Robust Direct Visual Servo Using Network-Synchronized Cameras

Schuurman, Derek C.; Capson, David W.

doi:10.1109/tra.2003.819718

Cited by 21 publications

(15 citation statements)

References 25 publications

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“…The considered visual servoing control can find applications in several robotic tasks requiring dexterous manipulations (e.g., packing, welding, cutting, etc.) (Schuurman & Capson, 2004). The differentially flat model of the robot has been analysed in Section 4.…”

Section: Simulation Testsmentioning

confidence: 99%

Derivative-Free Distributed Filtering for MIMO Robotic Systems under Delays and Packet Drops

Rigatos

2013

International Journal of Advanced Robotic Systems

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This paper presents an approach to distributed state estimation-based control of nonlinear MIMO systems, capable of incorporating delayed measurements in the estimation algorithm while also being robust to packet losses. First, the paper examines the problem of distributed nonlinear filtering over a communication/sensors network, and the use of the estimated state vector in a control loop. As a possible filtering approach, an extended information filter (EIF) is proposed. The extended information filter requires the computation of Jacobians which in the case of high order nonlinear dynamical systems can be a cumbersome procedure, while it also introduces cumulative errors to the state estimation due to the approximative linearization performed in the Taylor series expansion of the system's nonlinear model. To overcome the aforementioned weaknesses of the extended information filter, a derivative-free approach to extended information filtering has been proposed. Distributed filtering is now based on a derivative-free implementation of Kalman filtering which is shown to be applicable to MIMO nonlinear dynamical systems. In the proposed derivativefree extended information filtering, the system is first subject to a linearization transformation that makes use of the differential flatness theory. It is shown how the proposed distributed filtering method can succeed in compensation of random delays and packet drops which may appear during the transmission of measurements and of state vector estimates, thus assuring a reliable performance of the distributed filtering-based control scheme. Evaluation tests are carried out on benchmark MIMO nonlinear systems, such as multi-DOF robotic manipulators.

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Section: Simulation Testsmentioning

confidence: 99%

Derivative-Free Distributed Filtering for MIMO Robotic Systems under Delays and Packet Drops

Rigatos

2013

International Journal of Advanced Robotic Systems

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“…According to [17], such techniques can be classified into three groups, namely, feature-based eigen-shapes [42,43], physically-based eigen-snakes [22,44], and eigen-pictures [6,9,[45][46][47] which are all based on the Karhunen-Loeve transformation. The rest of this section is devoted to a summary of the parametric eigen-picture representation.…”

Section: Appearance-based Parametric Eigenspacementioning

confidence: 99%

Robust sequential view planning for object recognition using multiple cameras

Farshidi

Sirouspour

Kirubarajan

2009

Image and Vision Computing

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In this thesis the problem of object recognition/pose estimation using active sensing is investigated. It is assumed that multiple cameras acquire images from different view angles of an object belonging to a set of a priori known objects. The eigenspace method is used to process the sensory observations and produce an abstract measurement vector. This step is necessary to avoid the manipulation of the original sensor data, i.e. large images, that can render the sensor modelling and matching process practically infeasible.The eigenspace representation is known to have shortcomings in dealing with structured noise such as occlusion. To overcome this problem, models of occlusions and sensor noise have been incorporated into the probabilistic model of sensor/object to increase robustness with respect to such uncertainties. The active recognition algorithm has also been modified to consider the possibility of occlusion, as well as variation in the occlusion levels due to camera movements.A recursive Bayesian state estimation problem is formulated to model the observation uncertainties through a probabilistic scheme. This enables us to identify the object and estimate its pose by fusing the information obtained from individual cameras. To this end, an extensive training step is performed, providing the system with the sensor model required for the Bayesian estimation. In order to enhance the quality A multi-camera active vision system has been developed in order to implement the ideas proposed in this thesis. Comparative Monte Carlo experiments conducted with the two-camera system demonstrate the effectiveness of the proposed methods in object classification/pose estimation in the presence of structured noise. Different concepts introduced in this work, i.e., the multi-camera data fusion, the occlusion modelling, and the active camera movement, all improve the recognition process significantly. Specifically, these approaches all increase the recognition rate, decrease the number of steps taken before recognition is completed, and enhance robustness with respect to partial occlusion considerably.iv

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“…The problem of visual servoing over a network of synchronised cameras has been previously studied in Schuurman and Capson (2004). In this article, visual servoing over a cameras network is considered for the nonlinear dynamic model of a planar single-link robotic manipulator.…”

Section: Introductionmentioning

confidence: 99%

A derivative-free distributed filtering approach for sensorless control of nonlinear systems

Rigatos

2012

International Journal of Systems Science

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This article examines the problem of sensorless control for nonlinear dynamical systems with the use of derivative-free Extended Information Filtering (EIF). The system is first subject to a linearisation transformation and next state estimation is performed by applying the standard Kalman Filter to the linearised model. At a second level, the standard Information Filter is used to fuse the state estimates obtained from local derivative-free Kalman filters running at the local information processing nodes. This approach has significant advantages because unlike the EIF (i) is not based on local linearisation of the nonlinear dynamics (ii) does not assume truncation of higher order Taylor expansion terms thus preserving the accuracy and robustness of the performed estimation and (iii) does not require the computation of Jacobian matrices. As a case study a robotic manipulator is considered and a cameras network consisting of multiple vision nodes is assumed to provide the visual information to be used in the control loop. A derivative-free implementation of the EIF is used to produce the aggregate state vector of the robot by processing local state estimates coming from the distributed vision nodes. The performance of the considered sensorless control scheme is evaluated through simulation experiments.

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Robust Direct Visual Servo Using Network-Synchronized Cameras

Cited by 21 publications

References 25 publications

Derivative-Free Distributed Filtering for MIMO Robotic Systems under Delays and Packet Drops

Derivative-Free Distributed Filtering for MIMO Robotic Systems under Delays and Packet Drops

Robust sequential view planning for object recognition using multiple cameras

A derivative-free distributed filtering approach for sensorless control of nonlinear systems

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