Path Planning for Single Unmanned Aerial Vehicle by Separately Evolving Waypoints

Yang, Peng; Tang, Ke; Lozano, José A.; Cao, Xianbin

doi:10.1109/tro.2015.2459812

Cited by 121 publications

(93 citation statements)

References 41 publications

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“…In this study, a heuristic cost function is introduced that can promote the ant to move towards the target: (12) where T j is the distance between node j and the target point and T i is the distance between node i and the end point. If T j > T i , the ants are moving farther and farther away from the target and will be assigned a smaller probability to select this node.…”

Section: Heuristic Function and Valley-followingmentioning

confidence: 99%

“…If T j > T i , the ants are moving farther and farther away from the target and will be assigned a smaller probability to select this node. Equation (12) can promote the ants to walk to the destination node; however, the ants cannot use the local valley terrain information to achieve valley-following. When we analysed the terrain data, we found that the valley region was continuous, and the enhanced local valley-following algorithm is proposed accordingly.…”

Section: Heuristic Function and Valley-followingmentioning

confidence: 99%

“…The UAV cannot smoothly complete the turning process. There are some common segment fitting methods, such as the Dubins curve [15] and the Bézier curve [2], and some of the authors do not smooth the broken line [12]. In fact, a Fly-by Fix or Fly-over Fix [22] is usually used in PBN flight procedures for turning.…”

Section: Smooth Route Turningmentioning

confidence: 99%

“…Because these two methods use the dimension-reduction encode method to achieve evolutionary computation, the local valley-following method in Section 4.2.2 was not applicable. This study used the minimal flight altitude method in [12] to update the average altitude of the route. Because of the interval difference between adjacent waypoints, interval interpolation for the route was needed when calculating the average altitude.…”

Section: Comparison Experimentsmentioning

confidence: 99%

“…Genetic algorithms (GA) are used to solve the travelling salesman problems related to UAVs, such as maximum information collection [9]. The evolutionary algorithm (EA) is used for multi-constraint route planning in a simulation scenario [10][11][12]. The particle swarm optimizer (PSO) is used to solve the path planning problem of UAVs on the sea [13].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Unmanned Aerial Vehicle Route Planning in the Presence of a Threat Environment Based on a Virtual Globe Platform

Zhang

Liu

et al. 2016

IJGI

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Route planning is a key technology for an unmanned aerial vehicle (UAV) to fly reliably and safely in the presence of a threat environment. Existing route planning methods are mainly based on the simulation scene, whereas approaches based on the virtual globe platform have rarely been reported. In this paper, a new planning space for the virtual globe and the planner is proposed and a common threat model is constructed for threats including a no-fly zone, hazardous weather, radar coverage area, missile killing zone and dynamic threats. Additionally, an improved ant colony optimization (ACO) algorithm is developed to enhance route planning efficiency and terrain masking ability. Our route planning methods are optimized on the virtual globe platform for practicability. A route planning system and six types of planners were developed and implemented on the virtual globe platform. Finally, our evaluation results demonstrate that our optimum planner has better performance in terms of fuel consumption, terrain masking, and risk avoidance. Experiments also demonstrate that the method and system described in this paper can be used to perform global route planning and mission operations.

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Section: Heuristic Function and Valley-followingmentioning

confidence: 99%

Section: Heuristic Function and Valley-followingmentioning

confidence: 99%

Section: Smooth Route Turningmentioning

confidence: 99%

Section: Comparison Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Unmanned Aerial Vehicle Route Planning in the Presence of a Threat Environment Based on a Virtual Globe Platform

Zhang

Liu

et al. 2016

IJGI

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A Hybrid Evolutionary Algorithm for Offline UAV Path Planning

Ghambari

Idoumghar

Jourdan

et al. 2020

Lecture Notes in Computer Science

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Multi-UAS Minimum Time Search in Dynamic and Uncertain Environments

Carabaza

2021

Springer Theses

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La tesis es una obra original que no infringe los derechos de propiedad intelectual ni los derechos de propiedad industrial u otros, de acuerdo con el ordenamiento jurídico vigente, en particular, la Ley de Propiedad Intelectual (R.D. legislativo 1/1996, de 12 de abril, por el que se aprueba el texto refundido de la Ley de Propiedad Intelectual, modificado por la Ley 2/2019, de 1 de marzo, regularizando, aclarando y armonizando las disposiciones legales vigentes sobre la materia), en particular, las disposiciones referidas al derecho de cita.Del mismo modo, asumo frente a la Universidad cualquier responsabilidad que pudiera derivarse de la autoría o falta de originalidad del contenido de la tesis presentada de conformidad con el ordenamiento jurídico vigente.En Madrid, a ____ de _________________________ de 20___ Fdo.: _______________________________ Esta DECLARACIÓN DE AUTORÍA Y ORIGINALIDAD debe ser insertada en la primera página de la tesis presentada para la obtención del título de Doctor.

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Path Planning for Single Unmanned Aerial Vehicle by Separately Evolving Waypoints

Cited by 121 publications

References 41 publications

Unmanned Aerial Vehicle Route Planning in the Presence of a Threat Environment Based on a Virtual Globe Platform

Unmanned Aerial Vehicle Route Planning in the Presence of a Threat Environment Based on a Virtual Globe Platform

A Hybrid Evolutionary Algorithm for Offline UAV Path Planning

Multi-UAS Minimum Time Search in Dynamic and Uncertain Environments

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