Receding-horizon multi-objective optimization for disaster response

Kooktae Lee, Sonia Martínez, Jorge Cortés, Robert H. Chen and Mark B. Milan
Proceedings of the 2018 American Control Conference, Milwaukee, WI, USA, June 2018

Abstract:

This paper proposes a receding-horizon, multi-objective optimization approach for robot motion planning in disaster response scenarios. During a search and rescue mission, a robot is deployed in the disaster area to find and egress all victims. In doing so, multiple criteria characterize the effectiveness of such plan. We define three objective functions (performance, uncertainty about victim locations, and uncertainty about the environment) and formulate a multi-objective optimization problem employing a combined weighted-sum and $\epsilon$-constraint method. To handle dynamic scenarios, we employ a receding-horizon approach that allows to dynamically adapt the $\epsilon$ constraint. We illustrate the effectiveness of the proposed method via simulations.

File: main.pdf

Bib-tex entry:

@InProceedings{KL-SM-JC-RHC-MBM:18-acc},
author = {K. Lee and S. Mart{\'\i}nez and J. Cort\'es and R. H. Chen and M. B. Milan},
booktitle = {2018 American Control Conference},
title = {Receding-horizon multi-objective optimization for disaster response},
pages ={5304--5309},
month = {June},
year = {2018},
address ={Milwaukee, WI, USA}
}