Cellular Decomposition for Non-repetitive Coverage Task with Minimum Discontinuities

A mechanism to derive non-repetitive coverage path solutions with a proven minimal number of discontinuities is proposed in this work, with the aim to avoid unnecessary, costly end effector lift-offs for manipulators. The problem is motivated by the automatic polishing of an object. Due to the non-bijective mapping between the workspace and the joint-space, a continuous coverage path in the workspace may easily be truncated in the joint-space, incuring undesirable end effector lift-offs. Inversely, there may be multiple configuration choices to cover the same point of a coverage path through the solution of the Inverse Kinematics. The solution departs from the conventional local optimisation of the coverage path shape in task space, or choosing appropriate but possibly disconnected configurations, to instead explicitly explore the leaast number of discontinuous motions through the analysis of the structure of valid configurations in joint-space. The two novel contributions of this paper include proof that the least number of path discontinuities is predicated on the surrounding environment, independent from the choice of the actual coverage path; thus has a minimum. And an efficient finite cellular decomposition method to optimally divide the workspace into the minimum number of cells, each traversable without discontinuties by any arbitrary coverage path within. Extensive simulation examples and real-world results on a 5 DoF manipulator are presented to prove the validity of the proposed strategy in realistic settings

Advanced mathematical methods for collaborative robotics

Gracia Calandin, LI.; Perez-Vidal, C.; Valls-Miro, J. (2018). Advanced mathematical methods for collaborative robotics. Mathematical Problems in Engineering. 2018. https://doi.org/10.1155/2018/1605817S201

Human-robot cooperation for robust surface treatment using non-conventional sliding mode control

[EN] This work presents a human-robot closely collaborative solution to cooperatively perform surface treatment tasks such as polishing, grinding, deburring, etc. The method considers two force sensors attached to the manipulator end-effector and tool: one sensor is used to properly accomplish the surface treatment task, while the second one is used by the operator to guide the robot tool. The proposed scheme is based on task priority and adaptive non-conventional sliding mode control. The applicability of the proposed approach is substantiated by experimental results using a redundant 7R manipulator: the Sawyer cobot.This work was supported in part by the Spanish Government under the project DPI2017-87656-C2-1-R and the Generalitat Valenciana under Grants VALi + d APOSTD/2016/044 and APOSTD/2017/055.Solanes Galbis, JE.; Gracia Calandin, LI.; Muñoz-Benavent, P.; Valls Miro, J.; Girbés, V.; Tornero Montserrat, J. (2018). Human-robot cooperation for robust surface treatment using non-conventional sliding mode control. ISA Transactions. 80(1):528-541. https://doi.org/10.1016/j.isatra.2018.05.013S52854180

Human-robot collaboration for safe object transportation using force feedback

[EN] This work presents an approach based on multi-task, non-conventional sliding mode control and admittance control for human-robot collaboration aimed at handling applications using force feedback. The proposed robot controller is based on three tasks with different priority levels in order to cooperatively perform the safe transportation of an object with a human operator. In particular, a high-priority task is developed using non-conventional sliding mode control to guarantee safe reference parameters imposed by the task, e.g., keeping a load at a desired orientation (to prevent spill out in the case of liquids, or to reduce undue stresses that may compromise fragile items). Moreover, a second task based on a hybrid admittance control algorithm is used for the human operator to guide the robot by means of a force sensor located at the robot tool. Finally, a third low-priority task is considered for redundant robots in order to use the remaining degrees of freedom of the robot to achieve a pre-set secondary goal (e.g., singularity avoidance, remaining close to a homing configuration for increased safety, etc.) by means of the gradient projection method. The main advantages of the proposed method are robustness and low computational cost. The applicability and effectiveness of the proposed approach are substantiated by experimental results using a redundant 7R manipulator: the Sawyer collaborative robot. (C) 2018 Elsevier B.V. All rights reserved.This work was supported in part by the Spanish Government under Project DPI2017-87656-C2-1-R, and the Generalitat Valenciana under Grants VALi+d APOSTD/2016/044 and BEST/2017/029.Solanes Galbis, JE.; Gracia Calandin, LI.; Muñoz-Benavent, P.; Valls Miro, J.; Carmichael, MG.; Tornero Montserrat, J. (2018). Human-robot collaboration for safe object transportation using force feedback. Robotics and Autonomous Systems. 107:196-208. https://doi.org/10.1016/j.robot.2018.06.003S19620810

