Farrokh Janabi-Sharifi

Discrete-time adaptive windowing for velocity estimation

We present methods for velocity estimation from discrete and quantized position samples using adaptive windowing. Previous methods necessitate trade-offs between noise reduction, control delay, estimate accuracy, reliability, computational load, transient preservation, and difficulties with tuning. In contrast, a first-order adaptive windowing method is shown to be optimal in the sense that it minimizes the velocity error variance while maximizes the accuracy of the estimates, requiring no tradeoff. Variants of this method are also discussed. The effectiveness of the proposed technique is verified in simulation and by experiments on the control of a haptic device.

A Kalman-Filter-Based Method for Pose Estimation in Visual Servoing

The problem of estimating position and orientation (pose) of an object in real time constitutes an important issue for vision-based control of robots. Many vision-based pose-estimation schemes in robot control rely on an extended Kalman filter (EKF) that requires tuning of filter parameters. To obtain satisfactory results, EKF-based techniques rely on “known” noise statistics, initial object pose, and sufficiently high sampling rates for good approximation of measurement-function linearization. Deviations from such assumptions usually lead to degraded pose estimation during visual servoing. In this paper, a new algorithm, namely iterative adaptive EKF (IAEKF), is proposed by integrating mechanisms for noise adaptation and iterative-measurement linearization. The experimental results are provided to demonstrate the superiority of IAEKF in dealing with erroneous a priori statistics, poor pose initialization, variations in the sampling rate, and trajectory dynamics.

Hybrid motion control and planning strategies for visual servoing

This paper presents two hybrid strategies for robot visual servoing. Two specific image constraints, the image singularities and image local minima, are considered in both strategies. The hybrid motion control strategy consists of a local switching control between the image-based and position-based visual servoing for direct avoidance of image singularities and image local minima. The hybrid motion planning strategy consists of an artificial potential field-based global hybrid trajectory planner, where a complete set of Cartesian, image, and robot joint constraints under a complex visual servoing scenario are considered. In this strategy, the image singularities are resolved using the damped-least-square-based joint trajectory planning, while the image local minima are evaluated only along the planned image trajectories and automatically avoided in the image-based trajectory tracking. Two global planning methods are considered. In the first method, the end-effector trajectory is directly planned with respect to the stationary target object frame, which provides a much shorter translational path compared with the local planning method. In the second method, the target trajectory is planned with respect to the current end-effector frame, which minimizes the chances of image trajectories leaving the camera field of view. Simulation and experimental results are given to demonstrate the efficiency of the two hybrid strategies.

Model Reference Adaptive Control Design for a Teleoperation System with Output Prediction

In this paper, a new control scheme is proposed to ensure stability and good performance of the teleoperation systems while a wide range of time delays in transmission line is allowed. For this purpose, a new algorithm is recommended for time delay estimation and plant output prediction in the presence of white and color noise. A model reference adaptive controller (MRAC) is designed for the master site using the predicted output of the plant. An independent MRAC is also designed and integrated for the slave site. The proposed control system indicates good stability and tracking performance.

Data-driven modeling of thermal energy storage tank

In this paper data-driven model of thermal energy storage tank also known as buffer tank is developed using the measured data from Toronto and Region Conservation Authority (TRCA) Archetype House. Radiant floor heating (RFH) system uses hot water stored in the tank to supply heat to a zone during the heating season. The tank temperature is maintained by a ground source heat pump (GSHP). During the charging mode, GSHP supplies hot water to the buffer tank which raises the temperature of the water inside the tank. During the discharging mode, the hot water is supplied to the RFH system and cold water is returned to the buffer tank which lowers its temperature. The purpose of modeling is to determine the buffer tank temperature during the charging, simultaneous charging-discharging and discharging modes. First order linear models were developed by decoupling these modes. A good fit was found between the measured and predicted tank temperature.

Integration of the artificial potential field approach with simulated annealing for robot path planning

An integrated approach to local as well as global path planning of the robot in stationary environments is proposed. To provide effectiveness, the path planning is based on the artificial potential field method and to ensure robustness, the susceptibility to local minima is reduced by using some heuristic strategies. The simulated annealing technique is applied when trapped in local minima. The effectiveness of the proposed algorithms for local and global path planning is verified by a series of simulations. The performance of simulated annealing is evaluated by examining the effects of changing the various annealing parameters. The choices that improve simulated annealing performance are described.<<ETX>>

Automatic selection of image features for visual servoing

The problem of automatically selecting the image features is an important aspect of visual servoing systems. This paper addresses the main issues relevant to automatic features selection in the context of visual servoing. The constraints imposed by the task, the feature extraction and state estimation processes are derived. The quality measures are formulated and a features selection strategy is introduced. In addition to the features selection, the proposed methodology provides the automatic selection of windows for the target features during the servoing. Also it can be applied for optimal vision sensor positioning. The design and implementation of an automatic CAD-based features selector (AFS) using the proposed methodology has been achieved. The experimental results are presented to demonstrate the effectiveness and correctness of the proposed framework.

Comparison of Basic Visual Servoing Methods

In this paper, comparison of the position-based and image-based robot visual servoing methods is investigated, with an emphasis on the system stability, robustness, sensitivity, and dynamic performance in the Cartesian and image spaces. A common comparison framework using both predefined and taught references is defined in the context of the sensory task space robot control approach. The camera, target, and robot modeling errors in the system are considered in the comparison. Both methods are shown to be locally asymptotically stable and locally robust with respect to modeling errors. While the two methods are shown to be comparable and sensitive to the camera and target modeling errors when using predefined references, they are insensitive to these errors when using taught references. However, the system Cartesian and image trajectories and time-to-converge are affected by the camera, target, and robot modeling errors regardless of the type of references. Finally, other fundamental characteristics of the two methods including sensory task space singularity and local minima, motion coupling, and implementation issues are also compared. The comparison results are verified in simulations.

Stability and robustness of visual servoing methods

This paper presents a theoretical comparison of the stability and robustness of the two basic visual servoing methods, image-based and position-based visual servoing. A common framework for comparison based on the energy shaping plus damping injection methodology is presented. Previous stability results for the image-based method are extended to show the robustness of stability with respect to camera and target modeling, errors. Similar stability and robustness analyses are extended to the analysis of the position-based method. It is shown that both methods are locally asymptotically stable and robust with respect to camera and target modeling errors. However the stability boundary with respect to above errors has not been established.

Catheter Kinematics for Intracardiac Navigation

Steerable catheters are utilized frequently in minimally invasive cardiac interventions. Despite their extensive applications, the properties of the steerable section of such devices have not been thoroughly investigated. In this paper, the kinematics of the distal shaft of the catheters is modeled, and the catheters reachable workspace and its singular configurations are studied. The modeling is empirically validated through experiments with actual catheters mounted on a specialized robot. The statistical analysis of the experiments verify the effectiveness of the proposed model in estimating the catheters tip position. In the experiments, the modeling error does not exceed 2.66 plusmn 1.96 mm, and the mean absolute error in position coordinates is less than 1.55 mm. In addition, the linear relationship between the model and the measured position vectors is demonstrated, and the significance of the modeling goodness of fit is established. Based on the precision and computational effectiveness of the method, the applicability of the modeling to control and simulation purposes is postulated.