Algoritmo estocástico para la generación automática de trayectorias de un robot humanoide

Stochastic algorithm for automatic path planning of a humanoid robot

dc.creatorVillate, Cristian
dc.creatorPeña Cortes, Cesar Augusto
dc.creatorGualdron Guerrero, Oscar Eduardo
dc.date2019-02-12T01:39:10Z
dc.date2019-02-12T01:39:10Z
dc.date2018-01-01
dc.date.accessioned2023-10-03T19:40:50Z
dc.date.available2023-10-03T19:40:50Z
dc.identifierC. D. Villate Martínez, C. A. Peña Cortés y O. E. Gualdrón Guerrero, “Algoritmo estocástico para la generación automática de trayectorias de un robot humanoide,” INGE CUC, vol. 14, no. 1, pp. 30-40, 2018. DOI: http://doi.org/10.17981/ingecuc.14.1.2018.03
dc.identifierhttp://hdl.handle.net/11323/2400
dc.identifier2382-4700
dc.identifierCorporación Universidad de la Costa
dc.identifier0122-6517
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9171364
dc.descriptionIntroducción: La incorporación de sistemas de aprendizaje autónomos en la robótica permitirá la resolución de una gran cantidad de problemas. Uno de ellos es la marcha autónoma para el caso de los robots humanoides debido a la complejidad que tiene por la gran cantidad de variables que influyen en este proceso.Objetivo: Desarrollar algoritmos que generen marchas autónomas en un robot humanoide con varios grados de libertad.Metodología: El estudio inicia con el desarrollo de algoritmos estocásticos con pocas dimensiones; luego, se extiende a situaciones n-dimensionales. Posteriormente, se realizan pruebas en simulación, y, por último, las pruebas experimentales. Resultados: Se generó un algoritmo basado en el modelo físico del robot para crear las trayectorias de marcha estocásticamente.Se implementó un simulador que contempla las restricciones cinemáticas incluyendo colisiones para verificar los resultados. Adicionalmente, se realizaron cien pruebas experimentales donde se verificó el correcto funcionamiento de las trayectorias.Conclusiones: Se pudo corroborar que es posible crear un algoritmo estocástico que mezcla reglas determinantes y aleatorias para generar marchas automáticamente en robots humanoides, extendiendo conceptos generados en espacios bidimensionales y tridimensionales a coordenadas articulares n-dimensionales.
dc.descriptionIntroduction− The incorporation of an autonomous learning system in robotics will allow the resolution of a large number of problems. One is the autonomous march of the humanoid robots due to its complexity in the great number of variables regarding this process.Objective−Develop algorithms that generate autono-mous paths in a humanoid robot with various degrees of freedom. Methodology−The study begins with the develop-ment of stochastic algorithms with few dimensions. Then, it will be extended to n-dimensional situations. Afterwards, simulation tests will be carried out. And finally, the experimental tests are performed. Results− An algorithm was generated based on the physical model of the robot to create walking paths sto-chastically. A simulator that contemplates the kinematic constraints, including collisions, was implemented to ve-rify the results. In addition, one hundred experimental tests were done. With these tests, the correct operation of the trajectories was verified. Conclusions−It was verified that it is possible to crea-te a stochastic algorithm that mixes determinant and random rules to automatically generate paths in hu-manoid robots, hence, extending concepts generated in two-dimensional and three-dimensional spaces to n-di-mensional articulated coordinates.
dc.format11 páginas
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languagespa
dc.publisherCorporación Universidad de la Costa
dc.relationINGE CUC; Vol. 14, Núm. 1 (2018)
dc.relationINGE CUC
dc.relationINGE CUC
dc.relation[1] Z. Mohamed y G. Capi, “Development of a new mobile humanoid robot for assisting elderly people,” Procedia Engineering, vol. 41, no. Iris, pp. 345–351, 2012. DOI: 10.1016/j.proeng.2012.07.183. URL: http://dx.doi.org/10.1016/j.proeng.2012.07.183
dc.relation[2] G. Wiedebach et al., “Walking on partial footholds including line contacts with the humanoid robot atlas,” in IEEE-RAS International Conference on Humanoid Robots, pp. 1312–1319, 2016. DOI: 10.1109/HUMANOIDS.2016.7803439 URL: https://ieeexplore.ieee.org/document/7803439/
dc.relation[3] B. Ding, A. Plummer y P. Iravani, “Investigating Balancing Control of a Standing Bipedal Robot With Point Foot Contact,” IFAC-PapersOnLine, vol. 49, no. 21, pp. 403–408, 2016. DOI: http://dx.doi.org/10.1016/j.ifacol.2016.10.587
dc.relation[4] E. ; Ackerman y E. Guizzo, “Its Wheel-Leg Robot: ‘Best of Both Worlds,’” IEEE Spectrum, 2017. [En línea]. Disponible en: https://spectrum.ieee.org/automaton/robotics/humanoids/boston-dynamics-handle-robot. [Accessed: 20-Jul-2017] URL: https://spectrum.ieee.org/automaton/robotics/humanoids/ boston-dynamics-handle-robot
