Three Dimensional Multi-Objective UAV Path Planning Using Digital Pheromone Particle Swarm Optimization

Holub, Joseph; Foo, Jung Leng; Kalivarapu, Vijay; Winer, Eliot H.

doi:10.2514/6.2012-1525

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…La mise-en-oeuvre sur unité centrale de traitement (CPU) de Ozalp et Sahingoz (2013) d'un planificateur de trajectoires d'UAV basé sur l'algorithme génétique utilise une fonction d'évaluation qui assure aussi l'évitement de collision avec les obstacles en plus des considérations de Kok et al (2013). Swartzentruber et al (2010) et Holub et al (2012 présentent une mise-en-oeuvre sur CPU d'un planificateur de trajectoires d'UAV basé sur l'optimisation d'essaim de particules (particle swarm optimization (PSO)) qui va un peu plus loin que l'approche de Ozalp et Sahingoz (2013) en ajoutant la maximisation de la proximité de l'UAV des points de passage que l'UAV doit survoler. Guo et al (2009) et Ling et Hao (2015), avec leur mise-en-oeuvre sur CPU de deux différents algorithmes d'optimisation qui planifient la trajectoire d'UAV, utilisent une fonction d'évaluation qui, en plus d'accomplir l'optimisation de Ozalp et Sahingoz (2013), se sert d'une composante qui assure que les virages de la trajectoire respectent le rayon de courbure minimal de virage de l'UAV.…”

Section: éVolution Des Fonctions D'évaluation De Trajectoires D'uavunclassified

Point de référence pour la planification de trajectoires d’UAV à voilure fixe

Allaire

Labonté

Roberge

et al. 2021

J. Unmanned Veh. Sys.

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Les revues étendues de la littérature sur la planification de trajectoires s’entendent toutes sur un point commun : le manque d’un point de référence pour permettre de comparer les différentes mises-en-œuvre dans ce domaine. Ce travail présente une fonction d’évaluation de trajectoires de véhicules aériens sans pilote (UAV) à voilure fixe qui couvrent quatre critères de volabilité essentiels et trois critères d’optimisation qui peuvent être applicables à une grande variété de mission tout en étant adaptables à l’ajout de critères supplémentaires. Ce travail présente aussi une série de vingt scénarios permettant de couvrir une grande variété de conditions possibles pour permettre de caractériser la qualité des trajectoires planifiées pour un UAV à voilure fixe. Ce travail propose de combiner ces deux éléments pour constituer un environnement de test détaillé comme point de référence pour les futurs travaux sur la planification de trajectoires d’UAV à voilure fixe.

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Section: éVolution Des Fonctions D'évaluation De Trajectoires D'uavunclassified

Point de référence pour la planification de trajectoires d’UAV à voilure fixe

Allaire

Labonté

Roberge

et al. 2021

J. Unmanned Veh. Sys.

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“…28). Only 28% of the surveyed papers (Zheng et al 2003;Cocaud 2006;Allaire et al 2009;Guo et al 2009;Swartzentruber et al 2010;Chen et al 2011;Wan et al 2011;Holub et al 2012;Yan et al 2012;Ozalp and Sahingoz 2013;Roberge et al 2013Roberge et al , 2014Q. Wang et al 2014;Zhan et al 2014;Gardi et al 2015a;Ling and Hao 2015;Wen et al 2015) considered both 3D terrain and obstacle avoidance; these are the ones for which we will further analyze the flyability.…”

Section: D Terrain Collision and No-fly Zone Criteriamentioning

confidence: 99%

“…Both Ozalp and Sahingoz (2013) and Zheng et al (2003) UAV trajectory planners were based on GA. Swartzentruber et al (2010) used PSO as a path planner and B-spline curves to generate the trajectory. Holub et al (2012) built over Swartzentruber et al's (2010) worked and modified the PSO algorithm to improve its computing time. Roberge et al (2013) implemented both a GA and a PSO and demonstrated that its GA implementation provides better trajectories than the PSO implementation.…”

Section: Smoothing Criterionmentioning

confidence: 99%

Recent advances in unmanned aerial vehicles real-time trajectory planning

Allaire

Labonté

Tarbouchi

et al. 2019

J. Unmanned Veh. Sys.

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The growing interest in unmanned aerial vehicles (UAV) can be attributed to many factors, particularly the potential for them to operate beyond visual line of sight and at increasingly higher levels of autonomy. Trajectory planning defines the extent to which a vehicle is “autonomous”, therefore, to operate at high levels of autonomy, UAV will require real-time embedded trajectory planning that respects a minimum of flyability. This paper presents a review of recent advances in UAV trajectory planning and seeks to clarify the flyability, the real-time, and the embedded aspects, which are not adequately considered by previous survey papers. This work, which specifically focuses on Class II (≥150 and ≤600 kg) and Class III (>600 kg) fixed-wing UAV, analyses 60 papers from the last decade that mention some real-time achievement with respect to UAV trajectory planning. From this analysis, we highlight some challenges and some suggested orientations for future works.

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“…This technique can be used to find a local and global solution by providing proper bounds to the search space. PSO has been widely used in aerospace applications such as structural optimisation, controller optimisation, and flight route optimisation [32][33][34][35][36]. However, it started getting researchers' attention as a tool of aerodynamic parameters estimation of flight vehicles recently.…”

Section: Introductionmentioning

confidence: 99%

System identification of cropped delta UAVs from flight test methods using particle Swarm-Optimisation-based estimation

2022

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In the era of Unmanned Aerial Systems (UAS), an onboard autopilot occupies a prominent place and is inevitable for many of their modern applications. The efficacy of autopilot heavily relies upon the accuracy of the sensors employed and the capability of the onboard flight controller. In general, aerodynamic behaviour and flight dynamic capabilities of Unmanned Aerial Vehicles (UAVs) govern the selection and the design of flight controllers. Precise modeling of linear aerodynamic characteristics from flight data can be achieved using many of the existing classical parameter estimation techniques such as Output Error Method (OEM), Equation Error Method (EEM), and Filter Error Method (FEM). However, all the classical methods may not be readily applicable for aerodynamic modeling in nonlinear flight envelopes. The current manuscript is an attempt to exploit the capabilities of the Artificial Intelligence (AI) technique, named Particle Swarm Optimisation (PSO), in combination with Least Squares (LS) cost function to perform linear as well as nonlinear aerodynamic parameter estimation. The aforementioned task is accomplished by considering flight data from manoeuvers pertaining to linear angles of attack, moderate and near stall flight envelopes of two different UAVs with cropped delta planform geometry. Parameters estimated using the proposed LS-PSO method are consistent with minimum standard deviation and are on a par with OEM estimates. The proposed LS-PSO method enhances the capabilities of LS-based EEM while estimating stall characteristic parameters, which was not possible with LS alone. The longitudinal and lateral-directional static parameters estimated from the full-scale wind tunnel testing of the two UAVs were also used to corroborate the results obtained from the flight data using the LS-PSO method.

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Three Dimensional Multi-Objective UAV Path Planning Using Digital Pheromone Particle Swarm Optimization

Cited by 8 publications

References 13 publications

Point de référence pour la planification de trajectoires d’UAV à voilure fixe

Point de référence pour la planification de trajectoires d’UAV à voilure fixe

Recent advances in unmanned aerial vehicles real-time trajectory planning

System identification of cropped delta UAVs from flight test methods using particle Swarm-Optimisation-based estimation

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