Adaptive real-time estimation of end-effector position and orientation using precise measurements of end-effector position

Lertpiriyasuwat, V.; Berg, Martin C.

doi:10.1109/tmech.2006.876515

Cited by 31 publications

(10 citation statements)

References 23 publications

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“…In [32] and [33] the authors discussed different adaptive estimation of timevarying parameter approaches in nonlinear systems. Practical examples of the application of adaptive real-time parameter estimation for robotic systems are given in [34], [35].…”

Section: Introductionmentioning

confidence: 99%

Vision-based control of a knuckle boom crane with online cable length estimation

Tysse

Cibicik

Egeland

2020

IEEE/ASME Trans. Mechatron.

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A vision-based controller for a knuckle boom crane is presented. The controller is used to control the motion of the crane tip and at the same time, compensate for payload oscillations. The oscillations of the payload are measured with three cameras that are fixed to the crane king and are used to track two spherical markers fixed to the payload cable. Based on color and size information, each camera identifies the image points corresponding to the markers. The payload angles are then determined using linear triangulation of the image points. An extended Kalman filter is used for the estimation of payload angles and angular velocity. The length of the payload cable is also estimated using the least-squares technique with projection. The crane is controlled by a linear cascade controller where the inner control loop is designed to damp out the pendulum oscillation, and the crane tip is controlled by the outer loop. The control variable of the controller is the commanded crane tip acceleration, which is converted to a velocity command using a velocity loop. The performance of the control system is studied experimentally using a scaled laboratory version of a knuckle boom crane.

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Section: Introductionmentioning

confidence: 99%

Vision-based control of a knuckle boom crane with online cable length estimation

Tysse

Cibicik

Egeland

2020

IEEE/ASME Trans. Mechatron.

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“…Lertpiriyasuwat and Berg used a laser tracker and Kalman filter to estimate the position and orientation of the end-effector of a 6-DOF gantry robot in real time and compensate the pose errors dynamically. 16 Norman et al used an indoor GPS technology as a feedback measurement sensor and improved the absolute accuracy of a KUKA (Mississauga, Ontario) robot in real time. 17 Also, Jin et al proposed a robot-assisted assembly system to improve the accuracy in the process of installing small components.…”

Section: Introductionmentioning

confidence: 99%

Online pose correction of an industrial robot using an optical coordinate measure machine system

Gharaaty

Shu

Joubair

et al. 2018

International Journal of Advanced Robotic Systems

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In this article, a dynamic pose correction scheme is proposed to enhance the pose accuracy of industrial robots. The dynamic pose correction scheme uses the dynamic pose measurements as feedback to accurately guide the robot end-effector to the desired pose. The pose is measured online with an optical coordinate measure machine, that is, C-Track 780 from Creaform. A root mean square method is proposed to filter the noise from the pose measurements. The dynamic pose correction scheme adopts proportional-integral-derivaitve controller and generates commands to the FANUC robot controller. The developed dynamic pose correction scheme has been tested on two industrial robots, FANUC LR Mate 200iC and FANUC M20iA. The experimental results on both robots demonstrate that the robots can reach the desired pose with an accuracy of +0:050 mm for position and +0:050 for orientation. As a result, the developed pose correction can make the industrial robots meet higher accuracy requirement in the applications such as riveting, drilling, and spot welding.

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“…In the literature, Kalman‐based visual tracking algorithms have been proposed by many researchers , where Kalman‐filters are used to compensate the visual sensing errors and improve the tracking efficiency. Additionally, Kalman filters are also widely applied to the position control systems such as when the positioning measurements (encoder) are unreliable or inaccurate. However, the two types of applications using Kalman filter are only applied separately or sequentially in the existing articles.…”

Section: Introductionmentioning

confidence: 99%

Alignment Motion Control for an Automated Human Ear Surgery via Vision‐Servoing

Gao

Liang

Tan

2016

Asian Journal of Control

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To treat a worldwide common ear disease called Otitis Media with Effusion, a robotic device allowing fast and automatic grommet tube insertion has been designed in our earlier works. However, the previous version has to be manually placed close to the tympanic membrane before the insertion procedures. For the purpose of realizing a fully automated surgical process, locating and realizing a suitable insertion path from outside the ear forms a crucial step. It is then desirable to control the working channel to be aligned to the axial direction of auditory canal from the vision channel. In this paper, a fast and robust vision‐based servomechanism is proposed to achieve the planar alignment. To detect and track the region of auditory canal through the image sequences, an image segmentation algorithm based on the phase information and combining with the hough transformation technique is proposed. With the help of Kalman filter, a new look‐and‐move servomechanism can be achieved by utilizing the visual and motion measurements simultaneously, resulting in an effective and robust servoing performance.

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Adaptive real-time estimation of end-effector position and orientation using precise measurements of end-effector position

Cited by 31 publications

References 23 publications

Vision-based control of a knuckle boom crane with online cable length estimation

Vision-based control of a knuckle boom crane with online cable length estimation

Online pose correction of an industrial robot using an optical coordinate measure machine system

Alignment Motion Control for an Automated Human Ear Surgery via Vision‐Servoing

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