Thesis etd-06142022-171610 |
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Thesis type
Tesi di laurea magistrale
URN
etd-06142022-171610
Thesis title
SLAM system development for mobile robots in a vineyard with uneven terrain and steep slopes
Department
INGEGNERIA DELL'INFORMAZIONE
Course of study
INGEGNERIA ROBOTICA E DELL'AUTOMAZIONE
Supervisors
.
relatore Prof.ssa Pallottino, Lucia
relatore Ing. Stefanini, Elisa
relatore Ing. Stefanini, Elisa
Keywords
- agriculture
- localization
- mapping
- mobile robots
- RTK GNSS
- SLAM
Graduation session start date
07/07/2022
Availability
Withheld
Release date
07/07/2092
Abstract (Inglese)
Abstract (Italiano)
This thesis work proposes a new 6D Simultaneous Localization And Mapping Algorithm (SLAM) in an agricultural environment, which aims to localize a mobile robot, and, at the same time, recreate a map of the environment. The environment considered is a vineyard characterized by steep slopes and uneven ground.
Agriculture is one of the fields on which more research is being focusing in recent years, with a particular interest in making one of the heaviest workplaces for the human body more automated.
To enable a vehicle to move autonomously in an environment, it is essential that it be able to locate itself and have conception of its surroundings. Agricultural environments, compared to indoor or city environments, are highly unstructured. In particular, the vehicle will have to deal with environments that are not at, but rather with steep slopes, and uneven terrain, which introduce vibrations that make classic dead reckoning systems unusable.
To overcome this limitation, in this thesis work I developed a multi GNSS antennas system to estimate position and orientation of the robot. Investigating how many antennas to have to use, with what technology and with what geometry to arrange them on the vehicle.
However, the GNSS system alone cannot be relied upon for localization, as it may not always be available. Therefore, the multi-GNSS systems fit into a larger SLAM algorithm that aims to recognize the presence of vine trees and through them recreate a map of the vineyard itself. Through the use of the latter, the vehicle will be able to locate itself within the vineyard even in the absence of the GNSS signal.
The introduced system was designed to be used on the Robotnik Summit-XL Steel and developed on ROS. For the testing of the system, I performed simulations on Gazebo, evaluating the performance of the complete system, but also its behavior in case of GNSS signal loss. Finally, I tested the algorithm on the real robot in a real outdoor environment.
Agriculture is one of the fields on which more research is being focusing in recent years, with a particular interest in making one of the heaviest workplaces for the human body more automated.
To enable a vehicle to move autonomously in an environment, it is essential that it be able to locate itself and have conception of its surroundings. Agricultural environments, compared to indoor or city environments, are highly unstructured. In particular, the vehicle will have to deal with environments that are not at, but rather with steep slopes, and uneven terrain, which introduce vibrations that make classic dead reckoning systems unusable.
To overcome this limitation, in this thesis work I developed a multi GNSS antennas system to estimate position and orientation of the robot. Investigating how many antennas to have to use, with what technology and with what geometry to arrange them on the vehicle.
However, the GNSS system alone cannot be relied upon for localization, as it may not always be available. Therefore, the multi-GNSS systems fit into a larger SLAM algorithm that aims to recognize the presence of vine trees and through them recreate a map of the vineyard itself. Through the use of the latter, the vehicle will be able to locate itself within the vineyard even in the absence of the GNSS signal.
The introduced system was designed to be used on the Robotnik Summit-XL Steel and developed on ROS. For the testing of the system, I performed simulations on Gazebo, evaluating the performance of the complete system, but also its behavior in case of GNSS signal loss. Finally, I tested the algorithm on the real robot in a real outdoor environment.
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