Desplazamiento seguro y autónomo en entornos poco estructurados basado en técnicas de SLAM para un vehículo aéreo no tripulado
La pregunta: ¿Dónde me encuentro? Es una de las preguntas más básicas realizadas por la humanidad a lo largo del tiempo. El surgimiento, avance y desarrollo dentro del área de la robótica, tal como es la robótica autónoma, se ha convertido en uno de los logros más exitosos alcanzado al tiempo presen...
- Autores:
-
Salgado Luque, Jorge Alejandro
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Universidad Militar Nueva Granada
- Repositorio:
- Repositorio UMNG
- Idioma:
- spa
- OAI Identifier:
- oai:repository.unimilitar.edu.co:10654/18049
- Acceso en línea:
- http://hdl.handle.net/10654/18049
- Palabra clave:
- AVIONES SIN PILOTO
ROBOTICA
SLAM
Navigation
Pioneer3DX
DJI Matrice 600 Pro
ROS
Gmapping
Laser Sensor
Spatial Advanced Navigation
ZED
move_base
robot_localization
slam_gmapping
Hokuyo
SLAM
Navegación
Pioneer3DX
DJI Matrice 600 Pro
ROS
Gmapping
Sensor Láser
Spatial Advanced Navigation
ZED
move_base
robot_localization
slam_gmapping
Hokuyo
- Rights
- License
- Derechos Reservados - Universidad Militar Nueva Granada, 2018
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| dc.title.spa.fl_str_mv |
Desplazamiento seguro y autónomo en entornos poco estructurados basado en técnicas de SLAM para un vehículo aéreo no tripulado |
| dc.title.translated.spa.fl_str_mv |
Safe and autonomous displacement in unstructured environments based on SLAM Techniques for an unmanned aerial vehicle |
| title |
Desplazamiento seguro y autónomo en entornos poco estructurados basado en técnicas de SLAM para un vehículo aéreo no tripulado |
| spellingShingle |
Desplazamiento seguro y autónomo en entornos poco estructurados basado en técnicas de SLAM para un vehículo aéreo no tripulado AVIONES SIN PILOTO ROBOTICA SLAM Navigation Pioneer3DX DJI Matrice 600 Pro ROS Gmapping Laser Sensor Spatial Advanced Navigation ZED move_base robot_localization slam_gmapping Hokuyo SLAM Navegación Pioneer3DX DJI Matrice 600 Pro ROS Gmapping Sensor Láser Spatial Advanced Navigation ZED move_base robot_localization slam_gmapping Hokuyo |
| title_short |
Desplazamiento seguro y autónomo en entornos poco estructurados basado en técnicas de SLAM para un vehículo aéreo no tripulado |
| title_full |
Desplazamiento seguro y autónomo en entornos poco estructurados basado en técnicas de SLAM para un vehículo aéreo no tripulado |
| title_fullStr |
Desplazamiento seguro y autónomo en entornos poco estructurados basado en técnicas de SLAM para un vehículo aéreo no tripulado |
| title_full_unstemmed |
Desplazamiento seguro y autónomo en entornos poco estructurados basado en técnicas de SLAM para un vehículo aéreo no tripulado |
| title_sort |
Desplazamiento seguro y autónomo en entornos poco estructurados basado en técnicas de SLAM para un vehículo aéreo no tripulado |
| dc.creator.fl_str_mv |
Salgado Luque, Jorge Alejandro |
| dc.contributor.advisor.spa.fl_str_mv |
Solaque Guzmán, Leonardo Enrique |
| dc.contributor.author.spa.fl_str_mv |
Salgado Luque, Jorge Alejandro |
| dc.contributor.other.spa.fl_str_mv |
Sánchez, Guillermo Rondón Cárdenas, Daniel Santiago |
| dc.subject.lemb.spa.fl_str_mv |
AVIONES SIN PILOTO ROBOTICA |
| topic |
AVIONES SIN PILOTO ROBOTICA SLAM Navigation Pioneer3DX DJI Matrice 600 Pro ROS Gmapping Laser Sensor Spatial Advanced Navigation ZED move_base robot_localization slam_gmapping Hokuyo SLAM Navegación Pioneer3DX DJI Matrice 600 Pro ROS Gmapping Sensor Láser Spatial Advanced Navigation ZED move_base robot_localization slam_gmapping Hokuyo |
| dc.subject.keywords.spa.fl_str_mv |
SLAM Navigation Pioneer3DX DJI Matrice 600 Pro ROS Gmapping Laser Sensor Spatial Advanced Navigation ZED move_base robot_localization slam_gmapping Hokuyo |
| dc.subject.proposal.spa.fl_str_mv |
SLAM Navegación Pioneer3DX DJI Matrice 600 Pro ROS Gmapping Sensor Láser Spatial Advanced Navigation ZED move_base robot_localization slam_gmapping Hokuyo |
| description |
