Blind localization method for quadrotor-unmanned aerial vehicle (QUAV) utilizing genetic algortihm

Faelden, Gerard Ely U.; Maningo, Jose Martin Z.; Nakano, Reiichiro; Dadios, Elmer P.

doi:10.1109/hnicem.2014.7016214

Cited by 14 publications

(2 citation statements)

References 9 publications

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“…The fitness function is based on the threat cost (to safely avoid all threats for the UAV during flight) and path cost (to follow the shortest path to the destination). Faelden et al 29 present position localization of UAV with the use of genetic algorithm (L-GA) and transceivers signals. In GA, the received signal amount, population size and location of the transceiver are used as inputs to locate the UAV in the xyz -axis.…”

Section: Literature Reviewmentioning

confidence: 99%

Bio-inspired cluster–based optimal target identification using multiple unmanned aerial vehicles in smart precision agriculture

Salam

Javaid

Ahmad

2021

International Journal of Distributed Sensor Networks

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Farming is the major profession in several republics for centuries. However, due to the immigration of individuals from rural to urban, there is prevention in farming. The use of modern technology in the precision agriculture field increases productivity and also improves the exports of a country. The productivity may suffer due to different environmental factors, diseases and insects attacks on the crops, especially tomatoes. The target area (i.e. the affected crops area due to environmental factors) identification and delivery of timely information about diseases in the crops to the ground station are mandatory to make the precautionary measurements. In flying sensor networks, the localization and clustering of multiple unmanned aerial vehicles for target areas identification is a challenging task due to energy constraints, communication range, frequent change in topology, link expiration and high mobility. In this article, we proposed the localization and clustering of multiple unmanned aerial vehicles for the identification of affected target areas in the tomato crop field. The localization of unmanned aerial vehicles depends on the weights of environmental factors, that is, relative humidity, soil moisture, temperature, light intensity, NPK (nitrogen (n), phosphorus (p) and potassium (k)) and power of hydrogen (pH). A honey bee optimization approach is used for the localization and formation of multiple unmanned aerial vehicles’ cluster to accurately identify the target areas. The performance of our bio-inspired approach is compared in terms of communication overhead, packet delivery ratio, mean end-to-end delay and energy consumption with the existing swarm intelligence–based schemes and validated via a simulation. The simulation result shows that the bio-inspired approach performs better among the selected approaches.

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Section: Literature Reviewmentioning

confidence: 99%

Bio-inspired cluster–based optimal target identification using multiple unmanned aerial vehicles in smart precision agriculture

Salam

Javaid

Ahmad

2021

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…The path planner was used in a three-dimensional environment to calculate the path with curvature in rough terrain. An alternative way of position localization of quadrotor without using GPS or cameras was presented by Faelden et al [36]. The method was dependent on using a transceiver's signal as inputs for the genetic algorithm to locate the quadrotor in x, y, and z-axis.…”

Section: The Genetic Algorithmmentioning

confidence: 99%

Immunity-Based Framework for Autonomous Flight in GPS-Challenged Environment

Nuaimi¹

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A neural network approach to a cooperative balancing problem in quadrotor-unmanned aerial vehicles (QUAVs)

Faelden

Maningo

Nakano

et al. 2015

2015 International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Ma

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Blind localization method for quadrotor-unmanned aerial vehicle (QUAV) utilizing genetic algortihm

Cited by 14 publications

References 9 publications

Bio-inspired cluster–based optimal target identification using multiple unmanned aerial vehicles in smart precision agriculture

Bio-inspired cluster–based optimal target identification using multiple unmanned aerial vehicles in smart precision agriculture

Immunity-Based Framework for Autonomous Flight in GPS-Challenged Environment

A neural network approach to a cooperative balancing problem in quadrotor-unmanned aerial vehicles (QUAVs)

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