UAV-assisted autonomous mobile robot navigation for as-is 3D data collection and registration in cluttered environments

Kim, Pileun; Park, Jisoo; Cho, Yong; Kang, Junsuk

doi:10.1016/j.autcon.2019.102918

Cited by 68 publications

(27 citation statements)

References 14 publications

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“…Li and Liu (2019) presented improved neural networks to extract road information from remote-sensing images using a camera sensor equipped with UAV. Kim et al (2019b) proposed a UAV-assisted robotic approach that can significantly reduce human intervention, as well as the time for data collection and processing, and provide technologies to enable cluttered environments to be frequently monitored, updated and analyzed so as to support timely decision-making. Ham and Kamari (2019) proposed a new method of automatically retrieving photo-worthy frames containing construction-related content that is scattered in collected video footages or consecutive images.…”

Section: Information Management and Visualizationmentioning

confidence: 99%

Toward digitalization in the construction industry with immersive and drones technologies: a critical literature review

Elghaish¹,

Matarneh

Talebi³

et al. 2020

SASBE

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PurposeIn this study, a critical literature review was utilized in order to provide a clear review of the relevant existing studies. The literature was analyzed using the meta-synthesis technique to evaluate and integrate the findings in a single context.Design/methodology/approachDigital transformation in construction requires employing a wide range of various technologies. There is significant progress of research in adopting technologies such as unmanned aerial vehicles (UAVs), also known as drones, and immersive technologies in the construction industry over the last two decades. The purpose of this research is to assess the current status of employing UAVs and immersive technologies toward digitalizing the construction industry and highlighting the potential applications of these technologies, either individually or in combination and integration with each other.FindingsThe key findings are: (1) UAVs in conjunction with 4D building information modeling (BIM) can be used to assess the project progress and compliance checking of geometric design models, (2) immersive technologies can be used to enable controlling construction projects remotely, applying/checking end users’ requirements, construction education and team collaboration.Practical implicationsA detailed discussion around the application of UAVs and immersive technologies is provided. This is expected to support gaining an in-depth understanding of the practical applications of these technologies in the industry.Originality/valueThe review contributes a needed common basis for capturing progress made in UAVs and immersive technologies to date and assessing their impact on construction projects. Moreover, this paper opens a new horizon for novice researchers who will conduct research toward digitalized construction.

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Section: Information Management and Visualizationmentioning

confidence: 99%

Toward digitalization in the construction industry with immersive and drones technologies: a critical literature review

Elghaish¹,

Matarneh

Talebi³

et al. 2020

SASBE

View full text Add to dashboard Cite

show abstract

“…In addition, buildings in investigated areas may be vulnerable under helicopters and airplanes. Today, unmanned aircraft systems (UASs) have become one of the most popular tools for data collection in the field of 3D capturing of buildings and construction site surveying for both indoor and outdoor structures [31][32][33]. For example, Kim et al 2019 [31] operated drones over a construction site to efficiently gather data.…”

Section: Data Collection Approachesmentioning

confidence: 99%

“…Today, unmanned aircraft systems (UASs) have become one of the most popular tools for data collection in the field of 3D capturing of buildings and construction site surveying for both indoor and outdoor structures [31][32][33]. For example, Kim et al 2019 [31] operated drones over a construction site to efficiently gather data. Hamledari et al 2017 [33] implemented computer vision algorithms on drone images to automatically detect components of indoor partitions such as studs, insulation, electrical outlets, and the state of drywall work.…”

Section: Data Collection Approachesmentioning

confidence: 99%

Fusing tie points' RGB and thermal information for mapping large areas based on aerial images: A study of fusion performance under different flight configurations and experimental conditions

Hou

Volk

Chen

et al. 2021

Automation in Construction

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“…The scanning process has been often carried out using scanners handled by people [10] or scanners on board of vehicles controlled by people [11]. In [12], Kim et al assisted the scanning process with UAVs that generated an initial 3D map of the area, then based on this map, the optimal scanning locations are estimated, and the optimal navigation path is determined. After that, a robot equipped with a laser scanning system is used to follow the path and obtain high-resolution scans in the estimated locations.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Navigation in Search and Rescue Simulated Environment using Deep Reinforcement Learning

Abdeh

Abut

Akay

2021

Balkan Journal of Electrical and Computer Engineering

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Human assisted search and rescue (SAR) robots are increasingly being used in zones of natural disasters, industrial accidents, and civil wars. Due to complex terrains, obstacles, and uncertainties in time availability, there is a need for these robots to have a certain level of autonomy to act independently for approaching certain SAR tasks. One of these tasks is autonomous navigation. Previous approaches to develop autonomous or semiautonomous SAR navigating robots use heuristics-based methods. These algorithms, however, require environment-related prior knowledge and enough sensing capabilities, which are hard to maintain due to restrictions of size and weight in highly unstructured environments such as collapsed buildings. This study approaches the problem of autonomous navigation using a modified version of the Deep Q-Network algorithm. Unlike the classical usage of the entire game screen images to train the agent, our approach uses only the images captured by the agent's lowresolution camera to train the agent for navigating through an arena avoiding obstacles and to reach a victim. This approach is a much more relevant way of decision making in complex, uncertain contexts; since in real-world SAR scenarios, it is almost impossible to have the area's full information to be used by SAR teams. We simulated a SAR scenario, which consists of an arena full of randomly generated obstacles, a victim, and an autonomous SAR robot. The simulation results show that the agent was able to reach the victim in 56% of the evaluation episodes after 400 episodes of training.

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UAV-assisted autonomous mobile robot navigation for as-is 3D data collection and registration in cluttered environments

Cited by 68 publications

References 14 publications

Toward digitalization in the construction industry with immersive and drones technologies: a critical literature review

Toward digitalization in the construction industry with immersive and drones technologies: a critical literature review

Fusing tie points' RGB and thermal information for mapping large areas based on aerial images: A study of fusion performance under different flight configurations and experimental conditions

Autonomous Navigation in Search and Rescue Simulated Environment using Deep Reinforcement Learning

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