Enhanced Image-Based Visual Servoing Dealing with Uncertainties

Nowadays, the applications of robots in industrial automation have been considerably increased. There is increasing demand for the dexterous and intelligent robots that can work in unstructured environment. Visual servoing has been developed to meet this need by integration of vision sensors into robotic systems. Although there has been significant development in visual servoing, there still exist some challenges in making it fully functional in the industry environment. The nonlinear nature of visual servoing and also system uncertainties are part of the problems affecting the control performance of visual servoing. The projection of 3D image to 2D image which occurs in the camera creates a source of uncertainty in the system. Another source of uncertainty lies in the camera and robot manipulator's parameters. Moreover, limited field of view (FOV) of the camera is another issues influencing the control performance. There are two main types of visual servoing: position-based and image-based. This project aims to develop a series of new methods of image-based visual servoing (IBVS) which can address the nonlinearity and uncertainty issues and improve the visual servoing performance of industrial robots. The first method is an adaptive switch IBVS controller for industrial robots in which the adaptive law deals with the uncertainties of the monocular camera in eye-in-hand configuration. The proposed switch control algorithm decouples the rotational and translational camera motions and decomposes the IBVS control into three separate stages with different gains. This method can increase the system response speed and improve the tracking performance of IBVS while dealing with camera uncertainties. The second method is an image feature reconstruction algorithm based on the Kalman filter which is proposed to handle the situation where the image features go outside the camera's FOV. The combination of the switch controller and the feature reconstruction algorithm can not only improve the system response speed and tracking performance of IBVS, but also can ensure the success of servoing in the case of the feature loss. Next, in order to deal with the external disturbance and uncertainties due to the depth of the features, the third new control method is designed to combine proportional derivative (PD) control with sliding mode control (SMC) on a 6-DOF manipulator. The properly tuned PD controller can ensure the fast tracking performance and SMC can deal with the external disturbance and depth uncertainties. In the last stage of the thesis, the fourth new semi off-line trajectory planning method is developed to perform IBVS tasks for a 6-DOF robotic manipulator system. In this method, the camera's velocity screw is parametrized using time-based profiles. The parameters of the velocity profile are then determined such that the velocity profile takes the robot to its desired position. This is done by minimizing the error between the initial and desired features. The algorithm for planning the orientation of the robot is decoupled from the position planning of the robot. This allows a convex optimization problem which lead to a faster and more efficient algorithm. The merit of the proposed method is that it respects all of the system constraints. This method also considers the limitation caused by camera's FOV. All the developed algorithms in the thesis are validated via tests on a 6-DOF Denso robot in an eye-in-hand configuration

Sliding mode control for robust and smooth reference tracking in robot visual servoing

