Automation of 3D Building Model Generation Using Quadrotor

Bulgakov, Alexey; Evgenov, A.A.; Weller, Cornell

doi:10.1016/j.proeng.2015.10.065

Cited by 10 publications

(6 citation statements)

References 4 publications

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“…Weather Commercial RPAs are significantly low weight and can be affected by extreme weather conditions (Bulgakov et al, 2015;Ka cuni c et al, 2016). High winds (>30 km/h) limit control and reduce flight time due to overuse of battery power to maintain position.…”

Section: Site-related Problemsmentioning

confidence: 99%

Barriers to adoption of RPAs on construction projects: a task–technology fit perspective

Golizadeh

Hosseini

Edwards

et al. 2019

Construction Innovation

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Purpose-Extant literature extensively articulates the advantages of using remotely piloted aircrafts (RPAs) in a myriad of construction activities. Yet, the barriers that hinder their wider adoption on construction projects have received scant academic attention. This study aims at addressing this gap in the literature. Design/methodology/approach-This study reviews 59 papers published on the use of RPAs for construction activities and offers an evaluation of barriers to widespread adoption throughout the sector. Findings-Barriers are identified, collated and categorized into five thematic groups, namely, technical difficulties, restrictive regulatory environment, site-related problems, weather and organizational barriers. Practical implications-The paper contributes to knowledge by: signposting a need for reordering priorities when defining future research on RPAs, suggesting measures to address the barriers identified and providing pragmatic guidance for construction companies intending to use RPAs on their projects. Originality/value-Using the task-technology fit theory, the study uncovers that current RPA technology is an under-fit match for construction activities and represents a prominent barrier to adoption. This is a dissenting finding, given that past studies on RPAs have primarily focused upon addressing public acceptance, concerns and societal consequences. Enablers of the identified barriers are also collated from extant literature and contemporary practice and encapsulated in a conceptual model.

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Section: Site-related Problemsmentioning

confidence: 99%

Barriers to adoption of RPAs on construction projects: a task–technology fit perspective

Golizadeh

Hosseini

Edwards

et al. 2019

Construction Innovation

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“…In the recent time, the increasing interest in using 2D aerial images, collected by the UAV, in 3D model reconstruction is evident. It is very important to note that the UAV simplifies the process of taking images because operator can set the trajectory of the vehicle individually [8]. The multiple images are acquired by on board high resolution camera from multiangle and multiposition during the flight of the UAV.…”

Section: Reconstruction Of 3d Models Of the Railway Infrastructure Obmentioning

confidence: 99%

Railway Transport Infrastructure Monitoring by UAVs and Satellites

Ivashov¹,

Tataraidze²,

Razevig³

et al. 2019

JTTs

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Improving the rail transport security requires development and implementation of neoteric monitoring and control facilities in conditions of increasing speed and intensity of the train movement and high level of terrorist threat. Use of Earth remote sensing (ERS), permitting to obtain information from large areas with a sufficiently high resolution, can provide significant assistance in solving the mentioned problems. This paper discusses the possibility of using various means of remote sensing such as satellites and unmanned aerial vehicles (UAV), also known as drones, for receiving information in different ranges of the electromagnetic spectrum. The paper states that joint using of both these means gives new possibilities in improving railroad security.

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“…To deal with uncertainties, a linear quadratic regulator controller was applied for better stabilization and improving the flight quality of quadrotor under noisy sensor measurements [5]. A sliding mode controller was obtained based on the back-stepping method for stabilization of a quadrotor [6]. Model reference adaptive and fixed gain linear quadratic regulator controllers were implemented for a quadrotor with parametric uncertainties [7].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Trajectory Tracking Control of a Quadrotor Based on Iterative Learning Algorithm

Farzaneh¹,

Tavakolpour-Saleh²

2020

Journal of Engineering Technology and Applied Sciences

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This paper presents a new adaptive and optimal algorithm for the trajectory tracking control of a quadrotor using iterative learning algorithm (ILA) and enumerative learning method. Ordinarily the ILA, as an adaptive method, can perform well with PID control to improve the controller's performance for a nonlinear system. Quadrotors are considered as nonlinear and unstable systems in which the use of an adaptive and optimal controller can increase its stability and decrease error level. In this method, a PID controller is proposed for the inner and outer control loops of a quadrotor and the ILA is used to adapt PID control gains. Subsequently, an enumerative learning algorithm is used to optimize the learning rates of the ILA. For this purpose, at first, the dynamic model of the quadrotor is acquired. After that, the structure of the control system and the inner and outer control loops are defined. In the end, the simulation results for the trajectory tracking control are demonstrated. Through simulation, it is concluded that as time increases, the performance of the suggested control method in trajectory tracking control becomes better and better and error signals convergence to zero.

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Automation of 3D Building Model Generation Using Quadrotor

Cited by 10 publications

References 4 publications

Barriers to adoption of RPAs on construction projects: a task–technology fit perspective

Barriers to adoption of RPAs on construction projects: a task–technology fit perspective

Railway Transport Infrastructure Monitoring by UAVs and Satellites

Adaptive Trajectory Tracking Control of a Quadrotor Based on Iterative Learning Algorithm

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