Synchronization controller for a 3-RRR parallel manipulator

A 3-RRR parallel manipulator has been well-known as a closed-loop kinematic chain mechanism in which the end-effector generally a moving platform is connected to the base by several independent actuators. Performance of the robot is decided by performances of the component actuators which are independently driven by tracking controllers without acknowledging information from each other. The platform performance is degraded if any actuator could not be driven well. Therefore, this paper aims to develop an advanced synchronization (SYNC) controller for position tracking of a 3-RRR parallel robot using three DC motor-driven actuators. The proposed control scheme consists of three sliding mode controllers (SMC) to drive the actuators and a supervisory controller named PID-neural network controller (PIDNNC) to compensate the synchronization errors due to system nonlinearities, uncertainties and external disturbances. A Lyapunov stability condition is added to the PIDNNC training mechanism to ensure the robust tracking performance of the manipulator. Numerical simulations have been performed under different working conditions to demonstrate the effectiveness of the suggested control approach

A New Computed Torque Control System with an Uncertain RBF Neural Network Controller for a 7-DOF Robot

A novel percutaneous puncture robot system is proposed in the paper. Increasing the surgical equipment precision to reduce the patient\u27s pain and the doctor\u27s operation difficulty to treat smaller tumors can increase the success rate of surgery. To attain this goal, an optimized Computed Torque Law (CTL) using a radial basis function (RBF) neural network controller (RCTL) is proposed to improve the direction and position accuracy. BRF neural network with an uncertain term (URBF) which is able to compensate the system error caused by the imprecision of the model is added in the RCTL system. At first, a 7-DOF robotic system is established. It consists of robotic arm and actuator control channels. Now, the RBF compensator is added to the CTL to adjust the robot arm to reduce the position and direction errors. The angle and velocity errors of the robot arm are compensated using the RBF controller. According to the Lyapunov theory, the accuracy of torque control system depends on path tracking errors, inertia of robot, dynamic parameters and disturbance of each joint. Compared to general CTL approaches, the precision of a 7-DOF robot could be improved by adjusting the RBF parameters

A framework for robotized teleoperated tasks

"Premio al mejor artículo presentado en ROBOT 2011" atorgat pel Grupo de Robótica, Visión y Control de la Universidad de Sevilla, la Universidad Pablo Olavide i el Centro Avanzado de Tecnologías Aeroespaciales.Teleoperation systems allow the extension of the human operator’s sensing and manipulative capability into a remote environment to perform tasks at a distance, but the time-delays in the communications affect the stability and transparency of such systems. This work presents a teleoperation framework in which some novel tools, such as nonlinear controllers, relational positioning techniques, haptic guiding and augmented reality, are used to increase the sensation of immersion of the human operator in the remote site. Experimental evidence supports the advantages of the proposed framework.Award-winningPostprint (published version

Combined Admittance Control With Type II Singularity Evasion for Parallel Robots Using Dynamic Movement Primitives

[EN] This article addresses a new way of generating compliant trajectories for control using movement primitives to allow physical human-robot interaction where parallel robots (PRs) are involved. PRs are suitable for tasks requiring precision and performance because of their robust behavior. However, two fundamental issues must be resolved to ensure safe operation: first, the force exerted on the human must be controlled and limited, and second, Type II singularities should be avoided to keep complete control of the robot. We offer a unified solution under the dynamic movement primitives (DMP) framework to tackle both tasks simultaneously. DMPs are used to get an abstract representation for movement generation and are involved in broad areas, such as imitation learning and movement recognition. For force control, we design an admittance controller intrinsically defined within the DMP structure, and subsequently, the Type II singularity evasion layer is added to the system. Both the admittance controller and the evader exploit the dynamic behavior of the DMP and its properties related to invariance and temporal coupling, and the whole system is deployed in a real PR meant for knee rehabilitation. The results show the capability of the system to perform safe rehabilitation exercises.This work was supported in part by the Fondo Europeo de Desarrollo Regional under Grant PID2021-125694OB-I00, in part by the Vicerrectorado de Investigacion de la Universitat Politecnica de Valencia under Grant PAID-11-21, and in part by the Ministerio de Universidades, Gobierno de Espana under Grant FPU18/05105.Escarabajal-Sánchez, RJ.; Pulloquinga-Zapata, J.; Valera Fernández, Á.; Mata Amela, V.; Vallés Miquel, M.; Castillo-García, FJ. (2023). Combined Admittance Control With Type II Singularity Evasion for Parallel Robots Using Dynamic Movement Primitives. IEEE Transactions on Robotics. 39(3):2224-2239. https://doi.org/10.1109/TRO.2023.32381362224223939

A laboratory breadboard system for dual-arm teleoperation

The computing architecture of a novel dual-arm teleoperation system is described. The novelty of this system is that: (1) the master arm is not a replica of the slave arm; it is unspecific to any manipulator and can be used for the control of various robot arms with software modifications; and (2) the force feedback to the general purpose master arm is derived from force-torque sensor data originating from the slave hand. The computing architecture of this breadboard system is a fully synchronized pipeline with unique methods for data handling, communication and mathematical transformations. The computing system is modular, thus inherently extendable. The local control loops at both sites operate at 100 Hz rate, and the end-to-end bilateral (force-reflecting) control loop operates at 200 Hz rate, each loop without interpolation. This provides high-fidelity control. This end-to-end system elevates teleoperation to a new level of capabilities via the use of sensors, microprocessors, novel electronics, and real-time graphics displays. A description is given of a graphic simulation system connected to the dual-arm teleoperation breadboard system. High-fidelity graphic simulation of a telerobot (called Phantom Robot) is used for preview and predictive displays for planning and for real-time control under several seconds communication time delay conditions. High fidelity graphic simulation is obtained by using appropriate calibration techniques

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

URK: Utah robot kit - a three-link robot prototype

Journal ArticleIn this paper we will present the stages of designing and building a three-link robot manipulator prototype that was built as part of a research project for establishing a prototyping environment for robot manipulators. Building this robot enabled us determine the required subsystems and interfaces to build the prototyping environment, and provided hands-on experience for the real problems and difficulties that we would like to address and solve using this environment. Also, this robot will be used as an educational tool in robotics and control classes