-852 Brazil (e-mail: wainer@ic.unicamp.br).

[PDF]

Cite: Paulo Pinheiro, Eleri Cardozo, Jacques Wainer, and Eric Rohmer, "Cleaning Task Planning for an Autonomous Robot in Indoor Places with Multiples Rooms," International Journal of Machine Learning and Computing vol. 5, no. 2, pp. 86-90, 2015.

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