dc.relation[5] Y. Hosoda, S. Egawa, J. Tamamoto, K. Yamamoto, R. Nakamura y M. Togami, “Basic design of human-symbiotic robot EMIEW,” in IEEE International Conference on Intelligent Robots and Systems, no. c, pp. 5079–5084, 2006. DOI: 10.1109/IROS.2006.282596. URL: http://ieeexplore.ieee.org/document/4059227/
dc.relation[6] B. Henze, A. Dietrich y C. Ott, “An Approach to Combine Balancing with Hierarchical Whole-Body Control for Legged Humanoid Robots,” IEEE Robotics and Automation Letters, vol. 1, no. 2, pp. 700–707, 2016. DOI: 10.1109/LRA.2015.2512933. URL: http://ieeexplore.ieee.org/document/7368116/
dc.relation[7] Y. Liu, P. M. Wensing, J. P. Schmiedeler y D. E. Orin, “Terrain-Blind Humanoid Walking Based on a 3-D Actuated Dual-SLIP Model,” IEEE Robotics and Automation Letters, vol. 1, no. 2, pp. 1073–1080, 2016. DOI: 10.1109/LRA.2016.2530160. URL: http://ieeexplore.ieee.org/document/7407320/
dc.relation[8] M. W. Clearfield, “Learning to walk changes infants’ social interactions,” Infant Behavior and Development, vol. 34, no. 1, pp. 15–25, 2011. DOI: 10.1016/j.infbeh.2010.04.008. URL: http://dx.doi.org/10.1016/j.infbeh.2010.04.008
dc.relation[9] E. P. Shaw et al., “Measurement of attentional reserve and mental effort for cognitive workload assessment under various task demands during dual-task walking,” Biological Psychology, vol. 134, no. January, pp. 39–51, 2018. DOI: 10.1016/j.biopsycho.2018.01.009. URL: http://linkinghub.elsevier.com/retrieve/pii/S0301051118300413
dc.relation[10] K. B. Lee, H. Myung y J. H. Kim, “Online multiobjective evolutionary approach for navigation of humanoid robots,” IEEE Transactions on Industrial Electronics, vol. 62, no. 9, pp. 5586–5597, 2015. DOI: 10.1109/TIE.2015.2405901. URL: http://ieeexplore.ieee.org/document/7047860/
dc.relation[11] D. A. López, J. E. Hernández y C. A. Peña Cortes, “Advances in the control of bipedal platforms using the system,” Revista Colombiana de Tecnologías de Avanzada, vol. 2, pp. 117–124, 2013. DOI: https://doi. org/10.24054/16927257.v22.n22.2013.419. URL: http://revistas.unipamplona.edu.co/ojs_viceinves/index.php/RCTA/article/view/419
dc.relation[12] K. Teachasrisaksakul, Z. Q. Zhang, G. Z. Yang y B. Lo, “Imitation of dynamic walking with bsn for Humanoid robot,” IEEE Journal of Biomedical and Health Informatics, vol. 19, no. 3, pp. 794–802, 2015. DOI: 10.1109/JBHI.2015.2425221. URL: http://ieeexplore.ieee.org/document/7096914/
dc.relation[13] A. Barrientos, L. Peñin, C. Balager y R. Aracil, Fundamentos de Robótica, 2nd ed. Madrid: McGraw-Hill, 2007.
dc.relation[14] E. Luhta, “Walk Cycles,” in How to Cheat in Maya 2010, Boston: Focal Press, pp. 177–221, 2010. DOI: 10.1016/B978-0-240-81188-8.50008-4. URL: http://linkinghub.elsevier.com/retrieve/pii/B9780240811888500084
dc.relation[15] K. H. Koch, K. Mombaur y P. Soueres, “Optimizationbased walking generation for humanoid robot,” in IFAC Proceedings Volumes (IFAC-PapersOnline), vol. 45, no. 22, pp. 498–504, 2012. DOI: 10.3182/20120905-3-HR-2030.00189. URL: http://dx.doi.org/10.3182/20120905-3-HR-2030.00189
dc.relation[16] J. V. Nunez, A. Briseno, D. A. Rodriguez, J. M. Ibarra y V. M. Rodriguez, “Explicit Analytic Solution for Inverse Kinematics of Bioloid Humanoid Robot,” in 2012 Brazilian Robotics Symposium and Latin American Robotics Symposium, pp. 33–38, 2012. DOI: 10.1109/SBR-LARS.2012.62. URL: http://ieeexplore.ieee.org/document/6363315/
dc.relation[17] P. Wawrzynski, J. Mozaryn y J. Klimaszewski, “Robust estimation of walking robots velocity and tilt using proprioceptive sensors data fusion,” Robotics and Autonomous Systems, vol. 66, pp. 44–54, 2015. DOI: 10.1016/j.robot.2014.12.012. URL: http://dx.doi.org/10.1016/j.robot.2014.12.012
dc.relation[18] L. W. Tsai, Robot Analysis: The Mechanics of Serial and Parallel Manipulators. Maryland, USA: Wiley, 1999. URL: http://www.wiley.com/WileyCDA/WileyTitle/productCd-0471325937.html
dc.relation[19] J. Zhao, Z. Feng, F. Chu y N. Ma, “A Brief Introduction to Screw Theory,” in Advanced Theory of Constraint and Motion Analysis for Robot Mechanisms, pp. 29–79, 2014. DOI: 10.1016/B978-0-12-420162-0.00002-3. URL: http://www.sciencedirect.com/science/article/pii/B9780124201620000023
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dc.relationINGE CUC
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourceINGE CUC
dc.sourcehttps://revistascientificas.cuc.edu.co/ingecuc/article/view/1615
dc.subjectRobots humanoides
dc.subjectPlanificación de trayectorias
dc.subjectRobots autónomos
dc.subjectHumanoid robots
dc.subjectPath planning
dc.subjectAutonomous robot
dc.titleAlgoritmo estocástico para la generación automática de trayectorias de un robot humanoide
dc.titleStochastic algorithm for automatic path planning of a humanoid robot
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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