Multi-Objective Trajectory Planning for Slung-Load Quadrotor System

In this article, multi-objective trajectory planning has been carried out for a quadrotor carrying a slung load. The goal is to obtain non-dominated solutions for path length, mission duration, and dissipated energy cost functions. These costs are optimized by imposing constraints on the slung-load...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autor principal:	Halit Ergezer
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Multiobjective optimization slung-load quadrotor system trajectory planning Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/d870c26309c447fbbb5d5177dad1a2ad
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:d870c26309c447fbbb5d5177dad1a2ad
record_format	dspace
spelling	oai:doaj.org-article:d870c26309c447fbbb5d5177dad1a2ad2021-11-26T00:01:57ZMulti-Objective Trajectory Planning for Slung-Load Quadrotor System2169-353610.1109/ACCESS.2021.3129265https://doaj.org/article/d870c26309c447fbbb5d5177dad1a2ad2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9620080/https://doaj.org/toc/2169-3536In this article, multi-objective trajectory planning has been carried out for a quadrotor carrying a slung load. The goal is to obtain non-dominated solutions for path length, mission duration, and dissipated energy cost functions. These costs are optimized by imposing constraints on the slung-load quadrotor system’s endpoints, borders, obstacles, and dynamical equations. The dynamic model of a slung-load quadrotor system is used in the Euler-Lagrange formulation. Although the differential flatness feature is mostly used in this system’s trajectory planning, a fully dynamic model has been used in our study. A new multi-objective Genetic Algorithm has been developed to solve path planning, aiming to optimize trajectory length, mission time, and energy consumed during the mission. The solution process has a three-phase algorithm: Phase-1 is about randomly generating waypoints, Phase-2 is about constructing the initial non-dominated pool, and the final phase, Phase-3, is obtaining the solution. In addition to conventional genetic operators, simple genetic operators are proposed to improve the trajectories locally. Pareto Fronts have been obtained corresponding to exciting scenarios. The method has been tested, and results have been presented at the end. A comparison of the solutions obtained with MOGA operators and MOPSO over hypervolume values is also presented.Halit ErgezerIEEEarticleMultiobjective optimizationslung-load quadrotor systemtrajectory planningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 155003-155017 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Multiobjective optimization slung-load quadrotor system trajectory planning Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Multiobjective optimization slung-load quadrotor system trajectory planning Electrical engineering. Electronics. Nuclear engineering TK1-9971 Halit Ergezer Multi-Objective Trajectory Planning for Slung-Load Quadrotor System
description	In this article, multi-objective trajectory planning has been carried out for a quadrotor carrying a slung load. The goal is to obtain non-dominated solutions for path length, mission duration, and dissipated energy cost functions. These costs are optimized by imposing constraints on the slung-load quadrotor system’s endpoints, borders, obstacles, and dynamical equations. The dynamic model of a slung-load quadrotor system is used in the Euler-Lagrange formulation. Although the differential flatness feature is mostly used in this system’s trajectory planning, a fully dynamic model has been used in our study. A new multi-objective Genetic Algorithm has been developed to solve path planning, aiming to optimize trajectory length, mission time, and energy consumed during the mission. The solution process has a three-phase algorithm: Phase-1 is about randomly generating waypoints, Phase-2 is about constructing the initial non-dominated pool, and the final phase, Phase-3, is obtaining the solution. In addition to conventional genetic operators, simple genetic operators are proposed to improve the trajectories locally. Pareto Fronts have been obtained corresponding to exciting scenarios. The method has been tested, and results have been presented at the end. A comparison of the solutions obtained with MOGA operators and MOPSO over hypervolume values is also presented.
format	article
author	Halit Ergezer
author_facet	Halit Ergezer
author_sort	Halit Ergezer
title	Multi-Objective Trajectory Planning for Slung-Load Quadrotor System
title_short	Multi-Objective Trajectory Planning for Slung-Load Quadrotor System
title_full	Multi-Objective Trajectory Planning for Slung-Load Quadrotor System
title_fullStr	Multi-Objective Trajectory Planning for Slung-Load Quadrotor System
title_full_unstemmed	Multi-Objective Trajectory Planning for Slung-Load Quadrotor System
title_sort	multi-objective trajectory planning for slung-load quadrotor system
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/d870c26309c447fbbb5d5177dad1a2ad
work_keys_str_mv	AT halitergezer multiobjectivetrajectoryplanningforslungloadquadrotorsystem
_version_	1718409996852527104

Multi-Objective Trajectory Planning for Slung-Load Quadrotor System

Ejemplares similares