Human-robot collaboration for surface treatment tasks

[EN] This paper presents a human-robot closely collaborative solution to cooperatively perform surface treatment tasks such as polishing, grinding, finishing, deburring, etc. The proposed scheme is based on task priority and non-conventional sliding mode control. Furthermore, the proposal includes two force sensors attached to the manipulator end-effector and tool: one sensor is used to properly accomplish the surface treatment task, while the second one is used by the operator to guide the robot tool. The applicability and feasibility of the proposed collaborative solution for robotic surface treatment are substantiated by experimental results using a redundant 7R manipulator: the Sawyer collaborative robot.This work was supported in part by the Spanish Government under the project DPI-201787656-C2-1-R and the Generalitat Valenciana under Grant VALi+d.Gracia Calandin, LI.; Solanes Galbis, JE.; Muñoz-Benavent, P.; Valls Miro, J.; Perez-Vidal, C.; Tornero Montserrat, J. (2019). 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Estimation of Desired Motion Intention and compliance control for upper limb assist exoskeleton. International Journal of Control, Automation and Systems, 15(2), 802-814. doi:10.1007/s12555-015-0151-7Levant, A. (2003). Higher-order sliding modes, differentiation and output-feedback control. International Journal of Control, 76(9-10), 924-941. doi:10.1080/0020717031000099029Levant, A. (2005). Quasi-continuous high-order sliding-mode controllers. IEEE Transactions on Automatic Control, 50(11), 1812-1816. doi:10.1109/tac.2005.858646Martínez, S. S., Ortega, J. G., García, J. G., García, A. S., & Estévez, E. E. (2013). An industrial vision system for surface quality inspection of transparent parts. The International Journal of Advanced Manufacturing Technology, 68(5-8), 1123-1136. doi:10.1007/s00170-013-4904-2Massoud, A. T., ElMaraghy, H. A., & Lahdhiri, T. (1999). Journal of Intelligent and Robotic Systems, 25(3), 227-254. doi:10.1023/a:1008099522350Mitra, A., & Behera, L. (2015). Development of a Fuzzy Sliding Mode Controller with adaptive tuning technique for a MRI guided robot in the human vasculature. 2015 IEEE 13th International Conference on Industrial Informatics (INDIN). doi:10.1109/indin.2015.7281763Molina, J., Solanes, J. E., Arnal, L., & Tornero, J. (2017). On the detection of defects on specular car body surfaces. Robotics and Computer-Integrated Manufacturing, 48, 263-278. doi:10.1016/j.rcim.2017.04.009Nakamura, Y., Hanafusa, H., & Yoshikawa, T. (1987). Task-Priority Based Redundancy Control of Robot Manipulators. The International Journal of Robotics Research, 6(2), 3-15. doi:10.1177/027836498700600201Ortaç, G., Bilgi, A. S., Taşdemir, K., & Kalkan, H. (2016). A hyperspectral imaging based control system for quality assessment of dried figs. Computers and Electronics in Agriculture, 130, 38-47. doi:10.1016/j.compag.2016.10.001Papadopoulos, F., Küster, D., Corrigan, L. J., Kappas, A., & Castellano, G. (2016). 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IEEE ACCESS SPECIAL SECTION EDITORIAL: REAL-TIME MACHINE LEARNING APPLICATIONS IN MOBILE ROBOTICS

In the last ten years, advances in machine learning methods have brought tremendous developments to the field of robotics. The performance in many robotic applications such as robotics grasping, locomotion, human–robot interaction, perception and control of robotic systems, navigation, planning, mapping, and localization has increased since the appearance of recent machine learning methods. In particular, deep learning methods have brought significant improvements in a broad range of robot applications including drones, mobile robots, robotics manipulators, bipedal robots, and self-driving cars. The availability of big data and more powerful computational resources, such as graphics processing units (GPUs), has made numerous robotic applications feasible which were not possible previously