[EN] An approach based on sliding mode is proposed in this work for reference tracking in robot visual servoing. In particular, 2 sliding mode controls are obtained depending on whether joint accelerations or joint jerks are considered as the discontinuous control action. Both sliding mode controls are extensively compared in a 3D-simulated environment with their equivalent well-known continuous controls, which can be found in the literature, to highlight their similarities and differences. The main advantages of the proposed method are smoothness, robustness, and low computational cost. The applicability and robustness of the proposed approach are substantiated by experimental results using a conventional 6R industrial manipulator (KUKA KR 6 R900 sixx [AGILUS]) for positioning and tracking tasks.Spanish Government, Grant/Award Number: BES-2010-038486; Generalitat Valenciana, Grant/Award Number: BEST/2017/029 and APOSTD/2016/044Muñoz-Benavent, P.; Gracia, L.; Solanes, JE.; Esparza, A.; Tornero, J. (2018). Sliding mode control for robust and smooth reference tracking in robot visual servoing. International Journal of Robust and Nonlinear Control. 28(5):1728-1756. https://doi.org/10.1002/rnc.3981S17281756285Hutchinson, S., Hager, G. D., & Corke, P. I. (1996). A tutorial on visual servo control. IEEE Transactions on Robotics and Automation, 12(5), 651-670. doi:10.1109/70.538972Chaumette, F., & Hutchinson, S. (2008). Visual Servoing and Visual Tracking. Springer Handbook of Robotics, 563-583. doi:10.1007/978-3-540-30301-5_25Corke, P. (2011). Robotics, Vision and Control. Springer Tracts in Advanced Robotics. doi:10.1007/978-3-642-20144-8RYAN, E. P., & CORLESS, M. (1984). Ultimate Boundedness and Asymptotic Stability of a Class of Uncertain Dynamical Systems via Continuous and Discontinuous Feedback Control. IMA Journal of Mathematical Control and Information, 1(3), 223-242. doi:10.1093/imamci/1.3.223Chaumette, F., & Hutchinson, S. (2006). Visual servo control. I. Basic approaches. IEEE Robotics & Automation Magazine, 13(4), 82-90. doi:10.1109/mra.2006.250573Chaumette, F., & Hutchinson, S. (2007). Visual servo control. II. Advanced approaches [Tutorial]. IEEE Robotics & Automation Magazine, 14(1), 109-118. doi:10.1109/mra.2007.339609Bonfe M Mainardi E Fantuzzi C Variable structure PID based visual servoing for robotic tracking and manipulation 2002 Lausanne, Switzerland https://doi.org/10.1109/IRDS.2002.1041421Solanes, J. E., Muñoz-Benavent, P., Girbés, V., Armesto, L., & Tornero, J. (2015). On improving robot image-based visual servoing based on dual-rate reference filtering control strategy. Robotica, 34(12), 2842-2859. doi:10.1017/s0263574715000454Elena M Cristiano M Damiano F Bonfe M Variable structure PID controller for cooperative eye-in-hand/eye-to-hand visual servoing 2003 Istanbul, Turkey https://doi.org/10.1109/CCA.2003.1223145Hashimoto, K., Ebine, T., & Kimura, H. (1996). Visual servoing with hand-eye manipulator-optimal control approach. IEEE Transactions on Robotics and Automation, 12(5), 766-774. doi:10.1109/70.538981Chan A Leonard S Croft EA Little JJ Collision-free visual servoing of an eye-in-hand manipulator via constraint-aware planning and control 2011 San Francisco, CA, USA https://doi.org/10.1109/ACC.2011.5991008Allibert, G., Courtial, E., & Chaumette, F. (2010). Visual Servoing via Nonlinear Predictive Control. Lecture Notes in Control and Information Sciences, 375-393. doi:10.1007/978-1-84996-089-2_20Kragic, D., & Christensen, H. I. (2003). Robust Visual Servoing. The International Journal of Robotics Research, 22(10-11), 923-939. doi:10.1177/027836490302210009Mezouar Y Chaumette F Path planning in image space for robust visual servoing 2000 San Francisco, CA, USA https://doi.org/10.1109/ROBOT.2000.846445Morel, G., Zanne, P., & Plestan, F. (2005). Robust visual servoing: bounding the task function tracking errors. IEEE Transactions on Control Systems Technology, 13(6), 998-1009. doi:10.1109/tcst.2005.857409Hammouda, L., Kaaniche, K., Mekki, H., & Chtourou, M. (2015). Robust visual servoing using global features based