Trajectory planning for industrial robot using genetic algorithms

En las últimas décadas, debido la importancia de sus aplicaciones, se han propuesto muchas investigaciones sobre la planificación de caminos y trayectorias para los manipuladores, algunos de los ámbitos en los que pueden encontrarse ejemplos de aplicación son; la robótica industrial, sistemas autónomos, creación de prototipos virtuales y diseño de fármacos asistido por ordenador. Por otro lado, los algoritmos evolutivos se han aplicado en muchos campos, lo que motiva el interés del autor por investigar sobre su aplicación a la planificación de caminos y trayectorias en robots industriales. En este trabajo se ha llevado a cabo una búsqueda exhaustiva de la literatura existente relacionada con la tesis, que ha servido para crear una completa base de datos utilizada para realizar un examen detallado de la evolución histórica desde sus orígenes al estado actual de la técnica y las últimas tendencias. Esta tesis presenta una nueva metodología que utiliza algoritmos genéticos para desarrollar y evaluar técnicas para la planificación de caminos y trayectorias. El conocimiento de problemas específicos y el conocimiento heurístico se incorporan a la codificación, la evaluación y los operadores genéticos del algoritmo. Esta metodología introduce nuevos enfoques con el objetivo de resolver el problema de la planificación de caminos y la planificación de trayectorias para sistemas robóticos industriales que operan en entornos 3D con obstáculos estáticos, y que ha llevado a la creación de dos algoritmos (de alguna manera similares, con algunas variaciones), que son capaces de resolver los problemas de planificación mencionados. El modelado de los obstáculos se ha realizado mediante el uso de combinaciones de objetos geométricos simples (esferas, cilindros, y los planos), de modo que se obtiene un algoritmo eficiente para la prevención de colisiones. El algoritmo de planificación de caminos se basa en técnicas de optimización globales, usando algoritmos genéticos para minimizar una función objetivo considerando restricciones para evitar las colisiones con los obstáculos. El camino está compuesto de configuraciones adyacentes obtenidas mediante una técnica de optimización construida con algoritmos genéticos, buscando minimizar una función multiobjetivo donde intervienen la distancia entre los puntos significativos de las dos configuraciones adyacentes, así como la distancia desde los puntos de la configuración actual a la final. El planteamiento del problema mediante algoritmos genéticos requiere de una modelización acorde al procedimiento, definiendo los individuos y operadores capaces de proporcionar soluciones eficientes para el problema.Abu-Dakka, FJM. (2011). Trajectory planning for industrial robot using genetic algorithms [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10294Palanci

Development of lower-limb rehabilitation exercises using 3-PRS Parallel Robot and Dynamic Movement Primitives

