Development of Unmanned Aerial Vehicle (UAV) ornithopter with wireless radio control

Putri, Farika Tono; Ariyanto, Mochammad; Haryanto, Ismoyo; Arozi, Moh.; Caesarendra, Wahyu; Hanan, Muhamad Rizki Ibrahim

doi:10.1109/icitacee.2016.7892415

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…Therefore, unmanned aerial vehicle flight behavior can be influenced by mechanisms motivated by avian neuron activity. Research has focused on developing unmanned aerial vehicle flight behaviour [19,[21][22][23][24][25] but lesser research has focused on developing neuronal networks motivated by avian intelligence. This is because of lesser understanding on the role of cognitive ornithology in bird flight and associated adaptation.…”

Section: Motivation and Related Backgroundmentioning

confidence: 99%

Intelligent learning diversity mechanism for unmanned aerial vehicles applications

Periola¹,

Obayiuwana²

2020

Nig. J. Tech.

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The increased use of drones and aerial vehicles in applications poses challenges of airspace safety for aviation organizations. It is important to ensure the safety of the airspace when a significant number of unmanned aerial vehicles are deployed by civilian users. A solution that meets this requirement is important to promote innovation in the commercialization of air space for civilian users deploying unmanned aerial vehicle. The discussion in this paper proposes a mechanism that uses artificial intelligence to address this challenge. The proposed mechanism utilizes a low altitude platform (LAP) and entities in terrestrial wireless networks. The low altitude platform (LAP) observes, develops insights and training data (with human aid). The training data is used to develop learning mechanisms which determine the suitable unmanned aerial vehicles flight parameters in different scenarios. The use of the LAP reduces the burden of communicating with terrestrial base stations. The unmanned aerial vehicles have a reduced altitude between the LAPs in comparison to terrestrial base stations. This reduces the free space path loss and rain-induced attenuation. The performance benefit of the proposed mechanism in comparison to existing solution is examined via MATLAB simulations. Evaluation shows that the proposed mechanism reduces the network access costs by up to 90% on average. The proposed mechanism also increases available flight power and improves airspace safety by 37.3% and up to 53.2% on average respectively. Keywords: Autonomous unmanned aerial vehicles, Intelligence Paradigm; Aviation Safety, Capital Constrained Aviation Organizations.

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Section: Motivation and Related Backgroundmentioning

confidence: 99%

Intelligent learning diversity mechanism for unmanned aerial vehicles applications

Periola¹,

Obayiuwana²

2020

Nig. J. Tech.

View full text Add to dashboard Cite

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“…Previously, unmanned vehicles, such as Unmanned Aerial Vehicles (UAV) and Unmanned Ground Vehicles (UGV)/mobile robots, had become popular research topics in developing countries because of their versatility, and ease of development. For example, the development of an ornithopter-shaped UAV controlled by a radio control system [1], the development of a UAV embedded with a disinfectant spraying system to break the chain of the COVID-19 virus [2], and fuzzy-based fault-tolerant control for an omnidirectional mobile robot with four mecanum wheels [3].…”

Section: Introductionmentioning

confidence: 99%

Development and Performance Measurement of an Affordable Unmanned Surface Vehicle (USV)

et al. 2022

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Indonesia is a maritime country that has vast coastal resources and biodiversity. To support the Indonesian maritime program, a topography mapping tool is needed. The ideal topography mapping tool is the Unmanned Surface Vehicle (USV). This paper proposes the design, manufacture, and development of an affordable autonomous USV. The USV which is composed of thruster and rudder is quite complicated to build. This study employs rudderless and double thrusters as the main actuators. PID compensator is utilized as the feedback control for the autonomous USV. Energy consumption is measured when the USV is in autonomous mode. The Dynamics model of USV was implemented to study the roll stability of the proposed USV. Open-source Mission Planner software was selected as the Ground Control Station (GCS) software. Performance tests were carried out by providing the USV with an autonomous mission to follow a specific trajectory. The results showed that the developed USV was able to complete autonomous mission with relatively small errors, making it suitable for underwater topography mapping.

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Development of Unmanned Aerial Vehicle (UAV) ornithopter with wireless radio control

Cited by 2 publications

References 2 publications

Intelligent learning diversity mechanism for unmanned aerial vehicles applications

Intelligent learning diversity mechanism for unmanned aerial vehicles applications

Development and Performance Measurement of an Affordable Unmanned Surface Vehicle (USV)

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