on random process. International Journal of Computational Vision and Robotics, 5(2), 138. doi:10.1504/ijcvr.2015.068803Yang YX Liu D Liu H Robot-self-learning visual servoing algorithm using neural networks 2002 Beijing, China https://doi.org/10.1109/ICMLC.2002.1174473Sadeghzadeh, M., Calvert, D., & Abdullah, H. A. (2014). Self-Learning Visual Servoing of Robot Manipulator Using Explanation-Based Fuzzy Neural Networks and Q-Learning. Journal of Intelligent & Robotic Systems, 78(1), 83-104. doi:10.1007/s10846-014-0151-5Lee AX Levine S Abbeel P Learning Visual Servoing With Deep Features and Fitted Q-Iteration 2017Fakhry, H. H., & Wilson, W. J. (1996). A modified resolved acceleration controller for position-based visual servoing. Mathematical and Computer Modelling, 24(5-6), 1-9. doi:10.1016/0895-7177(96)00112-4Keshmiri, M., Wen-Fang Xie, & Mohebbi, A. (2014). Augmented Image-Based Visual Servoing of a Manipulator Using Acceleration Command. IEEE Transactions on Industrial Electronics, 61(10), 5444-5452. doi:10.1109/tie.2014.2300048Edwards, C., & Spurgeon, S. (1998). Sliding Mode Control. doi:10.1201/9781498701822Zanne P Morel G Piestan F Robust vision based 3D trajectory tracking using sliding mode control 2000 San Francisco, CA, USAOliveira TR Peixoto AJ Leite AC Hsu L Sliding mode control of uncertain multivariable nonlinear systems applied to uncalibrated robotics visual servoing 2009 St. Louis, MO, USAOliveira, T. R., Leite, A. C., Peixoto, A. J., & Hsu, L. (2014). Overcoming Limitations of Uncalibrated Robotics Visual Servoing by means of Sliding Mode Control and Switching Monitoring Scheme. Asian Journal of Control, 16(3), 752-764. doi:10.1002/asjc.899Li, F., & Xie, H.-L. (2010). Sliding mode variable structure control for visual servoing system. International Journal of Automation and Computing, 7(3), 317-323. doi:10.1007/s11633-010-0509-5Kim J Kim D Choi S Won S Image-based visual servoing using sliding mode control 2006 Busan, South KoreaBurger W Dean-Leon E Cheng G Robust second order sliding mode control for 6D position based visual servoing with a redundant mobile manipulator 2015 Seoul, South KoreaBecerra, H. M., López-Nicolás, G., & Sagüés, C. (2011). A Sliding-Mode-Control Law for Mobile Robots Based on Epipolar Visual Servoing From Three Views. IEEE Transactions on Robotics, 27(1), 175-183. doi:10.1109/tro.2010.2091750Parsapour, M., & Taghirad, H. D. (2015). Kernel-based sliding mode control for visual servoing system. IET Computer Vision, 9(3), 309-320. doi:10.1049/iet-cvi.2013.0310Xin J Ran BJ Ma XM Robot visual sliding mode servoing using SIFT features 2016 Chengdu, ChinaZhao, Y. M., Lin, Y., Xi, F., Guo, S., & Ouyang, P. (2016). Switch-Based Sliding Mode Control for Position-Based Visual Servoing of Robotic Riveting System. Journal of Manufacturing Science and Engineering, 139(4). doi:10.1115/1.4034681Moosavian, S. A. A., & Papadopoulos, E. (2007). Modified transpose Jacobian control of robotic systems. Automatica, 43(7), 1226-1233. doi:10.1016/j.automatica.2006.12.029Sagara, S., & Taira, Y. (2008). Digital control of space robot manipulators with velocity type joint controller using transpose of generalized Jacobian matrix. Artificial Life and Robotics, 13(1), 355-358. doi:10.1007/s10015-008-0584-7Khalaji, A. K., & Moosavian, S. A. A. (2015). Modified transpose Jacobian control of a tractor-trailer wheeled robot. Journal of Mechanical Science and Technology, 29(9), 3961-3969. doi:10.1007/s12206-015-0841-3Utkin, V., Guldner, J., & Shi, J. (2017). Sliding Mode Control in Electro-Mechanical Systems. doi:10.1201/9781420065619Utkin, V. (2016). Discussion Aspects of High-Order Sliding Mode Control. IEEE Transactions on Automatic Control, 61(3), 829-833. doi:10.1109/tac.2015.2450571Romdhane, H., Dehri, K., & Nouri, A. S. (2016). Discrete second-order sliding mode control based on optimal sliding function vector for multivariable systems with input-output representation. International Journal of Robust and Nonlinear Control, 26(17), 3806-3830. doi:10.1002/rnc.3536Sharma, N. K., & Janardhanan, S. (2017). Optimal discrete