[EN] The design of rehabilitation exercises applied to sprained ankles requires extreme caution, regarding the trajectories and the speed of the movements that will affect the patient. This paper presents a technique that allows a 3-PRS parallel robot to control such exercises, consisting of dorsi/plantar flexion and inversion/eversion ankle movements. The work includes a position control scheme for the parallel robot in order to follow a reference trajectory for each limb with the possibility of stopping the exercise in mid-execution without control loss. This stop may be motivated by the forces that the robot applies to the patient, acting like an alarm mechanism. The procedure introduced here is based on Dynamic Movement Primitives (DMPs).This work has been partially funded by FEDER-CICYT project with reference DPI2017-84201-R financed by Ministerio de Economía, Industria e Innovación (Spain).Escarabajal Sánchez, RJ.; Abu Dakka, FJM.; Pulloquinga Zapata, J.; Mata Amela, V.; Vallés Miquel, M.; Valera Fernández, Á. (2020). Development of lower-limb rehabilitation exercises using 3-PRS Parallel Robot and Dynamic Movement Primitives. Multidisciplinary Journal for Education, Social and Technological Sciences. 7(2):30-44. https://doi.org/10.4995/muse.2020.13907OJS304472Abu-Dakka, F. J., Valera, A., Escalera, J. A., Vallés, M., Mata, V., & Abderrahim, M. (2015). Trajectory adaptation and learning for ankle rehabilitation using a 3-PRS parallel robot. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9245, 483-494. https://doi.org/10.1007/978-3-319-22876-1_41Atkeson, C. G., Moore, A. W., & Schaal, S. (1997). Locally Weighted Learning. Artificial Intelligence Review, 11(1-5), 11-73. https://doi.org/10.1007/978-94-017-2053-3_2Brockett, C. L., & Chapman, G. J. (2016). Biomechanics of the ankle. Orthopaedics and Trauma, 30(3), 232-238. https://doi.org/10.1016/j.mporth.2016.04.015Dai, J. S., Zhao, T., & Nester, C. (2004). Sprained Ankle Physiotherapy Based Mechanism Synthesis and Stiffness Analysis of a Robotic Rehabilitation Device. Autonomous Robots, 16(2), 207-218. https://doi.org/10.1023/B:AURO.0000016866.80026.d7Díaz-Rodríguez, M., Mata, V., Valera, Á., & Page, Á. (2010). A methodology for dynamic parameters identification of 3-DOF parallel robots in terms of relevant parameters. Mechanism and Machine Theory, 45(9), 1337-1356. https://doi.org/10.1016/j.mechmachtheory.2010.04.007Díaz, I., Gil, J. J., & Sánchez, E. (2011). Lower-Limb Robotic Rehabilitation: Literature Review and Challenges. Journal of Robotics, 2011(i), 1-11. https://doi.org/10.1155/2011/759764Fanger, Y., Umlauft, J., & Hirche, S. (2016). Gaussian Processes for Dynamic Movement Primitives with application in knowledge-based cooperation. IEEE International Conference on Intelligent Robots and Systems, 2016-Novem, 3913-3919. https://doi.org/10.1109/IROS.2016.7759576Gosselin, C., & Angeles, J. (1990). Singularity Analysis of Closed-Loop Kinematic Chains. IEEE Transactions on Robotics and Automation, 6(3), 281-290. https://doi.org/10.1109/70.56660Hesse, S., & Uhlenbrock, D. (2000). A mechanized gait trainer for restoration of gait. Journal of Rehabilitation Research and Development, 37(6), 701-708.Ijspeert, A. J., Nakanishi, J., Hoffmann, H., Pastor, P., & Schaal, S. (2013). Dynamical movement primitives: Learning attractor models formotor behaviors. Neural Computation, 25(2), 328-373. https://doi.org/10.1162/NECO_a_00393Ijspeert, A. J., Nakanishi, J., & Schaal, S. (2002). Movement imitation with nonlinear dynamical systems in humanoid robots. Proceedings - IEEE International Conference on Robotics and Automation, 2, 1398-1403. https://doi.org/10.1109/ROBOT.2002.1014739Liu, G., Gao, J., Yue, H., Zhang, X., & Lu, G. (2006). Design and kinematics simulation of parallel robots for ankle rehabilitation. 2006 IEEE International Conference on Mechatronics and Automation, ICMA 2006, 2006, 1109-1113. https://doi.org/10.1109/ICMA.2006.257780Nakanishi, J., Morimoto, J., Endo, G., Cheng, G., Schaal, S., & Kawato, M. (2004). Learning from demonstration and adaptation of biped locomotion. Robotics and Autonomous Systems, 47(2-3), 79-91. https://doi.org/10.1016/j.robot.2004.03.003Nemec, B., & Ude, A. (2012). Action sequencing using dynamic movement primitives. Robotica, 30(5), 837-846. https://doi.org/10.1017/S0263574711001056Patel, Y. D., & George, P. M. (2012). Parallel Manipulators Applications-A Survey. Modern Mechanical Engineering, 02(03), 57-64. https://doi.org/10.4236/mme.2012.23008Paul, R. P. (1981). Robot Manipulators: Mathematics, Programming, and Control : the Computer Control of Robot Manipulators (p. 279).Reinkensmeyer, D. J., Aoyagi, D., Emken, J. L., Galvez, J. A., Ichinose, W., Kerdanyan, G., Maneekobkunwong, S., Minakata, K., Nessler, J. A., Weber, R., Roy, R. R., De Leon, R., Bobrow, J. E., Harkema, S. J., & Reggie Edgerton, V. (2006). Tools for understanding and optimizing robotic gait training. Journal of Rehabilitation Research and Development, 43(5), 657-670. https://doi.org/10.1682/JRRD.2005.04.0073Safran, M. R., Benedetti, R. S., Bartolozzi, A. R., & Mandelbaum, B. R. (1999). Lateral ankle sprains: A comprehensive review part 1: Etiology, pathoanatomy, histopathogenesis, and diagnosis. In Medicine and Science in Sports and Exercise (Vol. 31, Issue 7 SUPPL., pp. S429-S437).https://doi.org/10.1097/00005768-199907001-00004Saglia, J. A., Tsagarakis, N. G., Dai, J. S., & Caldwell, D. G. (2013). Control strategies for patient-assisted training using the ankle rehabilitation robot (ARBOT). IEEE/ASME Transactions on Mechatronics, 18(6), 1799-1808. https://doi.org/10.1109/TMECH.2012.2214228Schaal, S. (2006). Dynamic Movement Primitives -A Framework for Motor Control in Humans and Humanoid Robotics. In Adaptive Motion of Animals and Machines (pp. 261-280). https://doi.org/10.1007/4-431-31381-8_23Sui, P., Yao, L., Lin, Z., Yan, H., & Dai, J. S. (2009). Analysis and synthesis of ankle motion and rehabilitation robots. 2009 IEEE International Conference on Robotics and Biomimetics, ROBIO 2009, 3, 2533-2538. https://doi.org/10.1109/ROBIO.2009.5420487Tsoi, Y. H., Xie, S. Q., & Graham, A. E. (2009). Design, modeling and control of an ankle rehabilitation robot. Studies in Computational Intelligence, 177, 377-399. https://doi.org/10.1007/978-3-540-89933-4_18Vallés, M., Díaz-Rodrguez, M., Valera, Á., Mata, V., & Page, Á. (2012). Mechatronic development and dynamic control of a 3-dof parallel manipulator. Mechanics Based Design of Structures and Machines, 40(4), 434-452. https://doi.org/10.1080/15397734.2012.687292Xie, S. (2016). Advanced robotics for medical rehabilitation: current state of the art and recent advances. In Springer tracts in advanced robotics (Issue 108). https://doi.org/10.1007/978-3-319-19896-5Yoon, J., Ryu, J., & Lim, K. B. (2006). Reconfigurable ankle rehabilitation robot for various exercises. Journal of Robotic Systems, 22(SUPPL.), 15-33. https://doi.org/10.1002/rob.2015

