Visual servoing task is to control the pose of a robotic system, relative to a target, using visual features extracted from an image. Advanced robot systems often integrate up-to-date sensors, vision systems, and visual servoing techniques to deal with non-static target objects of various shapes and colors. The environment a robot is immersed in its orientation, and its motion can be described through visual information. The camera may be carried by the robot or fixed in the surroundings, known as endpoint closed-loop (eye-in-hand) and endpoint open-loop, respectively.
Visual servoing has proven to be useful in a wide range of real-world applications, such as military, medical devices, trade, search and rescue, security, among many others. For instance, visual servoing techniques can be applied on unmanned vehicles, which can be used for surveillance, road-traffic control, border inspection, and reserved areas supervision, to provide visual information from the surroundings. Nowadays, the study on enhancing the autonomy of these vehicles has focused on navigation and formation control, target recognition, and tracking, among many others, by improving their visual capabilities. For example, the formation control in unmanned aerial vehicle (UAV) swarms is used in applications like searching and mapping to fly in groups above vast areas for goods delivery, tracking and even locating and following military targets. Visual servoing must produces the necessary commands to maintain the vehicle attitude and define the flight path based on the provided information from onboard sensors.
Improving the autonomy of robots is still one of the challenges facing visual servoing and there are various ongoing studies in this field, where each system may differ from others in size, appearance, type of power plant, and more; due to these differences, they may also show distinct characteristics, but the equipment employed for evaluating their position and orientation is usually the same, which consists of inertial measurement units (IMU) and vison sensors. One of the flaws of using inertial information for motion is the accumulation of small errors on estimating the robot position, which consequently results in drift and deviation in determining its location over time. In modern motion equipment, the global positioning system (GPS) information is used for continuously correcting the IMU estimations and solving this problem, but in the event of interference or disconnection of the GPS signal, the position and motion error should remain and therefore visual servoing can be used for guidance and navigation in different kind of applications to improve the robot autonomy by feedback control of pose and motion through information acquired by onboard vision sensors.
The topics of interest, listed below, are aimed to show continuing efforts provided in the domain of Visual Serevoing applications.
Control system design for camera-carrying moving platforms.
Image processing algorithms for feature extraction.
Target location and recognition.
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Submission of manuscript: October 15, 2020
First notification: January 15, 2021
Submission of revised manuscript: February 15, 2021
Notification of the re-review: March 15, 2021
Final notification: April 1, 2021
Final paper due: May 1, 2021
E. Cabal-Yepez, PhD (Managing Guest Editor)
Dean of the Department of Multidisciplinary Studies
Engineering Division, Campus Irapuato-Salamanca, University of Guanajuato.
Yuriria, Guanajuato, Mexico
+52 4454589040 Ext. 1700.
He received his M.Eng. degree from Facultad de Ingenieria Mecanica Electrica y Electronica (FIMEE), Universidad de Guanajuato, Mexico, in 2001, and his Ph.D. degree from University of Sussex, United Kingdom, in 2007. In April 2008, he joined the Division de Ingenierias del Campus Irapuato-Salamanca de la Universidad de Guanajuato, where he is a Titular Professor and serves as the Dean of the Departamento de Estudios Multidisciplinarios. His current research interests are Digital Image and Signal Processing, Artificial Intelligence, Robotics, Smart Sensors, Real-Time Processing, Mechatronics, FPGAs, and Embedded Systems. He is a National Researcher with the Consejo Nacional de Ciencia y Tecnologia, Mexico.
A. H. Mazinan, PhD
Control Engineering Department,
South Tehran Branch, Islamic Azad University (IAU),
Email: email@example.com or firstname.lastname@example.org
He received the Ph.D. degree in 2009 in Control Engineering. He has been an Associate Professor and also the Director of Control Engineering Department at the Islamic Azad University, South Tehran Branch, Iran, since 2009. He is now an Associate Editor of Transactions of the Institute of Measurement and Control (Sage publisher), and an Associate Editor of Computers and Electrical Engineering (Elsevier Publisher). He is also a member of the Editorial Board in three international journals and also a member of programming committee in four international conferences. He has published more than 150 journal and conference papers. His current research interests include intelligent systems, model-based predictive control, over-actuated space systems modeling and control, time-frequency representation, filter banks, wavelet theory and image-video processing.