La pregunta: ¿Dónde me encuentro? Es una de las preguntas más básicas realizadas por la humanidad a lo largo del tiempo. El surgimiento, avance y desarrollo dentro del área de la robótica, tal como es la robótica autónoma, se ha convertido en uno de los logros más exitosos alcanzado al tiempo presente; siendo que un robot por sí mismo pueda realizar tareas y aún comportamientos sin ninguna intervención humana es algo pensado, antiguamente, sólo en la ciencia ficción. La cuestión: ¿Dónde me encuentro? Se ha transformado en: ¿Cómo sabe una unidad robótica dónde se encuentra? Y para responder este interrogante el objetivo de muchas de las investigaciones en este campo de estudio ha sido el de encontrar la mejor técnica o solución que permite al robot darle capacidades de navegación en un modo autónomo. En el contexto de desplazamiento de las plataformas robóticas aéreas en entornos poco estructurados, es decir, donde no se conoce el mapa a priori; es primordial la seguridad de los agentes actuantes dentro del área de trabajo, más aún, cuando se trata de vidas humanas. Para esto, es necesario un robot aéreo capacitado con una buena instrumentación, el cual debe ser capaz de sortear situaciones de riesgo cuando realiza la ejecución de sus labores para llegar a un éxito de misión. El presente trabajo pretende abordar una estrategia de Mapeo y Localización Simultánea dentro del sistema operativo robótico ROS, de manera que una plataforma robótica terrestre (Pioneer3DX) o aérea (DJI Matrice 600 Pro) pueda desplazarse en un espacio tridimensional con capacidad de auto-localizarse y tomar decisiones en tiempo real para no colisionar con obstáculos al dirigirse a un punto de misión definido. Se logra ejecutar el algoritmo de SLAM del paquete slam_gmapping con interfaz ROS y visualización en RVIZ con la plataforma robótica terrestre y aérea; con la capacidad de evadir obstáculos estáticos y dinámicos. |
| publishDate |
2018 |
| dc.date.accessioned.none.fl_str_mv |
2018-10-05T19:46:44Z 2019-12-26T22:10:50Z |
| dc.date.available.none.fl_str_mv |
2018-10-05T19:46:44Z 2019-12-26T22:10:50Z |
| dc.date.issued.none.fl_str_mv |
2018-08-16 |
| dc.type.spa.fl_str_mv |
info:eu-repo/semantics/bachelorThesis |
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http://purl.org/coar/resource_type/c_7a1f |
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Trabajo de grado |
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Text |
| dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/10654/18049 |
| url |
http://hdl.handle.net/10654/18049 |
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spa |
| language |
spa |
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IEEE Robotics Automation Magazine, 13, 99-110. doi:10.1109/MRA.2006.1638022 Fernández, E., Romero, A. M., Crespo, L. S., & Martinez, A. (2015). Learning ROS for Robotics Programming: Your One-stop Guide to the Robot Operating System. Packt Publishing. Fox, D., Burgard, W., & Thrun, S. (1997, 3). The dynamic window approach to collision avoidance. IEEE Robotics Automation Magazine, 4, 23-33. doi:10.1109/100.580977 Fraundorfer, F., & Scaramuzza, D. (2012, 6). Visual Odometry : Part II: Matching, Robustness, Optimization, and Applications. IEEE Robotics Automation Magazine, 19, 78-90. doi:10.1109/MRA.2012.2182810 Gerkey, B., Grisetti, G., Stachniss, C., & Burgard., W. (2012, 3 10). OpenSLAM: Gmapping and ROS gmapping. (B. Gerkey, Ed.) Retrieved from https://openslam-org.github.io/gmapping.html, https://github.com/ros-perception/slam_gmapping. Gómez, D. H. (2015). Desarrollo de una técnica SLAM para ambientes dinámicos tridimensionales. mathesis, Universidad Nacional de Colombia. 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Retrieved from http://robots.mobilerobots.com/wiki/ARIA, https://github.com/amor-ros-pkg/rosaria, http://wiki.ros.org/ROSARIA Kalman, R. E. (1960). A New Approach to Linear Filtering And Prediction Problems. ASME Journal of Basic Engineering Khairuddin, A. R., Talib, M. S., & Haron, H. (2015, 11). Review on simultaneous localization and mapping (SLAM). 