Evolutionary Path Planning Algorithm for Industrial Robots

This paper proposed a new methodology to solve collision free path planning problem for industrial robot using genetic algorithms. The method poses an optimization problem that aims to minimize the significant points traveling distance of the robot. The behavior of more two operational parameters - the end effector traveling distance and computational time - are analyzed. This algorithm is able to obtain the solution for any industrial robot working in the complex environments, just it needs to choose a suitable significant points for that robot. An application example has been illustrated using robot Puma 560. © 2012 Copyright Taylor & Francis and The Robotics Society of Japan.This paper has been made possible by the funding from the Spanish Ministry of MINISTERIO DE CIENCIA E INNOVACION through the Project Research and Technological Development DPI2010-20814-C02-01.Abu-Dakka, FJM.; Valero Chuliá, FJ.; Mata Amela, V. (2012). Evolutionary Path Planning Algorithm for Industrial Robots. 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Advanced Robotics, 11(1), 33-56. doi:10.1163/156855397x00038Valero, F., Mata, V., & Besa, A. (2006). Trajectory planning in workspaces with obstacles taking into account the dynamic robot behaviour. Mechanism and Machine Theory, 41(5), 525-536. doi:10.1016/j.mechmachtheory.2005.08.002Kavraki, L. E., Svestka, P., Latombe, J.-C., & Overmars, M. H. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 12(4), 566-580. doi:10.1109/70.508439S. M. LaValle and J. Kuffner, Randomized kinodynamic planning, in:Proc. IEEE Int. Conf. on Robotics and Automation, Detroit, MI, pp. 473–479 (1999).F. Abu-Dakka, F. J. Valero, V. Mata and J. L. Suñer. Path planning optimization of industrial robots using genetic algorithm, in:Proc. 16th International Workshop on Robotics in Alpe-Adria-Danube Region, Ljubljana, Slovenia, pp. 424–429 (2007).Rubio, F., Valero, F., Sunyer, J., & Mata, V. (2009). 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Evolutionary indirect approach to solving trayectory planning problem for industrial robots

