Multi-Robot Persistent Coverage with stochastic task costs
Derek Mitchell, Nilanjan Chakraborty, Katia Sycara, Nathan Michael
Abstract
We propose the Stochastic Multi-Robot Persistent Coverage Problem (SMRPCP) and correspondant methodology to compute an optimal schedule that enables a fleet of energy-constrained 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.
BibTeX
@inproceedings{iros2015_multirobotpersis,
title = {Multi-Robot Persistent Coverage with stochastic task costs},
author = {Derek Mitchell and Nilanjan Chakraborty and Katia Sycara and Nathan Michael},
booktitle = {IROS 2015},
year = {2015}
}