2020
Ramirez-Atencia, Cristian; Rodriguez-Fernandez, Victor; Camacho, David
A revision on multi-criteria decision making methods for multi-UAV mission planning support Journal Article
In: Expert Systems with Applications, vol. 160, pp. 113708, 2020, ISSN: 0957-4174.
Abstract | Links | BibTeX | Tags: Decision Making, Fuzzy methods, Planning, Unmanned Aircraft Systems
@article{RAMIREZATENCIA2020113708,
title = {A revision on multi-criteria decision making methods for multi-UAV mission planning support},
author = {Cristian Ramirez-Atencia and Victor Rodriguez-Fernandez and David Camacho},
url = {https://www.sciencedirect.com/science/article/pii/S0957417420305327},
doi = {https://doi.org/10.1016/j.eswa.2020.113708},
issn = {0957-4174},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Expert Systems with Applications},
volume = {160},
pages = {113708},
abstract = {Over the last decade, Unmanned Aerial Vehicles (UAVs) have been extensively used in many commercial applications due to their manageability and risk avoidance. One of the main problems considered is the mission planning for multiple UAVs, where a solution plan must be found satisfying the different constraints of the problem. This problem has multiple variables that must be optimized simultaneously, such as the makespan, the cost of the mission or the risk. Therefore, the problem has a lot of possible optimal solutions, and the operator must select the final solution to be executed among them. In order to reduce the workload of the operator in this decision process, a Decision Support System (DSS) becomes necessary. In this work, a DSS consisting of ranking and filtering systems, which order and reduce the optimal solutions, has been designed. With regard to the ranking system, a wide range of Multi-Criteria Decision Making (MCDM) methods, including some fuzzy MCDM, are compared on a multi-UAV mission planning scenario, in order to study which method could fit better in a multi-UAV decision support system. Expert operators have evaluated the solutions returned, and the results show, on the one hand, that fuzzy methods generally achieve better average scores, and on the other, that all of the tested methods perform better when the preferences of the operators are biased towards a specific variable, and worse when their preferences are balanced. For the filtering system, a similarity function based on the proximity of the solutions has been designed, and on top of that, a threshold is tuned empirically to decide how to filter solutions without losing much of the hypervolume of the space of solutions.},
keywords = {Decision Making, Fuzzy methods, Planning, Unmanned Aircraft Systems},
pubstate = {published},
tppubtype = {article}
}
Over the last decade, Unmanned Aerial Vehicles (UAVs) have been extensively used in many commercial applications due to their manageability and risk avoidance. One of the main problems considered is the mission planning for multiple UAVs, where a solution plan must be found satisfying the different constraints of the problem. This problem has multiple variables that must be optimized simultaneously, such as the makespan, the cost of the mission or the risk. Therefore, the problem has a lot of possible optimal solutions, and the operator must select the final solution to be executed among them. In order to reduce the workload of the operator in this decision process, a Decision Support System (DSS) becomes necessary. In this work, a DSS consisting of ranking and filtering systems, which order and reduce the optimal solutions, has been designed. With regard to the ranking system, a wide range of Multi-Criteria Decision Making (MCDM) methods, including some fuzzy MCDM, are compared on a multi-UAV mission planning scenario, in order to study which method could fit better in a multi-UAV decision support system. Expert operators have evaluated the solutions returned, and the results show, on the one hand, that fuzzy methods generally achieve better average scores, and on the other, that all of the tested methods perform better when the preferences of the operators are biased towards a specific variable, and worse when their preferences are balanced. For the filtering system, a similarity function based on the proximity of the solutions has been designed, and on top of that, a threshold is tuned empirically to decide how to filter solutions without losing much of the hypervolume of the space of solutions.