2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE), (pp. 85-90). doi:10.1109/ICCSCE.2015.7482163 Kim, C., Sakthivel, R., & Chung, W. K. (2007, 4). Unscented FastSLAM: A Robust Algorithm for the Simultaneous Localization and Mapping Problem. Proceedings 2007 IEEE International Conference on Robotics and Automation, (pp. 2439-2445). doi:10.1109/ROBOT.2007.363685 Kohlbrecher, S., & Meyer, J. (2012, 1 31). Tutorials: How to set up hector_slam for your robot. (J. M. Stefan Kohlbrecher, Ed.) Retrieved from http://wiki.ros.org/hector_slam/Tutorials/SettingUpForYourRobot Kohlbrecher, S., Meyer, J., Stryk, O., & Klingauf, U. (2011, 11). A Flexible and Scalable SLAM System with Full 3D Motion Estimation. Proc. IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR). Koubaa, A. (2016). Robot Operating System (ROS): The Complete Reference. Springer International Publishing. Lemoine, F. G., & Center, G. S. (1998). The development of the joint NASA GSFC and the National Imagery and Mapping Agency (NIMA) Geopotential Model EGM96. National Aeronautics and Space Administration, Goddard Space Flight Center. Leonard, J. J., & Durrant-Whyte, H. F. (1991, 6). Mobile robot localization by tracking geometric beacons. IEEE Transactions on Robotics and Automation, 7, 376-382. doi:10.1109/70.88147 Li, J., Cheng, L., Wu, H., Xiong, L., & Wang, D. (2012, 6). An overview of the simultaneous localization and mapping on mobile robot. 2012 Proceedings of International Conference on Modelling, Identification and Control, (pp. 358-364). Lowry, S., Sünderhauf, N., Newman, P., Leonard, J. J., Cox, D., Corke, P., & Milford, M. J. (2016, 2). Visual Place Recognition: A Survey. IEEE Transactions on Robotics, 32, 1-19. doi:10.1109/TRO.2015.2496823 Marder-Eppstein, E. (2016, 3 26). Package Summary - move_base. (D. V. Lu, M. Ferguson, & A. Hoy, Eds.) Retrieved from http://wiki.ros.org/move_base Marder-Eppstein, E., Berger, E., Foote, T., Gerkey, B., & Konolige, K. (2010, 5). The Office Marathon: Robust navigation in an indoor office environment. 2010 IEEE International Conference on Robotics and Automation, (pp. 300-307). doi:10.1109/ROBOT.2010.5509725 Maybeck, P. S. (1982). Stochastic Models, Estimation, and Control. Academic Press. Meeussen, W. (2010, 10 28). REP-105: Coordinate Frames for Mobile Platforms. (W. Meeussen, Ed.) 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Metric monocular SLAM and colour segmentation for multiple obstacle avoidance in autonomous flight. 2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS), (pp. 234-239). doi:10.1109/RED-UAS.2017.8101672 Saeedi, S., Trentini, M., Seto, M., & Li, H. (2016, 1). Multiple-Robot Simultaneous Localization and Mapping: A Review. J. Field Robot., 33, 3-46. doi:10.1002/rob.21620 Scaramuzza, D., & Fraundorfer, F. (2011, 12). Visual Odometry [Tutorial]. IEEE Robotics Automation Magazine, 18, 80-92. doi:10.1109/MRA.2011.943233 Siciliano, B., & Khatib, O. (2008). Springer Handbook of Robotics. Springer Berlin Heidelberg. Siegwart, R., & Nourbakhsh, I. R. (2004). Introduction to Autonomous Mobile Robots. Bradford Book. Smith, R. C., & Cheeseman, P. (1986). On the Representation and Estimation of Spatial Uncertainty. The International Journal of Robotics Research, 5, 56-68. doi:10.1177/027836498600500404 Sorenson, H. W. (1970, 7). Least-squares estimation: from Gauss to Kalman. IEEE Spectrum, 7, 63-68. doi:10.1109/MSPEC.1970.5213471 StereoLabs, I. (2016, 10 24). ZED Documentation. (I. StereoLabs, Ed.) Retrieved from https://www.stereolabs.com/zed/ StereoLabs, I. (2016, 10 24). ZED ROS Wrapper. (I. StereoLabs, Ed.) Retrieved from https://docs.stereolabs.com/integrations/ros/zed_node/, https://github.com/stereolabs/zed-ros-wrapper/releases Tully Foote, M. P. (2010, 10 8). REP-103: Standard Units of Measure and Coordinate Conventions. (M. P. Tully Foote, Ed.) Retrieved from http://www.ros.org/reps/rep-0103.html Unités, T. C. (2006). The International System of Units (SI). Comité International des Poids Mesures, 38, 95. Retrieved from https://www.bipm.org/en/publications/si-brochure/ Welch, G., & Bishop, G. (1995). An Introduction to the Kalman Filter. University of North Carolina at Chapel Hill. Chapel Hill, NC, USA: University of North Carolina at Chapel Hill. Zaman, S., Slany, W., & Steinbauer, G. (2011, 4). ROS-based mapping, localization and autonomous navigation using a Pioneer 3-DX robot and their relevant issues. 2011 Saudi International Electronics, Communications and Photonics Conference (SIECPC), (pp. 1-5). doi:10.1109/SIECPC.2011.5876943 |
