Constrained multi-objective optimization for multi-UAV planning
- URL: http://arxiv.org/abs/2402.06568v1
- Date: Fri, 9 Feb 2024 17:39:02 GMT
- Title: Constrained multi-objective optimization for multi-UAV planning
- Authors: Cristian Ramirez-Atencia, David Camacho
- Abstract summary: In this work, this problem has been solved using a multi-objective evolutionary algorithm combined with a constraint satisfaction problem model.
The algorithm has been tested on several missions of increasing complexity, and the computational complexity of the different element considered in the missions has been studied.
- Score: 5.574995936464475
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Over the last decade, developments in unmanned aerial vehicles (UAVs) has
greatly increased, and they are being used in many fields including
surveillance, crisis management or automated mission planning. This last field
implies the search of plans for missions with multiple tasks, UAVs and ground
control stations; and the optimization of several objectives, including
makespan, fuel consumption or cost, among others. In this work, this problem
has been solved using a multi-objective evolutionary algorithm combined with a
constraint satisfaction problem model, which is used in the fitness function of
the algorithm. The algorithm has been tested on several missions of increasing
complexity, and the computational complexity of the different element
considered in the missions has been studied.
Related papers
- Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning [52.64813150003228]
The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring.
In the upcoming six-generation (6G) era, UAV-assisted mobile edge computing (MEC) is particularly crucial in challenging environments such as mountainous or disaster-stricken areas.
The task offloading problem is one of the key issues in UAV-assisted MEC, primarily addressing the trade-off between minimizing the task delay and the energy consumption of the UAV.
arXiv Detail & Related papers (2025-01-11T02:32:42Z) - Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks [60.085771314013044]
Low-altitude economy holds significant potential for development in areas such as communication and sensing.
We propose a Clustering-based Multi-agent Deep Deterministic Policy Gradient (CMADDPG) algorithm to address the multi-UAV cooperative task scheduling challenges in SAGIN.
arXiv Detail & Related papers (2024-12-14T06:17:33Z) - UAV-enabled Collaborative Beamforming via Multi-Agent Deep Reinforcement Learning [79.16150966434299]
We formulate a UAV-enabled collaborative beamforming multi-objective optimization problem (UCBMOP) to maximize the transmission rate of the UVAA and minimize the energy consumption of all UAVs.
We use the heterogeneous-agent trust region policy optimization (HATRPO) as the basic framework, and then propose an improved HATRPO algorithm, namely HATRPO-UCB.
arXiv Detail & Related papers (2024-04-11T03:19:22Z) - 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) - Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual
Antenna Arrays [55.736718475856726]
Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT)
Existing UAV-assisted data harvesting and dissemination schemes require UAVs to frequently fly between the IoTs and access points.
We introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination.
arXiv Detail & Related papers (2023-08-03T02:49:50Z) - Evolutionary Multi-Objective Reinforcement Learning Based Trajectory
Control and Task Offloading in UAV-Assisted Mobile Edge Computing [8.168647937560504]
This paper studies the trajectory control and task offloading (TCTO) problem in an unmanned aerial vehicle (UAV)-assisted mobile edge computing system.
It adapts the evolutionary multi-objective RL (EMORL), a multi-policy multi-objective RL, to the TCTO problem.
arXiv Detail & Related papers (2022-02-24T11:17:30Z) - Multi-Task Learning with Sequence-Conditioned Transporter Networks [67.57293592529517]
We aim to solve multi-task learning through the lens of sequence-conditioning and weighted sampling.
We propose a new suite of benchmark aimed at compositional tasks, MultiRavens, which allows defining custom task combinations.
Second, we propose a vision-based end-to-end system architecture, Sequence-Conditioned Transporter Networks, which augments Goal-Conditioned Transporter Networks with sequence-conditioning and weighted sampling.
arXiv Detail & Related papers (2021-09-15T21:19:11Z) - Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal
Constraints [52.58352707495122]
We present a multi-robot allocation algorithm that decouples the key computational challenges of sequential decision-making under uncertainty and multi-agent coordination.
We validate our results over a wide range of simulations on two distinct domains: multi-arm conveyor belt pick-and-place and multi-drone delivery dispatch in a city.
arXiv Detail & Related papers (2020-05-27T01:10:41Z) - Bypassing or flying above the obstacles? A novel multi-objective UAV
path planning problem [0.0]
This study proposes a novel integer programming model for a collision-free discrete drone path planning problem.
Considering the possibility of bypassing obstacles or flying above them, this study aims to minimize the path length, energy consumption, and maximum path risk simultaneously.
arXiv Detail & Related papers (2020-04-12T13:42:05Z)
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.