Autonomous Aerial Power Plant Inspection in GPS-denied Environments
Inspection of coal-fired power plants is frequently dangerous, includes difficult places to reach, and can turn expensive due to the downtimes and cost of inspection crew. Robotic systems have shown capabilities to address some of these issues, but most of the current robotic inspection technology in power plants is designed for specific components. Conversely, recent advances in machine vision have empowered aerial platforms for long-range, remotely-controlled, GPS-based inspections of industrial plants. This capability has led to wide spread utilization of aerial robots (commonly termed Drones, UVS or UAS) platforms for inspection in less challenging environments where both collision avoidance, and GPS reception are not significant issues. The challenge in adapting airborne technology for power plant inspection lies in internal structures and the complex network of piping, and distribution systems, which impose significant risks for collision and can hinder the reception and transmission of GPS signals. The current state of the art in aerial inspection technology within the energy sector is controlled via radio control, and utilizes GPS-based navigation, for inspection of large-scale plants such as offshore platforms and wind turbine parks. Nevertheless, close-range and autonomous inspection in the GPS-denied environments of power plants has not yet been achieved, as it requires precise guidance and navigation with real-time situational awareness and obstacle avoidance capabilities. This endeavor introduced the use of rotary wing flying robots, due to their station keeping and vertical take-off capabilities for power plant components inspection. To enable close quarter inspection two methods were used. One method uses the 3D CAD (Three-dimensional Computer-Aided Design) model of the asset to inspect to generate the UAV’s inspection path. To acquire, analyze and process the 3D model, first, the STL file is produced to obtain surface points and vectors normal to the surface. Later, by introducing other variables such as wall offset and a controlled trajectory between each outline and each subsequent layer, the flight path is generated. The proposed framework will generate a path that will pass as close as desired from the surface and navigate in intricate environments. A second method, use advanced manufacturing techniques such as CNC (Computer Numerical Control) and additive manufacturing. Once the inspection flight path is obtained, vision-based navigation systems are employed to have the UAV autonomously tracking the provided trajectory. Finally, Artificial Intelligence-enabled developments are in charge of detecting cracks and corrosion in structural components of power plants. The proposed methods are validated in simulations, laboratory and industrial setups, where it is shown that the developed systems acting together enable close-quarter autonomous aerial inspection and mapping in power plant assets. The system can be further improved by adding more sensors to navigate in different GPS-denied environments, with non-homogeneous lighting conditions, dust and in general situations where vision-based systems may fail.
- Research Organization:
- Univ. of Texas at El Paso, TX (United States)
- Sponsoring Organization:
- USDOE Office of Fossil Energy (FE), Office of Clean Energy Systems (FE-22)
- DOE Contract Number:
- FE0031655
- OSTI ID:
- 1905874
- Report Number(s):
- DOE-UTEP-FE0031655
- Resource Relation:
- Related Information: M. M. Rizia, J. Reyes-Munoz, A. Choudhuri, and Flores-Abad, A.. "Intelligent Crack Detection in Infrastructure using Computer Vision at the Edge." (2022).Rizia, Mousumi, Julio A. Reyes-Munoz, Angel G. Ortega, Ahsan Choudhuri, and Angel Flores-Abad. "Autonomous aerial flight path inspection using advanced manufacturing techniques." Robotica (2022): 1-24.Reyes-Munoz, Julio A., and Angel Flores-Abad. "A MAV Platform for Indoors and Outdoors Autonomous Navigation in GPS-denied Environments." In 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), pp. 1708-1713. IEEE, 2021.Ortega, Angel, Julio Reyes Muñoz, Michael McGee, Ahsan R. Choudhuri, and Angel Flores-Abad. "Drone inspection flight path generation from 3d cad models: Power plant boiler case study." In AIAA Scitech 2020 Forum, p. 1091. 2020.
- Country of Publication:
- United States
- Language:
- English
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