Event Title

Nondestructive Data Driven Motion Planning for Inspection Robots

Presenter Information

Hung La, University of Nevada, Reno

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Description

During the first four years, the team led by Dr. Hung La of the Advanced Robotics and Automation (ARA) lab, University of Nevada, Reno (UNR), has developed four different climbing robotic prototypes and their control framework to allow the user to manually control these robots to climb on various steel structures. In the final year of this project, the ARA team aims to provide these climbing robotic systems an autonomous navigation function so that they can safely traverse on and visit all steel members of the bridge for efficient inspection.

This project expects two important algorithms to be developed: (1) point cloud segmentation algorithm/software for accurate and reliable classification of the bridge structures; (2) an autonomous navigation algorithm/software for the climbing robots. The successful development of these two algorithms will allow the robot to operate/inspect the bridge autonomously with minimum user’s intervention and provide a low cost and less traffic disruption inspection solution.

Presentation Date

10 Aug 2020, 2:00 pm - 2:30 pm

Meeting Name

INSPIRE-UTC 2021 Annual Meeting

Department(s)

Civil, Architectural and Environmental Engineering

Document Type

Presentation

Document Version

Final Version

File Type

text

Language(s)

English

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Aug 10th, 2:00 PM Aug 10th, 2:30 PM

Nondestructive Data Driven Motion Planning for Inspection Robots

During the first four years, the team led by Dr. Hung La of the Advanced Robotics and Automation (ARA) lab, University of Nevada, Reno (UNR), has developed four different climbing robotic prototypes and their control framework to allow the user to manually control these robots to climb on various steel structures. In the final year of this project, the ARA team aims to provide these climbing robotic systems an autonomous navigation function so that they can safely traverse on and visit all steel members of the bridge for efficient inspection.

This project expects two important algorithms to be developed: (1) point cloud segmentation algorithm/software for accurate and reliable classification of the bridge structures; (2) an autonomous navigation algorithm/software for the climbing robots. The successful development of these two algorithms will allow the robot to operate/inspect the bridge autonomously with minimum user’s intervention and provide a low cost and less traffic disruption inspection solution.