higher-order sliding mode control of uncertain LTI systems with partial state information. International Journal of Robust and Nonlinear Control. doi:10.1002/rnc.3785LEVANT, A. (1993). Sliding order and sliding accuracy in sliding mode control. International Journal of Control, 58(6), 1247-1263. doi:10.1080/00207179308923053Levant, A. (2003). Higher-order sliding modes, differentiation and output-feedback control. International Journal of Control, 76(9-10), 924-941. doi:10.1080/0020717031000099029Bartolini, G., Ferrara, A., & Usai, E. (1998). Chattering avoidance by second-order sliding mode control. IEEE Transactions on Automatic Control, 43(2), 241-246. doi:10.1109/9.661074Siciliano, B., Sciavicco, L., Villani, L., & Oriolo, G. (2009). Robotics. Advanced Textbooks in Control and Signal Processing. doi:10.1007/978-1-84628-642-1Deo, A. S., & Walker, I. D. (1995). Overview of damped least-squares methods for inverse kinematics of robot manipulators. Journal of Intelligent & Robotic Systems, 14(1), 43-68. doi:10.1007/bf01254007WHEELER, G., SU, C.-Y., & STEPANENKO, Y. (1998). A Sliding Mode Controller with Improved Adaptation Laws for the Upper Bounds on the Norm of Uncertainties. Automatica, 34(12), 1657-1661. doi:10.1016/s0005-1098(98)80024-1Yu-Sheng Lu. (2009). Sliding-Mode Disturbance Observer With Switching-Gain Adaptation and Its Application to Optical Disk Drives. IEEE Transactions on Industrial Electronics, 56(9), 3743-3750. doi:10.1109/tie.2009.2025719Chen, X., Shen, W., Cao, Z., & Kapoor, A. (2014). A novel approach for state of charge estimation based on adaptive switching gain sliding mode observer in electric vehicles. Journal of Power Sources, 246, 667-678. doi:10.1016/j.jpowsour.2013.08.039Cong, B. L., Chen, Z., & Liu, X. D. (2012). On adaptive sliding mode control without switching gain overestimation. International Journal of Robust and Nonlinear Control, 24(3), 515-531. doi:10.1002/rnc.2902Taleb, M., Plestan, F., & Bououlid, B. (2014). An adaptive solution for robust control based on integral high-order sliding mode concept. International Journal of Robust and Nonlinear Control, 25(8), 1201-1213. doi:10.1002/rnc.3135Zhu, J., & Khayati, K. (2016). On a new adaptive sliding mode control for MIMO nonlinear systems with uncertainties of unknown bounds. International Journal of Robust and Nonlinear Control, 27(6), 942-962. doi:10.1002/rnc.3608Hafez AHA Cervera E Jawahar CV Hybrid visual servoing by boosting IBVS and PBVS 2008 Damascus, SyriaKermorgant O Chaumette F Combining IBVS and PBVS to ensure the visibility constraint 2011 San Francisco, CA, USACorke, P. I., & Hutchinson, S. A. (2001). A new partitioned approach to image-based visual servo control. IEEE Transactions on Robotics and Automation, 17(4), 507-515. doi:10.1109/70.954764Yang, Z., & Shen, S. (2017). Monocular Visual–Inertial State Estimation With Online Initialization and Camera–IMU Extrinsic Calibration. IEEE Transactions on Automation Science and Engineering, 14(1), 39-51. doi:10.1109/tase.2016.2550621Chesi G Hashimoto K Static-eye against hand-eye visual servoing 2002 Las Vegas, NV, USABourdis N Marraud D Sahbi H Camera pose estimation using visual servoing for aerial video change detection 2012 Munich, GermanyShademan A Janabi-Sharifi F Sensitivity analysis of EKF and iterated EKF pose estimation for position-based visual servoing 2005 USAMalis, E., Mezouar, Y., & Rives, P. (2010). Robustness of Image-Based Visual Servoing With a Calibrated Camera in the Presence of Uncertainties in the Three-Dimensional Structure. IEEE Transactions on Robotics, 26(1), 112-120. doi:10.1109/tro.2009.2033332Chen J Behal A Dawson D Dixon W Adaptive visual servoing in the presence of intrinsic calibration uncertainty 2003 USAMezouar Y Malis E Robustness of central catadioptric image-based visual servoing to uncertainties on 3D parameters 2004 Sendai, JapanMarchand, E., Spindler, F., & Chaumette, F. (2005). ViSP for visual servoing: a generic software platform with a wide class of robot control skills. IEEE Robotics & Automation Magazine, 12(4), 40-52. doi:10.1109/mra.2005.157702