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Solaque Guzmán, Leonardo EnriqueSalgado Luque, Jorge AlejandroIngeniero en MecatrónicaSánchez, GuillermoRondón Cárdenas, Daniel SantiagoCalle 1002018-10-05T19:46:44Z2019-12-26T22:10:50Z2018-10-05T19:46:44Z2019-12-26T22:10:50Z2018-08-16http://hdl.handle.net/10654/18049La pregunta: ¿Dónde me encuentro? Es una de las preguntas más básicas realizadas por la humanidad a lo largo del tiempo. El surgimiento, avance y desarrollo dentro del área de la robótica, tal como es la robótica autónoma, se ha convertido en uno de los logros más exitosos alcanzado al tiempo presente; siendo que un robot por sí mismo pueda realizar tareas y aún comportamientos sin ninguna intervención humana es algo pensado, antiguamente, sólo en la ciencia ficción. La cuestión: ¿Dónde me encuentro? Se ha transformado en: ¿Cómo sabe una unidad robótica dónde se encuentra? Y para responder este interrogante el objetivo de muchas de las investigaciones en este campo de estudio ha sido el de encontrar la mejor técnica o solución que permite al robot darle capacidades de navegación en un modo autónomo. En el contexto de desplazamiento de las plataformas robóticas aéreas en entornos poco estructurados, es decir, donde no se conoce el mapa a priori; es primordial la seguridad de los agentes actuantes dentro del área de trabajo, más aún, cuando se trata de vidas humanas. Para esto, es necesario un robot aéreo capacitado con una buena instrumentación, el cual debe ser capaz de sortear situaciones de riesgo cuando realiza la ejecución de sus labores para llegar a un éxito de misión. El presente trabajo pretende abordar una estrategia de Mapeo y Localización Simultánea dentro del sistema operativo robótico ROS, de manera que una plataforma robótica terrestre (Pioneer3DX) o aérea (DJI Matrice 600 Pro) pueda desplazarse en un espacio tridimensional con capacidad de auto-localizarse y tomar decisiones en tiempo real para no colisionar con obstáculos al dirigirse a un punto de misión definido. Se logra ejecutar el algoritmo de SLAM del paquete slam_gmapping con interfaz ROS y visualización en RVIZ con la plataforma robótica terrestre y aérea; con la capacidad de evadir obstáculos estáticos y dinámicos.1. Introducción 1.1 Planteamiento del Problema 1.2 Objetivos 1.2.1 Objetivo General 1.2.2 Objetivos Específicos 1.3 Justificación 1.4 Metodología 2 Marco Teórico 2.1 Mapeo y Localización Simultánea (SLAM) 2.1.1 Características de SLAM 2.1.2 Formulación y estructura del problema de SLAM 2.2 Modelo Cinemático - Plataforma Robótica Móvil Terrestre 2.3 Modelo Cinemático - Plataforma Robótica Móvil Aérea 2.4 Filtro de Kalman 2.4.1 Proceso de Estimación 2.4.2 Introducción de orígenes computacionales del Filtro de Kalman 2.4.3 Algoritmo del Filtro Discreto de Kalman 2.5 Filtro de Kalman Extendido (EKF) 2.5.1 Orígenes Computacionales del Filtro de Kalman Extendido 2.6 Filtro de Partículas Rao-Blackwellized 3 Software 3.1 Robotic Operating System (ROS) 3.2 ROS Enhancement Proposals (REPs) 3.2.1 REP-103 3.2.2 REP-105 3.3 Paquetes de ROS Utilizados 3.3.1 SLAM con Gmapping (slam_gmapping) 3.3.2 Move Base 3.3.3 Robot Localization 3.3.4 DJI SDK 3.3.5 ZED ROS Wrapper 3.3.6 URG Node 3.3.7 Advanced Navigation Driver 3.3.8 RosAria 4 Hardware 4.1 Cámara Estereoscópica - ZED 4.2 Sensor Láser – Hokuyo 4.3 Spatial Advanced Navigation 4.4 Plataforma Robótica Móvil Terrestre – Pioneer3DX 4.5 Plataforma Robótica Móvil Aérea - DJI Matrice 600 Pro 4.6 