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Design of a behavior-based navigation algorithm for autonomous tunnel inspection

Riccardo Tassi

Design of a behavior-based navigation algorithm for autonomous tunnel inspection.

Rel. Marcello Chiaberge. Politecnico di Torino, Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica), 2022

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The modern world is characterized by the presence of a significant amount of subterranean infrastructures, including networks of tunnels, caverns, and the urban underground. Each of these environments offers intricate settings that could create difficulties for exploration, inspection, and several other activities. Conditions might deteriorate and vary over time, and there may be various risks. Most of the time, this confluence of difficulties and dangers creates circumstances that are too dangerous for workers. Situations like gas leaks, explosions, rock falls, confinement, and prolonged exposure to dust are all potentially lethal. Robotic solutions are thus required to operate when and where human risk is too high. Autonomous robots can lower risks by taking over potentially hazardous jobs for workers. For instance, an autonomous robot can do tasks like assessing the air quality or checking the conditions in hazardous mines. To achieve this goal, a robot platform must be developed with a series of sensors and algorithms that allow it to navigate autonomously inside the tunnel, collecting the necessary data without any human intervention. This thesis project intends to contribute to this field by designing a robotic platform with the least amount of sensors and algorithms to autonomously accomplish the tunnel inspection task. Specific requirements and environmental difficulties guided the design phase: the rover must be able to cover the entire unknown tunnel plan to perform a good inspection, in the presence of challenging terrains, low-light visibility, and GPS-denied environments. The selected robotic platform consists of a Clearpath Husky rover. A LiDAR sensor is employed to perceive walls and obstacles, while localization is achieved by fusing IMU and encoder data. The main effort was focused on developing the navigation algorithm. It consists of a behaviour-based navigation algorithm that does not require a global map of the environment to work. A state machine interprets LIDAR data and processes specific instantaneous paths tracked by the robot through a Pure Pursuit controller. The paths generation is designed to let the robot always keep the left wall, in such a way, the exploration of the entire tunnel plan is ensured. The navigation algorithm has been tested in simulation with three tunnel models with different sizes and characteristics. In all models tested the robot successfully covered the entire tunnel plans and returned to the starting point. Moreover, the entire system was also successfully deployed and tested on the robotic platform in a real environment.

Relators: Marcello Chiaberge
Academic year: 2022/23
Publication type: Electronic
Number of Pages: 86
Corso di laurea: Corso di laurea magistrale in Mechatronic Engineering (Ingegneria Meccatronica)
Classe di laurea: New organization > Master science > LM-25 - AUTOMATION ENGINEERING
Aziende collaboratrici: Politecnico di Torino - PIC4SER
URI: http://webthesis.biblio.polito.it/id/eprint/25599
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