Project-Based Learning Example: Controlling an Educational Robotic Arm With Computer Vision

Cabrè, Tomàs Pallejà; Cairol, Mercè Teixidó; Calafell, Davinia Font; Ribes, Marcel Tresánchez; Roca, J.

doi:10.1109/rita.2013.2273114

Cited by 34 publications

(18 citation statements)

References 12 publications

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“…One of the earliest approaches involved matching the features of the known object model (Grimson, 1990;Ellgammal, 2005;Sibiryakov, 2008) or shape primitives based recognition (Krivic and Solina, 2004;Nieuwenhuisen et al, 2012,). Currently, a very popular trend is to use machine learning algorithms (Cabre et al, 2013). Most of these approaches rely on the feature extrac-tion and matching methods, such as SIFT (Lowe, 2004;Collet, et al 2011) or SURF (Bay et al, 2008).…”

Section: Fig 1 Typical Pick-and-place Taskmentioning

confidence: 99%

Pick-and-Place Task Implementation Using Visual Open-Loop Control

Kolosowski

Wolniakowski

Bogdan

2019

Acta Mechanica Et Automatica

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In the ever increasing number of robotic system applications in the industry, the robust and fast visual recognition and pose estimation of workpieces are of utmost importance. One of the ubiquitous tasks in industrial settings is the pick-and-place task where the object recognition is often important. In this paper, we present a new implementation of a work-piece sorting system using a template matching method for recognizing and estimating the position of planar workpieces with sparse visual features. The proposed framework is able to distinguish between the types of objects presented by the user and control a serial manipulator equipped with parallel finger gripper to grasp and sort them automatically. The system is furthermore enhanced with a feature that optimizes the visual processing time by automatically adjusting the template scales. We test the proposed system in a real-world setup equipped with a UR5 manipulator and provide experimental results documenting the performance of our approach.

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Section: Fig 1 Typical Pick-and-place Taskmentioning

confidence: 99%

Pick-and-Place Task Implementation Using Visual Open-Loop Control

Kolosowski

Wolniakowski

Bogdan

2019

Acta Mechanica Et Automatica

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“…T.P. Cabre et al proposed an automation cell that combines computer vision system and robotic control [7]. In their…”

Section: Introductionmentioning

confidence: 99%

Robot vision to recognize both object and rotation for robot pick-and-place operation

Lin

Chen

2015

2015 International Conference on Advanced Robotics and Intelligent Systems (ARIS)

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This paper presents a robot vision system to recognize both different target objects and their poses that can be incorporated in robot programming by demonstration (robot PbD). In recent years, robot PbD has become a popular topic in the field of robotics. Programming robots is a time-consuming process and requires technical knowledge. However, robot task programming will be simplified by using a vision system to analyze human demonstration. Usually, design of a user-friendly human-robot interaction interface is crucial to implement the robot PbD process. We take pick-and-place operation as an example of robot PbD. Several objects are placed on a table without overlapping, and people pick one of them to other place, one after another. Before learning from human demonstration, if the robot can obtain initial information, e.g., to recognize the target object and to estimate its pose, it helps robot to refine its following decision, such as selecting the gripper type or the grip angle. Thus the authors develop the robot vision ads the first step to realize such working robot. The developed vision system has advantage of interactive trainability with the help of the proposed graphical user interface (GUI), in which the interpretable features can be easily selected by ordinary users. We also propose a simple scale-invariant pose estimation method. Experimental results support feasibility and effectiveness of the system.

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“…Centre-of-Mass based computation, filtering and colour segmentation algorithm are used in order to locate the target and the position of the robotic arm. In another work, an educational robotic arm performs the task of detecting a randomly placed object, picking it and moving it to a predefined container using a vision system [13]. A light blue foam-rubber cube is randomly placed on a target area which is surrounded by black lines.…”

Section: Introductionmentioning

confidence: 99%

Smart Robot Arm Motion Using Computer Vision

Iscimen

Atasoy²,

Kutlu³

et al. 2015

ElAEE

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In this study computer vision and robot arm are used together to design a smart robot arm system which can identify objects from images automatically and perform given tasks. A serving robot application, in which specific tableware can be identified and lifted from a table, is presented in this work. A new database was created by using images of objects used in serving a meal. This study consists of two phases: First phase includes recognition of the objects through computer vision algorithms and determining the specified objects' coordinates. Second phase is the realization of the robot arm's movement to the given coordinates. Artificial neural network is used for object recognition in this system. 98.30 % overall accuracy of recognition is achieved. Robot arm's joint angles were calculated by using coordinate dictionary for moving the arm to desired coordinates and the robot arm's movement was performed.

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Project-Based Learning Example: Controlling an Educational Robotic Arm With Computer Vision

Cited by 34 publications

References 12 publications

Pick-and-Place Task Implementation Using Visual Open-Loop Control

Pick-and-Place Task Implementation Using Visual Open-Loop Control

Robot vision to recognize both object and rotation for robot pick-and-place operation

Smart Robot Arm Motion Using Computer Vision

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