Preliminary Hazard Analysis for Uav-Assisted Bridge Inspection

Ayele, Yonas Zewdu; Ashraf, Behrooz

doi:10.2495/ut200141

“…Several articles have presented risk analyses for different drone operations [29]- [37]. None of these works have considered using the results of the risk analysis for online decision-making.…”

Section: A Motivationmentioning

confidence: 99%

Supervisory Risk Control with Application to Industrial Drone Inspection

Rothmund¹,

Thieme²,

Utne³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

This article develops and experimentally tests a supervisory risk controller used to increase the safety of drone operations. Its task is to monitor the state of the drone and environment and to use this information to automatically change safety-critical parameters in real-time during operation. A case study of a tethered industrial inspection drone is considered. A system theoretic process analysis (STPA) is performed to identify how the system can fail. A Dynamic Decision Network (DDN), used as an online risk model, is built based on the results of the STPA. An optimization approach is used to choose an optimal parameter configuration that ensures an acceptable risk level. Through experimental tests, it is demonstrated how the supervisory risk controller is able to identify the state of the drone and the environment by combining information from multiple measurements over time and how it chooses values for the maximum speed, safety distance, and maximum vertical acceleration that produces an acceptable risk level. The parameters are updated during flight based on the output from the supervisory risk controller. When no parameter set can ensure an acceptable risk level then a recommendation of aborting the mission is sent to the human operator. Video of the experimental results can be found at https://youtu.be/RKhG9bguRJY

show abstract

“…Several articles have presented risk analyses for different drone operations [29]- [37]. None of these works have considered using the results of the risk analysis for online decision-making.…”

Section: A Motivationmentioning

confidence: 99%

Supervisory Risk Control with Application to Industrial Drone Inspection

Rothmund¹,

Thieme²,

Utne³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

This article develops and experimentally tests a supervisory risk controller used to increase the safety of drone operations. Its task is to monitor the state of the drone and environment and to use this information to automatically change safety-critical parameters in real-time during operation. A case study of a tethered industrial inspection drone is considered. A system theoretic process analysis (STPA) is performed to identify how the system can fail. A Dynamic Decision Network (DDN), used as an online risk model, is built based on the results of the STPA. An optimization approach is used to choose an optimal parameter configuration that ensures an acceptable risk level. Through experimental tests, it is demonstrated how the supervisory risk controller is able to identify the state of the drone and the environment by combining information from multiple measurements over time and how it chooses values for the maximum speed, safety distance, and maximum vertical acceleration that produces an acceptable risk level. The parameters are updated during flight based on the output from the supervisory risk controller. When no parameter set can ensure an acceptable risk level then a recommendation of aborting the mission is sent to the human operator. Video of the experimental results can be found at https://youtu.be/RKhG9bguRJY

show abstract

“…Mismanagement of drone data has the potential to further confuse inspection engineers, who are now faced with an abundance of digital information [11]. As such, there exists a need to develop a data information model to store, process and manage the bridge inspection data captured through UAS [2,8,12]. To fill this gap, this paper proposed a novel methodology for a digital information model covering data acquisition through to a 3D GIS visualisation environment, also capable of integrating within a bridge management system (BMS).…”

Section: Introductionmentioning

confidence: 99%

A Digital Information Model Framework for UAS-Enabled Bridge Inspection

Achuthan

¹

,

Hay

²

,

Aliyari

³

et al. 2021

View full text Add to dashboard Cite

Unmanned aerial systems (UAS) provide two main functions with regards to bridge inspections: (1) high-quality digital imaging to detect element defects; (2) spatial point cloud data for the reconstruction of 3D asset models. With UAS being a relatively new inspection method, there is little in the way of existing framework for storing, processing and managing the resulting inspection data. This study has proposed a novel methodology for a digital information model covering data acquisition through to a 3D GIS visualisation environment, also capable of integrating within a bridge management system (BMS). Previous efforts focusing on visualisation functionality have focused on BIM and GIS as separate entities, which has a number of problems associated with it. This methodology has a core focus on the integration of BIM and GIS, providing an effective and efficient information model, which provides vital visual context to inspectors and users of the BMS. Three-dimensional GIS visualisation allows the user to navigate through a fully interactive environment, where element level inspection information can be obtained through point-and-click operations on the 3D structural model. Two visualisation environments were created: a web-based GIS application and a desktop solution. Both environments develop a fully interactive, user-friendly model which have fulfilled the aims of coordinating and streamlining the BMS process.

show abstract

Preliminary Hazard Analysis for Uav-Assisted Bridge Inspection

Cited by 4 publications

References 9 publications

Supervisory Risk Control with Application to Industrial Drone Inspection

Supervisory Risk Control with Application to Industrial Drone Inspection

Supervisory Risk Control with Application to Industrial Drone Inspection

A Digital Information Model Framework for UAS-Enabled Bridge Inspection

Contact Info

Product

Resources

About