In this paper, an indirect method for trajectory planning for industrial robots has been addressed using an evolutionary algorithm. The algorithm is divided into three stages: (1) The acquisition of Adjacent Configurations (AC) for Path Planning subjected to kinematics, geometric and obstacle avoidance constraints. (2) The acquisition of a collision-free path between initial and goal robot configurations. This path consists of a set of ACs, and (3) The acquisition of a temporal history of the evolution for the robot joint coordinates, by minimizing the required time subjected to actuator limits. This algorithm has been evaluated by comparing the results with the direct procedures proposed by Rubio articles in 2009 and 2010.Abu-Dakka, FJM.; Rubio Montoya, FJ.; Valero Chuliá, FJ.; Mata Amela, V. (2013). Evolutionary indirect approach to solving trayectory planning problem for industrial robots. European Journal of Mechanics - A/Solids. 42:210-218. doi:10.1016/j.euromechsol.2013.05.007S2102184

Evolutionary algorithm to solve trajectory planning problem, with robot dynamics considerations

[EN] This paper presents a new genetic algorithm methodology to obtain a smooth trajectory planning for industrial robots in complex environments. This method aims to gradually create the collision free trajectory as the robot moves. The presented method deals with the uncertainties associated with the unknown of the kinematic properties of intermediate via points since they are generated as the algorithm evolves towards the solution. As well, the objective of this algorithm is to minimize the trajectory time, which guide the robot motion. As an application example, this algorithm is applied over robot Puma 560. Some numerical examples are provided in this paper to evaluate the functionality of the algorithm.This paper has been possible thanks to the funding of Spanish Education, Culture and Sport Ministry by means of the Researching and Technologi Development Project IDEMOV DPI2010-20814-C02-01Abu-Dakka, FJM.; Valero Chuliá, FJ.; Suñer Martinez, JL.; Mata Amela, V. (2012). Evolutionary algorithm to solve trajectory planning problem, with robot dynamics considerations. International journal of mechanics and control. 13(1):15-20. http://hdl.handle.net/10251/81012S152013

Comparing the efficiency of five algorithms applied to path planning for industrial robots

This article is (c) Emerald Group Publishing and permission has been granted for this version to appear here https://riunet.upv.es/. Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Group Publishing Limited.Purpose The purpose of this paper is to compare the quality and efficiency of five methods for solving the path planning problem of industrial robots in complex environments. Design/methodology/approach In total, five methods are presented for solving the path planning problem and certain working parameters have been monitored using each method. These working parameters are the distance travelled by the robot and the computational time needed to find a solution. A comparison of results has been analyzed. Findings After this study, it could be easy to know which of the proposed methods is most suitable for application in each case, depending on the parameter the user wants to optimize. The findings have been summarized in the conclusion section. Research limitations/implications The five techniques which have been developed yield good results in general. Practical implications The algorithms introduced are able to solve the path planning problem for any industrial robot working with obstacles. Social implications The path planning algorithms help robots perform their tasks in a more efficient way because the path followed has been optimized and therefore they help human beings work together with the robots in order to obtain the best results from them. Originality/value The paper shows which algorithm offers the best results, depending on the example the user has to solve and the parameter to be optimized.Rubio Montoya, FJ.; Abu-Dakka, FJM.; Valero Chuliá, FJ.; Mata Amela, V. (2012). Comparing the efficiency of five algorithms applied to path planning for industrial robots. Industrial Robot: An International Journal. 39(6):580-591. doi:10.1108/01439911211268787S58059139

Global economic burden of unmet surgical need for appendicitis

Background There is a substantial gap in provision of adequate surgical care in many low- and middle-income countries. This study aimed to identify the economic burden of unmet surgical need for the common condition of appendicitis. Methods Data on the incidence of appendicitis from 170 countries and two different approaches were used to estimate numbers of patients who do not receive surgery: as a fixed proportion of the total unmet surgical need per country (approach 1); and based on country income status (approach 2). Indirect costs with current levels of access and local quality, and those if quality were at the standards of high-income countries, were estimated. A human capital approach was applied, focusing on the economic burden resulting from premature death and absenteeism. Results Excess mortality was 4185 per 100 000 cases of appendicitis using approach 1 and 3448 per 100 000 using approach 2. The economic burden of continuing current levels of access and local quality was US $92 492 million using approach 1 and$73 141 million using approach 2. The economic burden of not providing surgical care to the standards of high-income countries was $95 004 million using approach 1 and$75 666 million using approach 2. The largest share of these costs resulted from premature death (97.7 per cent) and lack of access (97.0 per cent) in contrast to lack of quality. Conclusion For a comparatively non-complex emergency condition such as appendicitis, increasing access to care should be prioritized. Although improving quality of care should not be neglected, increasing provision of care at current standards could reduce societal costs substantially