Visual servo control. II. Advanced approaches [Tutorial]

Chaumette, François; Hutchinson, Seth

doi:10.1109/mra.2007.339609

Cited by 862 publications

(344 citation statements)

References 42 publications

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“…The map-and-replay approach is closely related to the visual servoing, in which the control of a robot is based on visual measurements (Chaumette and Hutchinson, 2006;Chaumette and Hutchinson, 2007). Control inputs are either computed directly by a comparison of the current and reference image Segvic et al, 2009) or by a computation of camera coordinates in the world reference frame (Wilson et al, 1996;DeMenthon and Davis, 1992).…”

Section: Vision-based Navigationmentioning

confidence: 99%

Simple yet stable bearing‐only navigation

Krajník

Faigl

Vonásek

et al. 2010

Journal of Field Robotics

108

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This article describes a simple monocular navigation system for a mobile robot based on the map-and-replay technique. The presented method is robust, easy to implement, does not require sensor calibration or structured environment and its computational complexity is independent of the environment size. The method can navigate a robot while sensing only one landmark at a time, making it more robust than other monocular approaches. The aforementioned properties of the method allow even low-cost robots to effectively act in large outdoor and indoor environments with natural landmarks only.The basic idea is to utilize a monocular vision to correct only the robot's heading and leaving distance measurements just to the odometry. The heading correction itself can suppress the odometric error and prevent the overall position error from diverging.The influence of a map-based heading estimation and odometric errors on the overall position uncertainty is examined. A claim that for closed polygonal trajectories the position error of this type of navigation does not diverge is stated. The claim is defended mathematically and experimentally. The method has been experimentally tested in a set of indoor and outdoor experiments, during which the average position errors have been lower than 0.3 m for paths over 1 km long.

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Section: Vision-based Navigationmentioning

confidence: 99%

Simple yet stable bearing‐only navigation

Krajník

Faigl

Vonásek

et al. 2010

Journal of Field Robotics

108

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“…The servoing loop used is a simple image based visual servo scheme as described in [3]. In order to perform visual servoing, features need to be defined in the scene that can be tracked through the entire servoing process [3,4,9]. Typically, these features are image locations of markers/fiducials placed in the scene and are well defined and easy to extract from an image of the scene.…”

Section: Image Processing For Visual Servoingmentioning

confidence: 99%

A high throughput robot system for machine vision based plant phenotype studies

Subramanian

Spalding

Ferrier

2012

Machine Vision and Applications

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This work demonstrates how a high throughput robotic machine vision systems can quantify seedling development with high spatial and temporal resolution.The throughput that the system provides is high enough to match the needs of functional genomics research. Analyzing images of plant seedlings growing and responding to stimuli is a proven approach to finding the effects of an affected gene. However, with 10 4 genes in a typical plant genome, comprehensive studies will require high throughput methodologies. To increase throughput without sacrificing spatial or temporal resolution, a 3 axis robotic gantry system utilizing visual servoing was developed. The gantry consists of direct drive linear servo motors that can move the cameras at a speed of 1 m/s with an accuracy of 1 µm, and a repeatability of 0.1 µm. Perpendicular to the optical axis of the cameras was a 1 m 2 sample fixture holds 36 Petri plates in which 144 Arabidopsis thaliana seedlings (4 per Petri plate) grew vertically along the surface of an agar gel. A probabilistic image analysis algorithm was used to locate the root of seedlings and a normalized gray scale variance measure was used to achieve focus by servoing along the optical axis. Rotation of the sample holder induced a gravitropic bending response in the roots, which are approximately 45 µm wide and several millimeter in length. The custom hardware and software described here accurately quantified the gravitropic responses of the seedlings in parallel at approximately 3 min intervals over an 8-h period. Here we present an overview of our system and describe some of the necessary capabilities and challenges to automating plant phenotype studies.

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“…Visual information obtained from camera is utilized for closed-loop robot control, which is referred to as visual servoing system [7], [8]. An overview about the properties and challenges of visual servo systems can be found in [9]- [11]. A position based visual servoing (PBVS) is applied in this paper, where the object information is retrieved from the image and converted to 3D pose (including position and orientation) information for robot control.…”

Section: Introductionmentioning

confidence: 99%

Visual servoing for object manipulation: A case study in slaughterhouse

Andersen

et al. 2016

2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV)

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Abstract-Automation for slaughterhouse challenges the design of the control system due to the variety of the objects. Realtime sensing provides instantaneous information about each piece of work and thus, is useful for robotic system developed for slaughterhouse. In this work, a pick and place task which is a common task among tasks in slaughterhouse is selected as the scenario for the system demonstration. A vision system is utilized to grab the current information of the object, including position and orientation. The information about the object is then transferred to the robot side for path planning. An online and offline combined path planning algorithm is proposed to generate the desired path for the robot control. An industrial robot arm is applied to execute the path. The system is implemented for a lab-scale experiment, and the results show a high success rate of object manipulation in the pick and place task. The approach is implemented in ROS which allows utilization of the developed algorithm on different platforms with little extra effort.

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Visual servo control. II. Advanced approaches [Tutorial]

Cited by 862 publications

References 42 publications

Simple yet stable bearing‐only navigation

Simple yet stable bearing‐only navigation

A high throughput robot system for machine vision based plant phenotype studies

Visual servoing for object manipulation: A case study in slaughterhouse

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