Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Design and manufacturing of a prototype passive-active suspension system for a mobile robot used in greenhouse environments(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-06) Moreno Zubler, Marco Stefan; Santana Díaz, Alfredo; puemcuervo, emimayorquin; Farfán Cabrera, Leonardo Israel; Pérez Santiago, Rogelio; Bustamante Bello, Martín Rogelio; School of Engineering and Sciences; Campus Ciudad de México; Reséndiz Calderón, César DavidOne of the main problems in modern agriculture is the need to increase pro duction to meet a growing population’s demand while dealing with limited resources and a reduced labor force. The use of mobile robots in agriculture has the potential to revolutionize traditional farming, increasing productivity and efficiency. Mobile robots in agriculture are already used to automate tasks like weeding, spraying and harvesting. This thesis presents the design and manufacture of a prototype passive-active suspension system that provides sufficient mobility and stability for a mobile robot used in green-house environments. The engineering design method was followed, starting from the generation of design concepts and culminating in the manufacturing of a final design. Computer-aided manufacturing techniques were employed to produce the components of the mobile robot. A combination of computer numerical control (CNC) and conventional milling and turning machines were utilized for the manufacture. The robot is specifically designed for green house applications and features a passive-active suspension system capable of navigating various types of terrain. To achieve mobility, the robot utilizes a skid-steering mechanism, employing four independently powered in-wheel motors. The robot’s passive suspension incorporates a rocker type suspension that enables the robot to overcome obstacles up to a height of 200 mm while adapting to different terrain conditions. The designed suspension system helps distribute the load evenly across the wheels, improving traction and stability. Furthermore, the robot’s active suspension includes a variable height mechanism that allows adjusting the ground clearance between 343 mm and 541 mm, giving the robot the ability to adapt to different terrain types and obstacles. The variable height system is also capable of modifying the roll angle of the vehicle, this can be used for the compensation of the roll angle when driving on inclined terrain. The maximum roll angle compensation achievable is 12.7 ◦ . In conclusion, the designed systems enhance the mobility of the mobile robot, enabling it to navigate through unstructured environments commonly encountered in the agricultural sector.
- Sensor data fusion for a mobile robot using a neural network algorithm(Instituto Tecnológico y de Estudios Superiores de Monterrey) Barreto Cubero, Andrés Javier; GOMEZ ESPINOSA, ALFONSO; 57957; Gómez Espinosa, Alfonso; puelquio, emipsanchez; Cuan Urquizo, Enrique; Cruz Ramírez, Sergio Rolando; Escuela de Ingeniería y Ciencias; Campus Monterrey; Escobedo Cabello, Jesús ArturoMobile robots must be capable to obtain an accurate map of their surroundings and move within it. To detect different materials that might be undetectable to one sensor but not others it is necessary to have at least two sensors, with this is possible to generate a 2D occupancy map that is as close to reality as possible. In this thesis, an artificial neural network is used to fuse data from a tri-sensor (Intel RealSense Stereo Camera, 2D 360° LiDAR-Light Detection and Ranging Sensor and an HC-SR04 Ultrasonic Sensor) setup capable of detecting glass, polished metals, brick walls, wooden panels and other materials typically found in indoor environments. When a map is to be compiled out of different sensor’s data, it is necessary to implement a preprocessing scheme to filter all the outliers in the data for each sensor. Then, run a data fusion algorithm to integrate all the information into a single, more accurate 2D map that considers all sensor’s information. The Robotis Turtlebot 3 Waffle Pi robot is used as an experimental platform along with Robotic Operating System as the main Human Machine Interface to implement the algorithms. Test results show that with the fusion algorithm implemented, it is possible to detect glass and other obstacles invisible to the LiDAR with an estimated root-mean-square error of 4 cm with multiple sensor configurations.