Sistema de Procesamiento – AlienWare Alpha 4.7 Sistema de Procesamiento – Dell Portátil 5 Simulaciones 6 Resultados Experimentales 6.1 Plataforma Terrestre 6.2 Plataforma Aérea 7 Conclusiones y Trabajos Futuros 8 AnexosThe question: Where am I? It is one of the most basic questions asked by humanity over time. The emergence, advancement and development within the area of robotics, such as autonomous robotics, has become one of the most successful achievements reached at the time; Being that a robot by itself can perform tasks and even behaviors without any human intervention is something thought, formerly, only in science fiction. The question: Where am I? It has been transformed into: How does a robotic unit know where it is? And to answer this question the objective of many of the investigations in this field of study has been to find the best technique or solution that allows the robot to give navigation capabilities in an autonomous mode. In the context of the displacement of aerial robotic platforms in unstructured environments, that is, where the a priori map is not known; the safety of the agents acting within the workspace area is paramount, especially when it comes to human lives. For this, a trained aerial robot with a good instrumentation is necessary, which must be able to overcome situations of risk when it carries out its tasks to reach a mission success. This paper aims to address a strategy of simultaneous location and mapping within the robotic operating system ROS, so that a terrestrial robot platform (Pioneer3DX) or aerial (DJI Matrice 600 Pro) can move in a three-dimensional space with the ability to auto-locate and make decisions in real time so that do not collide with obstacles when going to a defined mission target. It is possible to execute the SLAM algorithm of the slam_gmapping package with ROS interface and visualization in RVIZ with the terrestrial and aerial robotic platform; with the ability to evade static and dynamic obstacles.Pregradoapplication/pdfspaDerechos Reservados - Universidad Militar Nueva Granada, 2018https://creativecommons.org/licenses/by-nc-nd/2.5/co/Atribución-NoComercial-SinDerivadashttp://purl.org/coar/access_right/c_abf2Desplazamiento seguro y autónomo en entornos poco estructurados basado en técnicas de SLAM para un vehículo aéreo no tripuladoSafe and autonomous displacement in unstructured environments based on SLAM Techniques for an unmanned aerial vehicleinfo:eu-repo/semantics/bachelorThesisTrabajo de gradoTexthttp://purl.org/coar/resource_type/c_7a1fAVIONES SIN PILOTOROBOTICASLAMNavigationPioneer3DXDJI Matrice 600 ProROSGmappingLaser SensorSpatial Advanced NavigationZEDmove_baserobot_localizationslam_gmappingHokuyoSLAMNavegaciónPioneer3DXDJI Matrice 600 ProROSGmappingSensor LáserSpatial Advanced NavigationZEDmove_baserobot_localizationslam_gmappingHokuyoFacultad de IngenieríadIngeniería en MecatrónicaIngeniería - Ingeniería en MecatrónicaUniversidad Militar Nueva GranadaAit-Jellal, R., & Zell, A. 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ROS-based mapping, localization and autonomous navigation using a Pioneer 3-DX robot and their relevant issues. 2011 Saudi International Electronics, Communications and Photonics Conference (SIECPC), (pp. 1-5). doi:10.1109/SIECPC.2011.5876943THUMBNAILSalgadoLuque_JorgeAlejandro_2018.pdf.jpgIM Thumbnailimage/jpeg5035http://repository.unimilitar.edu.co/bitstream/10654/18049/1/SalgadoLuque_JorgeAlejandro_2018.pdf.jpg52f13948a39e7e121dfe7fd0f34577adMD51ORIGINALSalgadoLuque_JorgeAlejandro_2018.pdfTesisapplication/pdf2390069http://repository.unimilitar.edu.co/bitstream/10654/18049/2/SalgadoLuque_JorgeAlejandro_2018.pdfb69658f7e794aab83d9f6178658f8018MD52LICENSElicense.txttext/plain2915http://repository.unimilitar.edu.co/bitstream/10654/18049/3/license.txt755421b5a8b45ce61d1a5793576f9a78MD5310654/18049oai:repository.unimilitar.edu.co:10654/180492020-06-30 13:06:53.107Repositorio Institucional UMNGbibliodigital@unimilitar.edu.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 |