Abstract
This paper presents a mathematical formulation of the problem of cleaning and delivery task in a large public space with multiple robots, along with a procedural solution based on task reallocation. The task in the cleaning problem is the cleaning zone. A group of robots are assigned to each cleaning zones according to the environmental parameters. Resource constraints make cleaning robots stop operation periodically, which can incur a mission failure or deterioration of the mission performance. In our solution approach, continuous operation is assured by replacing robots having resource problems with standby robots by task reallocation. Two resource constraints are considered in our formulation: the battery capacity and the garbage bin size. This paper describes and compares the performance of three task reallocation strategies: All-At-Once, Optimal-Vector, and Performance-Maximization. The performance measures include remaining garbage volume, cleaning quality, and cleaning time. Task allocation algorithms are tested by simulation in an area composed of 4 cleaning zones, and the Performance-Maximization strategy marked the best performance. Hence, a delivery task is added to the cleaning task. The delivery request operates as a new perturbation factor for the reallocation. The task allocation procedure for the delivery task includes the switching of tasks of the delivery robot itself as well as exchanging among cleaning robots to meet the balance of the cleaning performance. The experiment was conducted with 9 robots with the software architecture that enables multi-functional of a robot and they performed both pseudo-clean and delivery task successfully.
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Acknowledgments
This work was supported by the Robot R&D program of MOTIE/KEIT. [10051155, The Development of Robot Based Logistics Systems Applicable to Hospital-wide Environment]
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Jeon, S., Jang, M., Lee, D., Cho, YJ., Kim, J., Lee, J. (2016). Multiple Robots Task Allocation for Cleaning and Delivery. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. Studies in Computational Intelligence, vol 650. Springer, Cham. https://doi.org/10.1007/978-3-319-33386-1_10
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DOI: https://doi.org/10.1007/978-3-319-33386-1_10
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