Adaptive Jacobian vision based control for robots with uncertain depth information

Cheah, Chien Chern; Liu, Chao; Slotine, Jean‐Jacques E.

doi:10.1016/j.automatica.2010.04.009

Cited by 80 publications

(57 citation statements)

References 25 publications

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“…The following property associated with the depth z c is important for designing adaptive laws to directly estimate its unknown parameters as in Cheah et al (2010).…”

Section: Proof the Row Vectorsmmentioning

confidence: 99%

“…Note that, the schemes in Slotine (2007, 2010) can also cope with the vision-based control problem of robotic manipulators with unknown time-varying depth information. One of the differences between the approaches in Liang et al (2011) and Wang et al (2010Wang et al ( , 2007 and those in Cheah et al (2007Cheah et al ( , 2010 is that different strategies were used to deal with the unknown depth for obtaining its estimation. In Liang et al (2011) and Wang et al (2010Wang et al ( , 2007, the estimated depth was calculated by using the estimated camera parameters, while in Cheah et al (2007Cheah et al ( , 2010, it was obtained by directly estimating its unknown parameters.…”

Section: Introductionmentioning

confidence: 96%

“…Furthermore, just like most of other uncalibrated visual servoing approaches, existing depth-independentJacobian-matrix-based methods can only be applied to one of the two camera configurations, while it is believed that we can get benefits from an image-based control design method that can be applied to both camera configurations. Furthermore, though the strategies in Cheah et al (2007Cheah et al ( , 2010 can also handle uncertainties in the camera, kinematic and dynamic parameters in the presence of time-varying depth information, projection algorithms should be used to design the updating laws to estimate unknown parameters, such that positiveness of the depth parameter can be guaranteed to avoid unnecessary singularity of the controllers. This implies that knowledge about the bound of unknown parameters is required in Cheah et al (2007Cheah et al ( , 2010.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, though the strategies in Cheah et al (2007Cheah et al ( , 2010 can also handle uncertainties in the camera, kinematic and dynamic parameters in the presence of time-varying depth information, projection algorithms should be used to design the updating laws to estimate unknown parameters, such that positiveness of the depth parameter can be guaranteed to avoid unnecessary singularity of the controllers. This implies that knowledge about the bound of unknown parameters is required in Cheah et al (2007Cheah et al ( , 2010. Hence, in the presence of unknown camera, feature position, time-varying feature depth and dynamic parameters, the image-based tracking control of robots that can be applied to both camera configurations and can guarantee asymptotical convergence of image tracking errors without imposing the boundedness assumption of image position tracking errors and without knowing the bound of unknown parameters, remains an open problem.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A unified design method for adaptive visual tracking control of robots with eye-in-hand/fixed camera configuration

et al. 2015

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“…The following property associated with the depth z c is important for designing adaptive laws to directly estimate its unknown parameters as in Cheah et al (2010).…”

Section: Proof the Row Vectorsmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A unified design method for adaptive visual tracking control of robots with eye-in-hand/fixed camera configuration

et al. 2015

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“…By using a depth-independent interaction matrix, Liu et al [12] and Wang et al [13] introduced vision based controllers that are robust to uncertain camera parameters. Cheah et al [14] presented an adaptive Jacobian setpoint controller with concurrent adaptation to uncertain depth information and kinematics.…”

Section: Introductionmentioning

confidence: 99%

Multiple task-space robot control: Sense locally, act globally

Cheah

2012

2012 IEEE International Conference on Robotics and Automation

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Task-space sensory feedback information such as visual feedback is used in many modern robot control systems as it improves robustness to model uncertainty. However, existing sensory feedback control schemes are only valid locally in a finite task space within a limited sensing zone where singularity of the Jacobian matrix is avoided. In this paper, the global stability problem of task-space sensory feedback control system is formulated and solved. The proposed method is based on multiple regional feedback information where each feedback information is employed in a local region. The combination of the local feedback covers the entire workspace and thus guarantees the global movement of the robot. In addition, the switching from one feedback information to another is embedded in the controller without using any hard or discontinuous switching. Experimental results are presented to illustrate the performance of the proposed controller.

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Adaptive control and signal processing literature survey (No. 20)

2010

Adaptive Control & Signal

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In order to keep the readers up-to-date, the journal will contain a list of recently published journal articles relevant to the fields of adaptive control and signal processing six times a year. This list is compiled from a wide range of journals. Please note that although the list has been broadly categorized, these classifications are by no means strict and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please e-mail Erfu Yang at erfu.yang@eee.strath.ac.uk. ADAPTIVE SYSTEMSAbramovich Y, Frazer G, Johnson B. Iterative adaptive Kronecker MIMO radar beamformer: description and convergence analysis. IEEE Transactions on Signal Processing 2010; 58(7):3681-3691. Badwe A, Singh A, Patwardhan S, Gudi R. A constrained recursive pseudo-linear regression scheme for on-line parameter estimation in adaptive control. Journal of Process Control 2010; 20(5):559-572. Cassimatis N, Bignoli P, Bugajska M, Dugas S, Kurup U, Murugesan A, Bello P. An architecture for adaptive algorithmic hybrids. IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetic 2010; 40(3):903-914. Cheah C, Hou S, Zhao Y, Slotine J-J. Adaptive vision and force tracking control for robots with constraint uncertainty. IEEE/ASME Transactions on Mechatronics 2010; 15(3): 389-399. Cheah C, Liu C, Slotine J. Adaptive Jacobian vision based control for robots with uncertain depth information. Automatica 2010; 46(7):1228-1233. De Maio A, Conte E. Adaptive detection in Gaussian interference with unknown covariance after reduction by invariance. IEEE Transactions on Signal Processing 2010; 58(6):2925-2934. Fei J. Robust adaptive vibration tracking control for a micro-electro-mechanical systems vibratory gyroscope with bound estimation. control allocation for non-linear systems with internal dynamics. IET Control Theory and Applications 2010; 4(6):909-922. Liu L, Xie X-J. Decentralized adaptive stabilization for interconnected systems with dynamic input-output and nonlinear interactions. Automatica 2010; 46(6):1060-1067. Malloci I, Daafouz J, Iung C. Stability and stabilization of two time scale switched systems in discrete time. IEEE Transactions on Automatic Control 2010; 55(6):1434-1438. Mileounis G, Babadi B, Kalouptsidis N, Tarokh V. An adaptive greedy algorithm with application to nonlinear communications. IEEE Transactions on Signal Processing 2010; 58(6):2998-3007. Nguyen H-X, Nguyen H-H. Adaptive relaying in noncoherent cooperative networks. IEEE Transactions on Signal Processing 2010; 58(7):3938-3945. Pigg S, Bodson M. Adaptive algorithms for the rejection of sinusoidal disturbances acting on unknown plants. IEEE Transactions on Control Systems Technology 2010; 18(4): 822-836. Tucci M, Raugi M. Adaptive FIR neural model for centroid learning in self-organizing maps. IEEE Transactions on Neural Networks 2010; 21(6):948-960. Wang L, de Lamare R, Yukawa M. Adaptive reducedrank constrained constant modulus algorithms based on

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Adaptive Jacobian vision based control for robots with uncertain depth information

Cited by 80 publications

References 25 publications

A unified design method for adaptive visual tracking control of robots with eye-in-hand/fixed camera configuration

A unified design method for adaptive visual tracking control of robots with eye-in-hand/fixed camera configuration

Multiple task-space robot control: Sense locally, act globally

Adaptive control and signal processing literature survey (No. 20)

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