A revision on Multi-Criteria Decision Making methods for Multi-UAV
Mission Planning Support
- URL: http://arxiv.org/abs/2402.18743v1
- Date: Wed, 28 Feb 2024 22:54:08 GMT
- Title: A revision on Multi-Criteria Decision Making methods for Multi-UAV
Mission Planning Support
- Authors: Cristian Ramirez-Atencia and Victor Rodriguez-Fernandez and David
Camacho
- Abstract summary: Unmanned Aerial Vehicles (UAVs) have been extensively used in many commercial applications.
One of the main problems considered is the Mission Planning for multiple UAVs.
A Decision Support System (DSS) has been designed to order and reduce the optimal solutions.
- Score: 4.198865250277024
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- Learning Multiple Initial Solutions to Optimization Problems [52.9380464408756]
Sequentially solving similar optimization problems under strict runtime constraints is essential for many applications.
We propose learning to predict emphmultiple diverse initial solutions given parameters that define the problem instance.
We find significant and consistent improvement with our method across all evaluation settings and demonstrate that it efficiently scales with the number of initial solutions required.
arXiv Detail & Related papers (2024-11-04T15:17:19Z) - AQA: Adaptive Question Answering in a Society of LLMs via Contextual Multi-Armed Bandit [59.10281630985958]
In question answering (QA), different questions can be effectively addressed with different answering strategies.
We develop a dynamic method that adaptively selects the most suitable QA strategy for each question.
Our experiments show that the proposed solution is viable for adaptive orchestration of a QA system with multiple modules.
arXiv Detail & Related papers (2024-09-20T12:28:18Z) - Weighted strategies to guide a multi-objective evolutionary algorithm
for multi-UAV mission planning [12.97430155510359]
This work proposes a weighted random generator for the creation and mutation of new individuals.
The main objective of this work is to reduce the convergence rate of the MOEA solver for multi-UAV mission planning.
arXiv Detail & Related papers (2024-02-28T23:05:27Z) - Solving Complex Multi-UAV Mission Planning Problems using
Multi-objective Genetic Algorithms [4.198865250277024]
This paper presents a new Multi-Objective Genetic Algorithm for solving complex Mission Planning Problems (MPP)
A hybrid fitness function has been designed using a Constraint Satisfaction Problem (CSP) to check if solutions are valid.
Experimental results show that the new algorithm is able to obtain good solutions, however as the problem becomes more complex, the optimal solutions also become harder to find.
arXiv Detail & Related papers (2024-02-09T16:13:21Z) - Uncertainty-Aware Search Framework for Multi-Objective Bayesian
Optimization [40.40632890861706]
We consider the problem of multi-objective (MO) blackbox optimization using expensive function evaluations.
We propose a novel uncertainty-aware search framework referred to as USeMO to efficiently select the sequence of inputs for evaluation.
arXiv Detail & Related papers (2022-04-12T16:50:48Z) - Learning Proximal Operators to Discover Multiple Optima [66.98045013486794]
We present an end-to-end method to learn the proximal operator across non-family problems.
We show that for weakly-ized objectives and under mild conditions, the method converges globally.
arXiv Detail & Related papers (2022-01-28T05:53:28Z) - Discovering Diverse Solutions in Deep Reinforcement Learning [84.45686627019408]
Reinforcement learning algorithms are typically limited to learning a single solution of a specified task.
We propose an RL method that can learn infinitely many solutions by training a policy conditioned on a continuous or discrete low-dimensional latent variable.
arXiv Detail & Related papers (2021-03-12T04:54:31Z) - Uncertainty aware Search Framework for Multi-Objective Bayesian
Optimization with Constraints [44.25245545568633]
We consider the problem of constrained multi-objective (MO) blackbox optimization using expensive function evaluations.
We propose a novel framework named Uncertainty-aware Search framework for Multi-Objective Optimization with Constraints.
We show that USeMOC is able to achieve more than 90 % reduction in the number of simulations needed to uncover optimized circuits.
arXiv Detail & Related papers (2020-08-16T23:34:09Z) - Follow the bisector: a simple method for multi-objective optimization [65.83318707752385]
We consider optimization problems, where multiple differentiable losses have to be minimized.
The presented method computes descent direction in every iteration to guarantee equal relative decrease of objective functions.
arXiv Detail & Related papers (2020-07-14T09:50:33Z) - Learning What to Defer for Maximum Independent Sets [84.00112106334655]
We propose a novel DRL scheme, coined learning what to defer (LwD), where the agent adaptively shrinks or stretch the number of stages by learning to distribute the element-wise decisions of the solution at each stage.
We apply the proposed framework to the maximum independent set (MIS) problem, and demonstrate its significant improvement over the current state-of-the-art DRL scheme.
arXiv Detail & Related papers (2020-06-17T02:19:31Z) - sKPNSGA-II: Knee point based MOEA with self-adaptive angle for Mission
Planning Problems [2.191505742658975]
Some problems have many objectives which lead to a large number of non-dominated solutions.
This paper presents a new algorithm that has been designed to obtain the most significant solutions.
This new algorithm has been applied to the real world application in Unmanned Air Vehicle (UAV) Mission Planning Problem.
arXiv Detail & Related papers (2020-02-20T17:07:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.