Carnegie Mellon University
The
Multi-robot Persistent Coverage with Stochastic Task Costs

Derek Mitchell, Nilanjan Chakraborty, Katia Sycara, and Nathan Michael
International Conference on Intelligent Robots and Systems (IROS), September, 2015.


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Abstract
We propose the Stochastic Multi-Robot Persistent Coverage Problem (SMRPCP) and correspondant methodology to compute an optimal schedule that enables a fleet of energyconstrained unmanned aerial vehicles to repeatedly perform a set of tasks while maximizing the frequency of task completion and preserving energy reserves via recharging depots. The approach enables online modeling of uncertain task costs and yields a schedule that adapts according to an evolving energy expenditure model. A fast heuristic method is formulated that enables online generation of a schedule that concurrently maximizes task completion frequency and avoids the risk of individual robot energy-depletion and consequential platform failure. Failure mitigation is introduced through a recourse strategy that routes robots based on acceptable levels of risk. Simulation and experimental results evaluate the efficacy of the proposed methodology and demonstrate online system-level adaptation due to increasingly certain costs models acquired during the deployment execution.

Notes

Text Reference
Derek Mitchell, Nilanjan Chakraborty, Katia Sycara, and Nathan Michael, "Multi-robot Persistent Coverage with Stochastic Task Costs," International Conference on Intelligent Robots and Systems (IROS), September, 2015.

BibTeX Reference
@inproceedings{Mitchell_2015_8176,
   author = "Derek Mitchell and Nilanjan Chakraborty and Katia Sycara and Nathan Michael",
   title = "Multi-robot Persistent Coverage with Stochastic Task Costs",
   booktitle = "International Conference on Intelligent Robots and Systems (IROS)",
   month = "September",
   year = "2015",
}