Exploring Convolutional Networks for End-to-End Visual Servoing

Present image based visual servoing approaches rely on extracting hand crafted visual features from an image. Choosing the right set of features is important as it directly affects the performance of any approach. Motivated by recent breakthroughs in performance of data driven methods on recognition and localization tasks, we aim to learn visual feature representations suitable for servoing tasks in unstructured and unknown environments. In this paper, we present an end-to-end learning based approach for visual servoing in diverse scenes where the knowledge of camera parameters and scene geometry is not available a priori. This is achieved by training a convolutional neural network over color images with synchronised camera poses. Through experiments performed in simulation and on a quadrotor, we demonstrate the efficacy and robustness of our approach for a wide range of camera poses in both indoor as well as outdoor environments.Comment: IEEE ICRA 201

Manipulating Highly Deformable Materials Using a Visual Feedback Dictionary

The complex physical properties of highly deformable materials such as clothes pose significant challenges fanipulation systems. We present a novel visual feedback dictionary-based method for manipulating defoor autonomous robotic mrmable objects towards a desired configuration. Our approach is based on visual servoing and we use an efficient technique to extract key features from the RGB sensor stream in the form of a histogram of deformable model features. These histogram features serve as high-level representations of the state of the deformable material. Next, we collect manipulation data and use a visual feedback dictionary that maps the velocity in the high-dimensional feature space to the velocity of the robotic end-effectors for manipulation. We have evaluated our approach on a set of complex manipulation tasks and human-robot manipulation tasks on different cloth pieces with varying material characteristics.Comment: The video is available at goo.gl/mDSC4

PAMPC: Perception-Aware Model Predictive Control for Quadrotors

We present the first perception-aware model predictive control framework for quadrotors that unifies control and planning with respect to action and perception objectives. Our framework leverages numerical optimization to compute trajectories that satisfy the system dynamics and require control inputs within the limits of the platform. Simultaneously, it optimizes perception objectives for robust and reliable sens- ing by maximizing the visibility of a point of interest and minimizing its velocity in the image plane. Considering both perception and action objectives for motion planning and control is challenging due to the possible conflicts arising from their respective requirements. For example, for a quadrotor to track a reference trajectory, it needs to rotate to align its thrust with the direction of the desired acceleration. However, the perception objective might require to minimize such rotation to maximize the visibility of a point of interest. A model-based optimization framework, able to consider both perception and action objectives and couple them through the system dynamics, is therefore necessary. Our perception-aware model predictive control framework works in a receding-horizon fashion by iteratively solving a non-linear optimization problem. It is capable of running in real-time, fully onboard our lightweight, small-scale quadrotor using a low-power ARM computer, to- gether with a visual-inertial odometry pipeline. We validate our approach in experiments demonstrating (I) the contradiction between perception and action objectives, and (II) improved behavior in extremely challenging lighting conditions